TY - JOUR AB - Background: Degradation rate is a fundamental aspect of mRNA metabolism, and the factors governing it remain poorly characterized. Understanding the genetic and biochemical determinants of mRNA half-life would enable more precise identification of variants that perturb gene expression through post-transcriptional gene regulatory mechanisms. Results: We establish a compendium of 39 human and 27 mouse transcriptome-wide mRNA decay rate datasets. A meta-analysis of these data identified a prevalence of technical noise and measurement bias, induced partially by the underlying experimental strategy. Correcting for these biases allowed us to derive more precise, consensus measurements of half-life which exhibit enhanced consistency between species. We trained substantially improved statistical models based upon genetic and biochemical features to better predict half-life and characterize the factors molding it. Our state-of-the-art model, Saluki, is a hybrid convolutional and recurrent deep neural network which relies only upon an mRNA sequence annotated with coding frame and splice sites to predict half-life (r=0.77). The key novel principle learned by Saluki is that the spatial positioning of splice sites, codons, and RNA-binding motifs within an mRNA is strongly associated with mRNA half-life. Saluki predicts the impact of RNA sequences and genetic mutations therein on mRNA stability, in agreement with functional measurements derived from massively parallel reporter assays. Conclusions: Our work produces a more robust ground truth for transcriptome-wide mRNA half-lives in mammalian cells. Using these revised measurements, we trained Saluki, a model that is over 50% more accurate in predicting half-life from sequence than existing models. Saluki succinctly captures many of the known determinants of mRNA half-life and can be rapidly deployed to predict the functional consequences of arbitrary mutations in the transcriptome. © 2022, The Author(s). AU - Agarwal, V. AU - Kelley, D. R. C2 - 36419176 C7 - 245 DB - Scopus DO - 10.1186/s13059-022-02811-x IS - 1 KW - Deep neural networks mRNA half-life mRNA stability Post-transcriptional gene regulation Animals Biological Assay Humans Mammals Mice RNA Stability RNA, Messenger Transcriptome messenger RNA 3' untranslated region animal cell Article biochemical analysis cell specificity codon cohort analysis consensus controlled study convolutional neural network deep learning gene control gene regulatory network genetic model genetic variability half life time human human cell luciferase assay mammal cell mouse nonhuman prediction prevalence RNA degradation RNA sequence RNA splice site RNA-binding domain statistical model animal bioassay genetics mammal meta analysis M3 - Article N1 - Export Date: 28 December 2023; Cited By: 11; CODEN: GNBLF PY - 2022 SN - 14747596 (ISSN) ST - The genetic and biochemical determinants of mRNA degradation rates in mammals T2 - Genome Biology TI - The genetic and biochemical determinants of mRNA degradation rates in mammals UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142430003&doi=10.1186%2fs13059-022-02811-x&partnerID=40&md5=d0ec0379035168d0b4a4cc4882b5df06 VL - 23 ID - 21 ER - TY - JOUR AB - Early detection and accurate prediction of the risk of early childhood caries (ECC) are essential for effective prevention and management. This systematic review aims to assess the performance and applicability of machine learning algorithms in ECC prediction and detection. A comprehensive search was conducted to identify studies utilizing machine learning algorithms to predict or detect ECC. The included (n = 6) studies demonstrated high accuracy, sensitivity, specificity, and area under the receiver operating characteristic (AUC) values related to predicting and detecting ECC. The application of machine learning algorithms contributed to enhanced clinical decision-making, targeted preventive measures, and improved ECC management. The studies also highlighted the importance of considering multiple factors, including demographic, environmental, and genetic factors, when developing dental caries prediction models. Machine learning algorithms hold significant potential for ECC prediction and detection, having promising performance outcomes. Due to the heterogeneity of the studies, no meta-analysis could be performed. Moreover, further research is needed to explore the feasibility, acceptability, and effectiveness of integrating these algorithms into dental practice. This approach would ultimately contribute to enabling more effective and personalized dental caries management and improved oral health outcomes for diverse populations. © 2023 by the author. AU - Al-Namankany, A. C7 - 214 DB - Scopus DO - 10.3390/dj11090214 IS - 9 KW - dental caries detection machine learning oral health prediction systematic review M3 - Review N1 - Export Date: 28 December 2023; Cited By: 0 PY - 2023 SN - 23046767 (ISSN) ST - Influence of Artificial Intelligence-Driven Diagnostic Tools on Treatment Decision-Making in Early Childhood Caries: A Systematic Review of Accuracy and Clinical Outcomes T2 - Dentistry Journal TI - Influence of Artificial Intelligence-Driven Diagnostic Tools on Treatment Decision-Making in Early Childhood Caries: A Systematic Review of Accuracy and Clinical Outcomes UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172912384&doi=10.3390%2fdj11090214&partnerID=40&md5=d36dc0e04026dd0fde37cacb07b9b4ae VL - 11 ID - 10 ER - TY - JOUR AB - Article Title and Bibliographic Information: Artificial intelligence applications in restorative dentistry: A systematic review. Revilla-León, M., Gómez-Polo, M., Vyas, S., Barmak, A. B., Özcan, M., Att, W., & Krishnamurthy, V. R. J Prosthet Dent 2021 Source of Funding: Not reported. Type of Study/Design: Systematic review. © 2023 Elsevier Inc. AU - Alqutaibi, A. Y. AU - Aboalrejal, A. N. C2 - 36914305 C7 - 101837 DB - Scopus DO - 10.1016/j.jebdp.2023.101837 IS - 1 KW - AI Artificial intelligence Dental caries Machine learning Restorative dentistry Root fracture Dentistry Humans human M3 - Note N1 - Export Date: 28 December 2023; Cited By: 2 PY - 2023 SN - 15323382 (ISSN) ST - ARTIFICIAL INTELLIGENCE (AI) AS AN AID IN RESTORATIVE DENTISTRY IS PROMISING, BUT STILL A WORK IN PROGRESS T2 - Journal of Evidence-Based Dental Practice TI - ARTIFICIAL INTELLIGENCE (AI) AS AN AID IN RESTORATIVE DENTISTRY IS PROMISING, BUT STILL A WORK IN PROGRESS UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148729645&doi=10.1016%2fj.jebdp.2023.101837&partnerID=40&md5=6538520344f393a9c3a7349170a50769 VL - 23 ID - 20 ER - TY - JOUR AB - Introduction: The role of artificial intelligence (AI) is currently increasing in terms of diagnosing diseases and planning treatment in endodontics. However, findings from individual research studies are not systematically reviewed and compiled together. Hence, this study aimed to systematically review, appraise, and evaluate neural AI algorithms employed and their comparative efficacy to conventional methods in endodontic diagnosis and treatment planning. Methods: The present research question focused on the literature search about different AI algorithms and models of AI assisted endodontic diagnosis and treatment planning. The search engine included databases such as Google Scholar, PubMed, and Science Direct with search criteria of primary research paper, published in English, and analyzed data on AI and its role in the field of endodontics. Results: The initial search resulted in 785 articles, exclusion based on abstract relevance, animal studies, grey literature and letter to editors narrowed down the scope of selected articles to 11 accepted for review. The review data supported the findings that AI can play a crucial role in the area of endodontics, such as identification of apical lesions, classifying and numbering teeth, detecting dental caries, periodontitis and periapical disease, diagnosing different dental problems, helping dentists make referrals, and also helping them make plans for treatment of dental disorders in a timely and effective manner with greater accuracy. Conclusion: AI with different models or frameworks and algorithms can help dentists to diagnose and manage endodontic problems with greater accuracy. However, endodontic fraternity needs to provide more emphasis on the utilization of AI, provision of evidence based guidelines and implementation of the AI models. © 2022 The Authors AU - Asiri, A. F. AU - Altuwalah, A. S. DB - Scopus DO - 10.1016/j.sdentj.2022.04.004 IS - 4 KW - Artificial intelligence Artificial neural networks Endodontics Neural artificial intelligence Treatment planning artificial neural network data base dental caries diagnostic accuracy human learning algorithm panoramic radiography periodontitis reliability Review search engine sensitivity and specificity systematic review tooth disease tooth fracture tooth periapical disease validity M3 - Review N1 - Export Date: 28 December 2023; Cited By: 6 PY - 2022 SN - 10139052 (ISSN) SP - 270-281 ST - The role of neural artificial intelligence for diagnosis and treatment planning in endodontics: A qualitative review T2 - Saudi Dental Journal TI - The role of neural artificial intelligence for diagnosis and treatment planning in endodontics: A qualitative review UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129951062&doi=10.1016%2fj.sdentj.2022.04.004&partnerID=40&md5=ba4bb9e691c26190bce623f337db1c05 VL - 34 ID - 13 ER - TY - JOUR AB - For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease-specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders EXpanding (T-REX) was created to identify changes in both rare and common cells across human immune monitoring settings. T-REX identified cells with highly similar phenotypes that localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infec-tions. Specialized MHCII tetramer reagents that mark rhinovirus-specific CD4+ cells were left out during analysis and then used to test whether T-REX identified biologically significant cells. T-REX identified rhinovirus-specific CD4+ T cells based on phenotypically homogeneous cells expanding by ≥95% following infection. T-REX successfully identified hotspots of virus-specific T cells by comparing infection (day 7) to either pre-infection (day 0) or post-infection (day 28) samples. Plotting the direction and degree of change for each individual donor provided a useful summary view and revealed patterns of immune system behavior across immune monitoring settings. For example, the magnitude and direction of change in some COVID-19 patients was comparable to blast crisis acute myeloid leukemia patients undergoing a complete response to chemotherapy. Other COVID-19 patients instead displayed an immune trajectory like that seen in rhinovirus infection or checkpoint inhibitor therapy for melanoma. The T-REX algorithm thus rapidly identifies and characterizes mech-anistically significant cells and places emerging diseases into a systems immunology context for comparison to well-studied immune changes. ‍© Barone et al. AU - Barone, S. M. AU - Paul, A. G. A. AU - Muehling, L. M. AU - Lannigan, J. A. AU - Kwok, W. W. AU - Turner, R. B. AU - Woodfolk, J. A. AU - Irish, J. M. C2 - 34350827 C7 - e64653 DB - Scopus DO - 10.7554/ELIFE.64653 KW - Adolescent Adult Algorithms CD4-Positive T-Lymphocytes COVID-19 Humans Leukemia, Myeloid, Acute Melanoma Neoplasms Picornaviridae Infections Rhinovirus SARS-CoV-2 Unsupervised Machine Learning Young Adult ADP ribosyl cyclase/cyclic ADP ribose hydrolase 1 beta1 integrin CD14 antigen CD147 antigen CD27 antigen CD64 antigen chemokine receptor CCR5 chemokine receptor CCR6 chemokine receptor CCR7 chemokine receptor CXCR3 chemokine receptor CXCR5 decay accelerating factor HLA antigen inducible T cell costimulator magnetic bead major histocompatibility antigen class 1 major histocompatibility antigen class 2 protein t box transcription factor 21 transcription factor t cell factor 1 tumor necrosis factor receptor superfamily member 6 unclassified drug acute myeloid leukemia algorithm Article autofluorescence imaging cancer chemotherapy cancer therapy CD4+ T lymphocyte cell count cell proliferation computer analysis controlled study convalescence coronavirus disease 2019 density gradient centrifugation flow cytometry human immune response immunocompetent cell induced pluripotent stem cell induction chemotherapy influenza machine learning meta analysis (topic) middle aged myelodysplastic syndrome natural killer cell nonhuman phenotype respiratory tract disease Rhinovirus infection T lymphocyte Th17 cell tracking responder expanding immunology isolation and purification neoplasm picornavirus infection M3 - Article N1 - Export Date: 28 December 2023; Cited By: 9 PY - 2021 SN - 2050084X (ISSN) ST - Unsupervised machine learning reveals key immune cell subsets in covid-19, rhinovirus infection, and cancer therapy T2 - eLife TI - Unsupervised machine learning reveals key immune cell subsets in covid-19, rhinovirus infection, and cancer therapy UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112769970&doi=10.7554%2fELIFE.64653&partnerID=40&md5=0818a86d2cac27c859fb308cd4f523ed VL - 10 ID - 24 ER - TY - JOUR AB - In later years the potential contribution of forest bioenergy to mitigate climate change has been increasingly questioned due to temporal displacement between CO2emissions when forest biomass is used for energy and subsequent sequestration of carbon in new biomass. Also disturbance of natural decay of dead biomass when used for energy affect the carbon dynamics of forest ecosystems. These perturbations of forest ecosystems are summarized under the concept of carbon debt and its payback time. Narrative reviews demonstrate that the payback time of apparently comparable forest bioenergy supply scenarios vary by up to 200 years allowing amble room for confusion and dispute about the climate benefits of forest bioenergy. This meta-analysis confirm that the outcome of carbon debt studies lie in the assumptions and find that methodological rather than ecosystem and management related assumptions determine the findings. The study implies that at the current development of carbon debt methodologies and their lack of consensus the concept in it-self is inadequate for informing and guiding policy development. At the management level the carbon debt concept may provide valuable information directing management principles in a more climate benign directions. © 2017 Elsevier Ltd AU - Bentsen, N. S. DB - Scopus DO - 10.1016/j.rser.2017.02.004 KW - Carbon debt Forest bioenergy Machine learning Meta-analysis Payback time Biomass Climate change Ecosystems Forestry Learning systems Bio-energy CO 2 emission Energy Forest ecosystem Machine-learning Temporal displacement Carbon M3 - Review N1 - Export Date: 28 December 2023; Cited By: 47; CODEN: RSERF PY - 2017 SN - 13640321 (ISSN) SP - 1211-1217 ST - Carbon debt and payback time – Lost in the forest? T2 - Renewable and Sustainable Energy Reviews TI - Carbon debt and payback time – Lost in the forest? UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85012927447&doi=10.1016%2fj.rser.2017.02.004&partnerID=40&md5=c248a1f1114e838d5d568bd9f0d4c46c VL - 73 ID - 16 ER - TY - JOUR AB - The Integration of machine learning and traditional image processing in dentistry has resulted in many applications like automatic teeth identification and numbering, caries, anomaly, disease detection, and dental treatment prediction. They have a broad scope in different applications observed in the dentistry literature review. This study reviews the literature on deep learning and dental radiograph analysis. We present an overview of machine learning algorithms in different areas of dentistry: tooth identification and numbering, Dental disease detection, and dental predictive treatment models. The methods under each area are briefly discussed. The dental radiograph data set required for performing experiments is summarized from the available literature. The study concludes by discussing new research opportunities and initiatives in this field. This paper offers a comprehensive overview of this innovative, challenging, and growing area in dentistry. © 2023 The Authors AU - Bhat, S. AU - Birajdar, G. K. AU - Patil, M. D. C7 - 100282 DB - Scopus DO - 10.1016/j.health.2023.100282 KW - Artificial intelligence Deep learning Dentistry Diagnostics analytics Machine learning Medical image processing automation clinical decision support system computer assisted diagnosis convolutional neural network dental caries dental examination dental procedure dentist human image processing image segmentation Review systematic review tooth disease tooth radiography X ray analysis M3 - Review N1 - Export Date: 28 December 2023; Cited By: 0 PY - 2023 SN - 27724425 (ISSN) ST - A comprehensive survey of deep learning algorithms and applications in dental radiograph analysis T2 - Healthcare Analytics TI - A comprehensive survey of deep learning algorithms and applications in dental radiograph analysis UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179051305&doi=10.1016%2fj.health.2023.100282&partnerID=40&md5=403fc01e70ae0ef2242781717895c466 VL - 4 ID - 32 ER - TY - JOUR AB - As one of the most prevalent infective diseases worldwide, it is crucial that we not only know the constituents of the oral microbiome in dental caries but also understand its functionality. Herein, we present a reproducible meta-analysis to effectively report the key components and the associated functional signature of the oral microbiome in dental caries. Publicly available sequencing data were downloaded from online repositories and subjected to a standardized analysis pipeline before analysis. Meta-analyses identified significant differences in alpha and beta diversities of carious microbiomes when compared to healthy ones. Additionally, machine learning and receiver operator characteristic analysis showed an ability to discriminate between healthy and disease microbiomes. We identified from importance values, as derived from random forest analyses, a group of genera, notably containing Selenomonas, Aggregatibacter, Actinomyces and Treponema, which can be predictive of dental caries. Finally, we propose the most appropriate study design for investigating the microbiome of dental caries by synthesizing the studies, which had the most accurate differentiation based on random forest modelling. In conclusion, we have developed a non-biased, reproducible pipeline, which can be applied to microbiome meta-analyses of multiple diseases, but importantly we have derived from our meta-analysis a key group of organisms that can be used to identify individuals at risk of developing dental caries based on oral microbiome inhabitants. © 2022 The Authors. APMIS published by John Wiley & Sons Ltd on behalf of Scandinavian Societies for Pathology, Medical Microbiology and Immunology. AU - Butcher, M. C. AU - Short, B. AU - Veena, C. L. R. AU - Bradshaw, D. AU - Pratten, J. R. AU - McLean, W. AU - Shaban, S. M. A. AU - Ramage, G. AU - Delaney, C. C2 - 36050830 DB - Scopus DO - 10.1111/apm.13272 IS - 12 KW - 16S bioinformatics dental caries Microbiome sequencing tooth decay Actinomyces Dental Caries Susceptibility Humans Microbiota Aggregatibacter Article carbohydrate metabolism carbon metabolism classifier diagnostic accuracy diagnostic value disease simulation DNA extraction human KEGG machine learning meta analysis mouth flora prediction predictive value random forest receiver operating characteristic Selenomonas Shannon index Simpson index Treponema upregulation genetics microflora M3 - Article N1 - Export Date: 28 December 2023; Cited By: 4; CODEN: APMSE PY - 2022 SN - 09034641 (ISSN) SP - 763-777 ST - Meta-analysis of caries microbiome studies can improve upon disease prediction outcomes T2 - APMIS TI - Meta-analysis of caries microbiome studies can improve upon disease prediction outcomes UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138422836&doi=10.1111%2fapm.13272&partnerID=40&md5=cd523f52430e682c980cb17bfcbed076 VL - 130 ID - 26 ER - TY - JOUR AB - Background. Early diagnosis and monitoring the evolution of the patients is required to be able to have effective preventive attitudes. An easy and cost-effective way of diagnosis is needed for this purpose. The aim of the study was to evaluate the AI level of use in dentistry diagnosis and the fields of its applicability especially for early diagnosis purposes. A secondary objective was to point out the measured performances for automated AI diagnosis by comparison with standard diagnosis procedures. Material and methods. A comprehensive electronic search was performed in November 2022 through PubMed, Scopus, and Web of Science databases. The following keywords were used to search the databases: (”Artificial Intelligence” OR ”neural network” OR ”Deep learning” OR “Machine learning”) AND (”Dentistry” OR “Dental medicine”) AND (” periodontal disease” OR ”periodontics” OR ”Carious lesions” OR ”oral cancer” OR ”restorative” or “early diagnosis”). The risk of bias (RoB) of the included studies was assessed using PROBAST tool. Results. A total of 334 publications were collected after searching the databases. For 218 remaining publications the title and the abstract were assessed. The reviewers agreed to continue with 69 studies for full text assessment. Because 49 studies had not completely fulfilled the inclusion criteria only 20 publications were included in the final analysis. AI automatic data processing for diagnostic purposes was implemented in the field of dental radiology, oral pathology, restorative dentistry, pedodontics, oncology, endodontics, and periodontics. Conclusion. AI based automatic diagnostic is a powerful and reliable tool that has a great future potential for different fields of dental medicine like periodontal disease, oral cancer, and carious lesions. © 2022, Amaltea Medical Publishing House. All rights reserved. AU - Chifor, R. AU - Arsenescu, T. AU - Dascalu, L. M. AU - Badea, A. F. DB - Scopus DO - 10.37897/RJS.2022.3.7 IS - 3 KW - artificial intelligence automatic diagnosis early diagnosis machine learning preventive dentistry M3 - Review N1 - Export Date: 28 December 2023; Cited By: 0 PY - 2022 SN - 18430805 (ISSN) SP - 106-115 ST - Automated diagnosis using artificial intelligence a step forward for preventive dentistry: A systematic review T2 - Romanian Journal of Stomatology TI - Automated diagnosis using artificial intelligence a step forward for preventive dentistry: A systematic review UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146769354&doi=10.37897%2fRJS.2022.3.7&partnerID=40&md5=b362bd672d3ffe55bd45410f47fd6c99 VL - 68 ID - 8 ER - TY - JOUR AB - Systematic reviews are difficult to keep up to date, but failure to do so leads to a decay in review currency, accuracy, and utility. We are developing a novel approach to systematic review updating termed “Living systematic review” (LSR): systematic reviews that are continually updated, incorporating relevant new evidence as it becomes available. LSRs may be particularly important in fields where research evidence is emerging rapidly, current evidence is uncertain, and new research may change policy or practice decisions. We hypothesize that a continual approach to updating will achieve greater currency and validity, and increase the benefits to end users, with feasible resource requirements over time. © 2017 Elsevier Inc. AU - Elliott, J. H. AU - Synnot, A. AU - Turner, T. AU - Simmonds, M. AU - Akl, E. A. AU - McDonald, S. AU - Salanti, G. AU - Meerpohl, J. AU - MacLehose, H. AU - Hilton, J. AU - Tovey, D. AU - Shemilt, I. AU - Thomas, J. AU - Agoritsas, T. AU - Perron, C. AU - Hodder, R. AU - Pestridge, C. AU - Albrecht, L. AU - Horsley, T. AU - Platt, J. AU - Armstrong, R. AU - Nguyen, P. H. AU - Plovnick, R. AU - Arno, A. AU - Ivers, N. AU - Quinn, G. AU - Au, A. AU - Johnston, R. AU - Rada, G. AU - Bagg, M. AU - Jones, A. AU - Ravaud, P. AU - Boden, C. AU - Kahale, L. AU - Richter, B. AU - Boisvert, I. AU - Keshavarz, H. AU - Ryan, R. AU - Brandt, L. AU - Kolakowsky-Hayner, S. A. AU - Salama, D. AU - Brazinova, A. AU - Nagraj, S. K. AU - Buchbinder, R. AU - Lasserson, T. AU - Santaguida, L. AU - Champion, C. AU - Lawrence, R. AU - Santesso, N. AU - Chandler, J. AU - Les, Z. AU - Schünemann, H. J. AU - Charidimou, A. AU - Leucht, S. AU - Chou, R. AU - Low, N. AU - Sherifali, D. AU - Churchill, R. AU - Maas, A. AU - Siemieniuk, R. AU - Cnossen, M. C. AU - Cossi, M. J. AU - Macleod, M. AU - Skoetz, N. AU - Counotte, M. AU - Marshall, I. AU - Soares-Weiser, K. AU - Craigie, S. AU - Marshall, R. AU - Srikanth, V. AU - Dahm, P. AU - Martin, N. AU - Sullivan, K. AU - Danilkewich, A. AU - García, L. M. AU - Danko, K. AU - Mavergames, C. AU - Taylor, M. AU - Donoghue, E. AU - Maxwell, L. J. AU - Thayer, K. AU - Dressler, C. AU - McAuley, J. AU - Egan, C. AU - Tritton, R. AU - McKenzie, J. AU - Tsafnat, G. AU - Elliott, S. A. AU - Tugwell, P. AU - Etxeandia, I. AU - Merner, B. AU - Turgeon, A. AU - Featherstone, R. AU - Mondello, S. AU - Foxlee, R. AU - Morley, R. AU - van Valkenhoef, G. AU - Garner, P. AU - Munafo, M. AU - Vandvik, P. AU - Gerrity, M. AU - Munn, Z. AU - Wallace, B. AU - Glasziou, P. AU - Murano, M. AU - Wallace, S. A. AU - Green, S. AU - Newman, K. AU - Watts, C. AU - Grimshaw, J. AU - Nieuwlaat, R. AU - Weeks, L. AU - Gurusamy, K. AU - Nikolakopoulou, A. AU - Weigl, A. AU - Haddaway, N. AU - Noel-Storr, A. AU - Wells, G. AU - Hartling, L. AU - O'Connor, A. AU - Wiercioch, W. AU - Hayden, J. AU - Page, M. AU - Wolfenden, L. AU - Helfand, M. AU - Pahwa, M. AU - Yepes Nuñez, J. J. AU - Higgins, J. AU - Pardo, J. P. AU - Yost, J. AU - Hill, S. AU - Pearson, L. C2 - 28912002 DB - Scopus DO - 10.1016/j.jclinepi.2017.08.010 KW - Evidence synthesis Guidelines Living guidelines Living systematic review Systematic review Access to Information Biomedical Research Guidelines as Topic Humans Information Dissemination Review Literature as Topic Time Factors decision making human machine learning medical research peer review policy priority journal protocol compliance publication publishing Review statistical analysis systematic review (topic) validity literature practice guideline time factor M3 - Review N1 - Export Date: 28 December 2023; Cited By: 337; CODEN: JCEPE PY - 2017 SN - 08954356 (ISSN) SP - 23-30 ST - Living systematic review: 1. Introduction—the why, what, when, and how T2 - Journal of Clinical Epidemiology TI - Living systematic review: 1. Introduction—the why, what, when, and how UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028993474&doi=10.1016%2fj.jclinepi.2017.08.010&partnerID=40&md5=8daf7a2bea2c4909c2f11d91fee24b18 VL - 91 ID - 9 ER - TY - JOUR AB - Dental caries is a very common condition, which can lead to serious complications, including tooth loss and infection of the whole human body. Dentists in their daily practice, apart from visual-tactile examination, use radiological methods, such as periapical radiographs and bitewings. Artificial intelligence (AI) is a tool that can be used in diagnosing and detecting cavities. It can help to avoid more invasive treatment and further consequences. The goal of this systematic review was to present the use of artificial intelligence in radiological dental caries diagnostics. In total, twelve studies meeting inclusion criteria were analyzed, and image databases varied from 93 to 3,868 radiographs, with average value of 1,091.17 radiographs. Most of the included studies employed bitewings and periapical images, and authors used different methods and AI algorithms. Accuracy was performed in nine researches. The highest accuracy was 99%, the lowest 73.3%. Also, nine researches provided information on number of observers, which varied from 1 to 25. Comparing all the studies, it was difficult to draw out a conclusion. Artificial intelligence in radiological images may assist dentists and radiologist to perform better and faster examination, and it may be a used in routine dental care. However, more researches are needed in the field of dentistry and radiology. © 2021 Termedia Publishing House Ltd.. All rights reserved. AU - Futyma-Gabka, K. AU - Rózylo-Kalinowska, I. DB - Scopus DO - 10.5114/jos.2021.111664 IS - 4 KW - Artificial intelligence Dental caries Detecting caries Radiology M3 - Review N1 - Export Date: 28 December 2023; Cited By: 1 PY - 2021 SN - 00114553 (ISSN) ST - The use of artificial intelligence in radiological diagnosis and detection of dental caries: A systematic review T2 - Journal of Stomatology TI - The use of artificial intelligence in radiological diagnosis and detection of dental caries: A systematic review UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123555312&doi=10.5114%2fjos.2021.111664&partnerID=40&md5=027009e4e5aca1f5c3d9bbe9a9e2243f VL - 74 ID - 2 ER - TY - JOUR AB - Plant disease management in agriculture science is the primary concern for every country, as the food demand is increasing fast due to an increase in population. Furthermore, modern technology has improved the efficacy and precision of disease detection in plants and animals. Plant disease identification using various image processing approaches has recently been employed on a big scale to help farmers monitor their plantation areas. Based on the perpetuation and spread, the diseases can be floral, foliar, and soilborne. Grain production is typically affected by foliar diseases, which reduce photosynthetic area, duration, and function. Soil-borne conditions include vascular wilt, root rot, and damping-off; and can exhibit symptoms such as wilting of foliage, root decay, and sudden death. This paper highlights the significant issues and challenges for leaf disease classification. A comparative study of different methods based on the agricultural product, methodology, efficiency, advantages, and disadvantages is also included. The review study analyzes the most frequently used machine learning algorithms in the last five to seven years, revealing that Support Vector Machine (SVM) has been extensively used for disease classification. An analysis of specific Techniques (Feature Extraction plus machine learning-based Classification algorithm) and their associated accuracy is also performed, demonstrating that (ORB) features combined with Linear SVM provide the highest accuracy of 99.98%. © 2023 IETE. AU - Goel, L. AU - Nagpal, J. DB - Scopus DO - 10.1080/02564602.2022.2121772 IS - 3 KW - Artificial neural network Convolutional neural network K-Nearest neighbor Machine learning Plant disease diagnosis Support vector machine Agricultural products Computer aided diagnosis Convolution Convolutional neural networks Image processing Learning algorithms Learning systems Nearest neighbor search Plants (botany) Disease classification K-near neighbor Machine learning techniques Machine-learning Nearest-neighbour Plant disease Support vectors machine Systematic Review Support vector machines M3 - Review N1 - Export Date: 28 December 2023; Cited By: 2; CODEN: ITREE PY - 2023 SN - 02564602 (ISSN) SP - 423-439 ST - A Systematic Review of Recent Machine Learning Techniques for Plant Disease Identification and Classification T2 - IETE Technical Review (Institution of Electronics and Telecommunication Engineers, India) TI - A Systematic Review of Recent Machine Learning Techniques for Plant Disease Identification and Classification UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138673293&doi=10.1080%2f02564602.2022.2121772&partnerID=40&md5=07bbe6ae5df140e6739438bf0dbc41f9 VL - 40 ID - 30 ER - TY - JOUR AB - Cancer is one of the top causes of death globally. Recently, microarray gene expression data has been used to aid in cancer’s effective and early detection. The use of DNA microarray technology to uncover information from the expression levels of thousands of genes has enormous promise. The DNA microarray technique can determine the levels of thousands of genes simultaneously in a single experiment. The analysis of gene expression is critical in many disciplines of biological study to obtain the necessary information. This study analyses all the research studies focused on optimizing gene selection for cancer detection using artificial intelligence. One of the most challenging issues is figuring out how to extract meaningful information from massive databases. Deep Learning architectures have performed efficiently in numerous sectors and are used to diagnose many other chronic diseases and to assist physicians in making medical decisions. In this study, we have evaluated the results of different optimizers on a RNA sequence dataset. The Deep learning algorithm proposed in the study classifies five different forms of cancer, including kidney renal clear cell carcinoma (KIRC), Breast Invasive Carcinoma (BRCA), lung adenocarcinoma (LUAD), Prostate Adenocarcinoma (PRAD) and Colon Adenocarcinoma (COAD). The performance of different optimizers like Stochastic gradient descent (SGD), Root Mean Squared Propagation (RMSProp), Adaptive Gradient Optimizer (AdaGrad), and Adaptive Momentum (AdaM). The experimental results gathered on the dataset affirm that AdaGrad and Adam. Also, the performance analysis has been done using different learning rates and decay rates. This study discusses current advancements in deep learning-based gene expression data analysis using optimized feature selection methods. Copyright © 2022 Gupta, Gupta, Shabaz and Sharma. AU - Gupta, S. AU - Gupta, M. K. AU - Shabaz, M. AU - Sharma, A. C7 - 952709 DB - Scopus DO - 10.3389/fphys.2022.952709 KW - artificial intelligence cancer deep learning gene expression Rna-sequences acute lymphoblastic leukemia Adaptive Gradient Optimizer Adaptive Momentum algorithm artificial neural network AutoEncoder with Cox regression network backpropagation through time bioinformatics biological study breast invasive carcinoma cancer classification cancer diagnosis clear cell renal cell carcinoma colon adenocarcinoma comprehensibility convolutional neural network cross entropy cytogenetics data base Deep Belief Neural Network DNA microarray technology DNAJC2 gene EBSCO database Embase gene gene mutation gene selection GMPPA gene high throughput sequencing human Kaplan Meier method long short term memory network lung adenocarcinoma mathematical computing meta analysis microarray analysis MMRN2 gene multi layer perceptron Neuro Fuzzy method oncology performance Preferred Reporting Items for Systematic Reviews and Meta-Analyses process optimization prostate adenocarcinoma Review RNA sequence Root Mean Squared Propagation stochastic gradient descent systematic review training Web of Science ZNF560 gene M3 - Review N1 - Export Date: 28 December 2023; Cited By: 11 PY - 2022 SN - 1664042X (ISSN) ST - Deep learning techniques for cancer classification using microarray gene expression data T2 - Frontiers in Physiology TI - Deep learning techniques for cancer classification using microarray gene expression data UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140997612&doi=10.3389%2ffphys.2022.952709&partnerID=40&md5=47280af86d9d2f9e2ba03c462bd82c7d VL - 13 ID - 18 ER - TY - JOUR AB - Article Title and Bibliographic Information: Mohammad-Rahimi H, Reza Motamedian S, Hossein Rohban M, Krois J, Uribe SE, Mahmoudinia E, Rokhshad R, Nadimi M, Schwendicke F, Deep learning for caries detection: A systematic review, J Dent, 2022,122, 104115. ISSN 0300-5712 https://doi.org/10.1016/j.jdent.2022.104115. Source of Funding: Information not available Type of Study/Design: Systematic review © 2022 Elsevier Inc. AU - Hegde, S. AU - Gao, J. C2 - 36494110 C7 - 101772 DB - Scopus DO - 10.1016/j.jebdp.2022.101772 IS - 4 KW - Accuracy Deep learning algorithms Dental caries Dental images Diagnosis Sensitivity Specificity Algorithms Deep Learning Dental Care Humans algorithm dental procedure human M3 - Note N1 - Export Date: 28 December 2023; Cited By: 0 PY - 2022 SN - 15323382 (ISSN) ST - DEEP LEARNING ALGORITHMS SHOW SOME POTENTIAL AS AN ADJUNCTIVE TOOL IN CARIES DIAGNOSIS T2 - Journal of Evidence-Based Dental Practice TI - DEEP LEARNING ALGORITHMS SHOW SOME POTENTIAL AS AN ADJUNCTIVE TOOL IN CARIES DIAGNOSIS UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141531905&doi=10.1016%2fj.jebdp.2022.101772&partnerID=40&md5=5d7772de2e1db6cf6bd90f8a2c5e0374 VL - 22 ID - 28 ER - TY - JOUR AB - Background/purpose: Artificial intelligence (AI) has made deep inroads into dentistry in the last few years. The aim of this systematic review was to identify the development of AI applications that are widely employed in dentistry and evaluate their performance in terms of diagnosis, clinical decision-making, and predicting the prognosis of the treatment. Materials and methods: The literature for this paper was identified and selected by performing a thorough search in the electronic data bases like PubMed, Medline, Embase, Cochrane, Google scholar, Scopus, Web of science, and Saudi digital library published over the past two decades (January 2000–March 15, 2020).After applying inclusion and exclusion criteria, 43 articles were read in full and critically analyzed. Quality analysis was performed using QUADAS-2. Results: AI technologies are widely implemented in a wide range of dentistry specialties. Most of the documented work is focused on AI models that rely on convolutional neural networks (CNNs) and artificial neural networks (ANNs). These AI models have been used in detection and diagnosis of dental caries, vertical root fractures, apical lesions, salivary gland diseases, maxillary sinusitis, maxillofacial cysts, cervical lymph nodes metastasis, osteoporosis, cancerous lesions, alveolar bone loss, predicting orthodontic extractions, need for orthodontic treatments, cephalometric analysis, age and gender determination. Conclusion: These studies indicate that the performance of an AI based automated system is excellent. They mimic the precision and accuracy of trained specialists, in some studies it was found that these systems were even able to outmatch dental specialists in terms of performance and accuracy. © 2020 Association for Dental Sciences of the Republic of China AU - Khanagar, S. B. AU - Al-ehaideb, A. AU - Maganur, P. C. AU - Vishwanathaiah, S. AU - Patil, S. AU - Baeshen, H. A. AU - Sarode, S. C. AU - Bhandi, S. DB - Scopus DO - 10.1016/j.jds.2020.06.019 IS - 1 KW - Artificial intelligence dentistry Artificial neural networks Computer-aided diagnosis Convolutional neural networks Deep learning models Machine learning M3 - Review N1 - Export Date: 28 December 2023; Cited By: 161 PY - 2021 SN - 19917902 (ISSN) SP - 508-522 ST - Developments, application, and performance of artificial intelligence in dentistry – A systematic review T2 - Journal of Dental Sciences TI - Developments, application, and performance of artificial intelligence in dentistry – A systematic review UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087203517&doi=10.1016%2fj.jds.2020.06.019&partnerID=40&md5=51ecc86d916aed248a155f7251571631 VL - 16 ID - 17 ER - TY - JOUR AB - Oral diseases are the most prevalent chronic childhood diseases, presenting as a major public health issue affecting children of all ages in the developing and developed countries. Early detection and control of these diseases is very crucial for a child’s oral health and general wellbeing. The aim of this systematic review is to assess the performance of artificial intelligence models designed for application in pediatric dentistry. A systematic search of the literature was conducted using different electronic databases, primarily (PubMed, Scopus, Web of Science, Embase, Cochrane) and secondarily (Google Scholar and the Saudi Digital Library) for studies published from 1 January 2000, until 20 July 2022, related to the research topic. The quality of the twenty articles that satisfied the eligibility criteria were critically analyzed based on the QUADAS-2 guidelines. Artificial intelligence models have been utilized for the detection of plaque on primary teeth, prediction of children’s oral health status (OHS) and treatment needs (TN); detection, classification and prediction of dental caries; detection and categorization of fissure sealants; determination of the chronological age; determination of the impact of oral health on adolescent’s quality of life; automated detection and charting of teeth; and automated detection and classification of mesiodens and supernumerary teeth in primary or mixed dentition. Artificial intelligence has been widely applied in pediatric dentistry in order to help less-experienced clinicians in making more accurate diagnoses. These models are very efficient in identifying and categorizing children into various risk groups at the individual and community levels. They also aid in developing preventive strategies, including designing oral hygiene practices and adopting healthy eating habits for individuals. © 2022 by the authors. AU - Khanagar, S. B. AU - Alfouzan, K. AU - Alkadi, L. AU - Albalawi, F. AU - Iyer, K. AU - Awawdeh, M. C7 - 9819 DB - Scopus DO - 10.3390/app12199819 IS - 19 KW - age estimation artificial intelligence automated learning caries detection deep learning machine learning pediatric dentistry pedodontics prediction M3 - Review N1 - Export Date: 28 December 2023; Cited By: 0 PY - 2022 SN - 20763417 (ISSN) ST - Performance of Artificial Intelligence (AI) Models Designed for Application in Pediatric Dentistry—A Systematic Review T2 - Applied Sciences (Switzerland) TI - Performance of Artificial Intelligence (AI) Models Designed for Application in Pediatric Dentistry—A Systematic Review UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140011210&doi=10.3390%2fapp12199819&partnerID=40&md5=d22c9b33a0104b9441837a4cc0203dcc VL - 12 ID - 29 ER - TY - JOUR AB - Evolution in the fields of science and technology has led to the development of newer applications based on Artificial Intelligence (AI) technology that have been widely used in medical sciences. AI-technology has been employed in a wide range of applications related to the diagnosis of oral diseases that have demonstrated phenomenal precision and accuracy in their performance. The aim of this systematic review is to report on the diagnostic accuracy and performance of AI-based models designed for detection, diagnosis, and prediction of dental caries (DC). Eminent elec-tronic databases (PubMed, Google scholar, Scopus, Web of science, Embase, Cochrane, Saudi Digital Library) were searched for relevant articles that were published from January 2000 until February 2022. A total of 34 articles that met the selection criteria were critically analyzed based on QUADAS-2 guidelines. The certainty of the evidence of the included studies was assessed using the GRADE approach. AI has been widely applied for prediction of DC, for detection and diagnosis of DC and for classification of DC. These models have demonstrated excellent performance and can be used in clinical practice for enhancing the diagnostic performance, treatment quality and patient outcome and can also be applied to identify patients with a higher risk of developing DC. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. AU - Khanagar, S. B. AU - Alfouzan, K. AU - Awawdeh, M. AU - Alkadi, L. AU - Albalawi, F. AU - Alfadley, A. C7 - 1083 DB - Scopus DO - 10.3390/diagnostics12051083 IS - 5 KW - artificial intelligence dental caries detection diagnosis prediction accuracy clinical practice decision making deep neural network dental practice dental procedure diagnostic accuracy disease classification head and neck cancer human micro-computed tomography panoramic radiography predictive value Review sensitivity and specificity support vector machine systematic review task performance M3 - Review N1 - Export Date: 28 December 2023; Cited By: 9 PY - 2022 SN - 20754418 (ISSN) ST - Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)—A Systematic Review T2 - Diagnostics TI - Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)—A Systematic Review UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129584447&doi=10.3390%2fdiagnostics12051083&partnerID=40&md5=de862fde063430b4fdd8bd70815feb38 VL - 12 ID - 3 ER - TY - JOUR AB - Objectives: Detecting caries lesions is challenging for dentists, and deep learning models may help practitioners to increase accuracy and reliability. We aimed to systematically review deep learning studies on caries detection. Data: We selected diagnostic accuracy studies that used deep learning models on dental imagery (including radiographs, photographs, optical coherence tomography images, near-infrared light transillumination images). The latest version of the quality assessment tool for diagnostic accuracy studies (QUADAS-2) tool was used for risk of bias assessment. Meta-analysis was not performed due to heterogeneity in the studies methods and their performance measurements. Sources: Databases (Medline via PubMed, Google Scholar, Scopus, Embase) and a repository (ArXiv) were screened for publications published after 2010, without any limitation on language. Study selection: From 252 potentially eligible references, 48 studies were assessed full-text and 42 included, using classification (n = 26), object detection (n = 6), or segmentation models (n = 10). A wide range of performance metrics was used; image, object or pixel accuracy ranged between 68%-99%. The minority of studies (n = 11) showed a low risk of biases in all domains, and 13 studies (31.0%) low risk for concerns regarding applicability. The accuracy of caries classification models varied, i.e. 71% to 96% on intra-oral photographs, 82% to 99.2% on peri-apical radiographs, 87.6% to 95.4% on bitewing radiographs, 68.0% to 78.0% on near-infrared transillumination images, 88.7% to 95.2% on optical coherence tomography images, and 86.1% to 96.1% on panoramic radiographs. Pooled diagnostic odds ratios varied from 2.27 to 32,767. For detection and segmentation models, heterogeneity in reporting did not allow useful pooling. Conclusion: An increasing number of studies investigated caries detection using deep learning, with a diverse types of architectures being employed. Reported accuracy seems promising, while study and reporting quality are currently low. Clinical significance: Deep learning models can be considered as an assistant for decisions regarding the presence or absence of carious lesions. © 2022 Elsevier Ltd AU - Mohammad-Rahimi, H. AU - Motamedian, S. R. AU - Rohban, M. H. AU - Krois, J. AU - Uribe, S. E. AU - Mahmoudinia, E. AU - Rokhshad, R. AU - Nadimi, M. AU - Schwendicke, F. C2 - 35367318 C7 - 104115 DB - Scopus DO - 10.1016/j.jdent.2022.104115 KW - Artificial intelligence Dental caries Dentistry Machine learning Neural networks Systematic review Deep Learning Dental Caries Susceptibility Humans Reproducibility of Results Sensitivity and Specificity diagnostic imaging human reproducibility M3 - Review N1 - Export Date: 28 December 2023; Cited By: 44; CODEN: JDENA PY - 2022 SN - 03005712 (ISSN) ST - Deep learning for caries detection: A systematic review T2 - Journal of Dentistry TI - Deep learning for caries detection: A systematic review UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131701962&doi=10.1016%2fj.jdent.2022.104115&partnerID=40&md5=0e927a983405ca3335a96952aae99957 VL - 122 ID - 5 ER - TY - JOUR AB - Objectives: This systematic review aimed at evaluating the performance of artificial intelligence (AI) models in detecting dental caries on oral photographs. Methods: Methodological characteristics and performance metrics of clinical studies reporting on deep learning and other machine learning algorithms were assessed. The risk of bias was evaluated using the quality assessment of diagnostic accuracy studies 2 (QUADAS-2) tool. A systematic search was conducted in EMBASE, Medline, and Scopus. Results: Out of 3410 identified records, 19 studies were included with six and seven studies having low risk of biases and applicability concerns for all the domains, respectively. Metrics varied widely and were assessed on multiple levels. F1-scores for classification and detection tasks were 68.3%–94.3% and 42.8%–95.4%, respectively. Irrespective of the task, F1-scores were 68.3%–95.4% for professional cameras, 78.8%–87.6%, for intraoral cameras, and 42.8%–80% for smartphone cameras. Limited studies allowed assessing AI performance for lesions of different severity. Conclusion: Automatic detection of dental caries using AI may provide objective verification of clinicians' diagnoses and facilitate patient-clinician communication and teledentistry. Future studies should consider more robust study designs, employ comparable and standardized metrics, and focus on the severity of caries lesions. © 2023 The Authors. Oral Diseases published by Wiley Periodicals LLC. AU - Moharrami, M. AU - Farmer, J. AU - Singhal, S. AU - Watson, E. AU - Glogauer, M. AU - Johnson, A. E. W. AU - Schwendicke, F. AU - Quinonez, C. C2 - 37392423 DB - Scopus DO - 10.1111/odi.14659 KW - deep learning dental caries intraoral camera oral photograph smartphone M3 - Review N1 - Export Date: 28 December 2023; Cited By: 3; CODEN: ORDIF PY - 2023 SN - 1354523X (ISSN) ST - Detecting dental caries on oral photographs using artificial intelligence: A systematic review T2 - Oral Diseases TI - Detecting dental caries on oral photographs using artificial intelligence: A systematic review UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164153646&doi=10.1111%2fodi.14659&partnerID=40&md5=e1707cc7b7ea67211c46ac8d4954e577 ID - 31 ER - TY - JOUR AB - Purpose: The aim of this study was to analyse and review deep learning convolutional neural networks for detecting and diagnosing early-stage dental caries on periapical radiographs. Materials and Methods: In order to conduct this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies published from 2015 to 2021 under the keywords (deep convolutional neural network) AND (caries), (deep learning caries) AND (convolutional neural network) AND (caries) were systematically reviewed. Results: When dental caries is improperly diagnosed, the lesion may eventually invade the enamel, dentin, and pulp tissue, leading to loss of tooth function. Rapid and precise detection and diagnosis are vital for implementing appropriate prevention and treatment of dental caries. Radiography and intraoral images are considered to play a vital role in detecting dental caries; nevertheless, studies have shown that 20% of suspicious areas are mistakenly diagnosed as dental caries using this technique; hence, diagnosis via radiography alone without an objective assessment is inaccurate. Identifying caries with a deep convolutional neural network-based detector enables the operator to distinguish changes in the location and morphological features of dental caries lesions. Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects. Conclusion: Clinical applications of deep learning convolutional neural networks in the dental field have shown significant accuracy in detecting and diagnosing dental caries, and these models hold promise in supporting dental practitioners to improve patient outcomes. (Imaging Sci Dent 20210074) © 2021 by Korean Academy of Oral and Maxillofacial Radiology AU - Musri, N. AU - Christie, B. AU - Ichwan, S. J. A. AU - Cahyanto, A. DB - Scopus DO - 10.5624/ISD.20210074 KW - Deep Learning Dental Dental Caries Neural Network Models Radiography M3 - Article N1 - Export Date: 28 December 2023; Cited By: 4 PY - 2021 SN - 22337822 (ISSN) SP - 1-6 ST - Deep learning convolutional neural network algorithms for the early detection and diagnosis of dental caries on periapical radiographs: A systematic review T2 - Imaging Science in Dentistry TI - Deep learning convolutional neural network algorithms for the early detection and diagnosis of dental caries on periapical radiographs: A systematic review UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111779105&doi=10.5624%2fISD.20210074&partnerID=40&md5=d3e19ce1cd389933afebd75cc1512e23 VL - 51 ID - 15 ER - TY - JOUR AB - Objectives: To critically examine the methods used for full economic evaluations of preventive interventions for dental caries and periodontitis. Methods: Published literature post-2000 was searched to April 2021. Based on a developed intervention classification framework for dental caries and periodontitis, only universal, selective or indicated interventions were included in this review. The Drummond 10-point checklist was used for quality appraisal. Results: Of 3,007 unique records screened for relevance, 73 studies were reviewed. Most model-based studies (61/73) used cost-effectiveness analysis (49%) or cost-benefit analysis (28%). Trial-based studies (16/73) commonly used cost-effectiveness analysis (59%). Four studies used both economic evaluation methods. Sixty-four papers (88%) were on dental caries, eight papers (11%) focused on periodontitis, and one paper (1%) included both oral diseases; 72% of model-based and 82% of trial-based studies were of good quality. The most frequently investigated dental caries preventive interventions were water fluoridation (universal intervention; cost-saving or cost-effective), fissure sealant and fluoride varnish (selective and indicated interventions; cost-effectiveness outcomes were inconsistent). Supportive periodontal therapy with oral health education (indicated intervention; cost-effective) was the most frequently evaluated preventive intervention for periodontitis. Thirty percent of studies with a time horizon > 1 year did not apply an appropriate discount rate and 26% did not comprehensively discuss other important considerations beyond the technical analysis. Conclusions: Generic health outcome measures should be incorporated for economic evaluations on preventive interventions for dental caries and periodontitis, and an increased focus to prevent periodontitis using economic evaluation methods is needed to inform resource allocation and policy decision-making. © 2022, The Author(s). AU - Nguyen, T. M. AU - Tonmukayakul, U. AU - Le, L. K. D. AU - Calache, H. AU - Mihalopoulos, C. DB - Scopus DO - 10.1007/s40258-022-00758-5 IS - 1 KW - chlorhexidine fissure sealant fluoride varnish interleukin 1 mouthwash xylitol adult artificial intelligence checklist child clinical attachment level cost cost benefit analysis cost control cost effectiveness analysis dental caries dental procedure disability-adjusted life year economic evaluation education fluoridation genetic screening health education health status human interrater reliability mouth hygiene periodontitis prophylaxis quality adjusted life year Review societal cost sugar-sweetened beverage systematic review telehealth tooth brushing tooth extraction tooth fracture tooth root canal Willingness To Pay M3 - Review N1 - Export Date: 28 December 2023; Cited By: 6; CODEN: AHEHA PY - 2023 SN - 11755652 (ISSN) SP - 53-70 ST - Economic Evaluations of Preventive Interventions for Dental Caries and Periodontitis: A Systematic Review T2 - Applied Health Economics and Health Policy TI - Economic Evaluations of Preventive Interventions for Dental Caries and Periodontitis: A Systematic Review UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137845985&doi=10.1007%2fs40258-022-00758-5&partnerID=40&md5=3048739b000616262826526b0d9d0daa VL - 21 ID - 23 ER - TY - JOUR AB - Water, sanitation, and hygiene provisions are essential during emergencies to prevent infectious disease outbreaks caused by improper human excreta management in settlements for people affected by natural disasters and conflicts. Human excreta disinfection is required when long-term containment in latrines is not feasible on-site. Alkali additives, including lime, are effective disinfectants for wastewater and faecal sludge containing large amounts of solid and dissolved organic matter. The aim of this study was to determine the minimum dose and contact time of alkali additives for treating virus-containing human excreta in emergency situations. We used literature data collected by searching Google Scholar and Web of Science. The date of the last search for each study was 31th May 2023. Only peer-reviewed articles that included disinfection practices in combination with quantitative data for the physicochemical data of a matrix and viral decay were selected for data extraction. Two reviewers independently collected data from each study. We extracted datasets from 14 studies that reported quantitative information about their disinfection tests, including viral decay over time, matrix types, and physicochemical properties. Three machine learning algorithms were applied to the collected dataset to determine the time required to achieve specified levels of virus inactivation under different environmental conditions. The best model was used to estimate the contact time to achieve a 3-log10 inactivation of RNA virus in wastewater and faeces. The most important variables for predicting the contact time were pH, temperature, and virus type. The estimated contact time for 3 log inactivation of RNA virus was <2 h at pH 12, which was achieved by adding 1.8 and 3.1% slaked lime to wastewater and faeces, respectively. The contact time decreased exponentially with the pH of the sludge and wastewater. In contrast, the pH of the sludge and wastewater increased linearly with the slaked lime dosage. Lime treatment is a promising measure where long-term containment in latrine is not feasible in densely populated areas, as 1 day is sufficient to inactivate viruses. The relationship we have identified between required contact time and lime dosage is useful for practitioners in determining appropriate treatment conditions of human waste. Copyright © 2023 Oishi and Sano. AU - Oishi, W. AU - Sano, D. C2 - 38026419 C7 - 1286595 DB - Scopus DO - 10.3389/fpubh.2023.1286595 KW - alkaline treatment disinfection machine learning sanitation slaked lime viruses Disinfectants Humans Sewage Wastewater calcium oxide disinfectant agent chemistry human virus M3 - Article N1 - Export Date: 28 December 2023; Cited By: 0 PY - 2023 SN - 22962565 (ISSN) ST - Estimation of alkali dosage and contact time for treating human excreta containing viruses as an emergency response: a systematic review T2 - Frontiers in Public Health TI - Estimation of alkali dosage and contact time for treating human excreta containing viruses as an emergency response: a systematic review UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177658096&doi=10.3389%2ffpubh.2023.1286595&partnerID=40&md5=d1e392a3a7742d3449355491e7b8bf9f VL - 11 ID - 27 ER - TY - CONF AB - The use of Artificial Intelligence with the help of machine learning has eased the work of healthcare practitioners. This systematic review aims to find effective machine learning models to detect dental caries and oral cancer. Image datasets used in the studies ranged from 74 to 3000 images. Different researchers used different approaches and different evaluation metrics to evaluate their studies with Accuracy and Area Under the Curve (AUC) being the most common metrics. The current implementations of machine learning models lay a foundation to reduce the time and effort required to develop newer models for future developments. Overcoming limitations of small datasets, data integration, and dataset standardization will increase the performance and accuracy of the models and will help machine learning become an integral part of dentistry. © 2022 IEEE. AU - Patil, A. AU - Bhalekar, M. AU - Dhatrak, P. DB - Scopus DO - 10.1109/ICETET-SIP-2254415.2022.9791585 KW - Deep Learning Dental Caries Dentistry Machine Learning Oral Cancer Data integration Diseases Learning systems 'current Areas under the curves Evaluation metrics Image datasets Machine learning models Machine-learning Systematic Review N1 - Export Date: 28 December 2023; Cited By: 0 PB - IEEE Computer Society PY - 2022 SN - 21570477 (ISSN); 978-166546741-4 (ISBN) ST - Enhancement in Dentistry using Machine Learning: A Systematic Review T2 - International Conference on Emerging Trends in Engineering and Technology, ICETET TI - Enhancement in Dentistry using Machine Learning: A Systematic Review UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132306002&doi=10.1109%2fICETET-SIP-2254415.2022.9791585&partnerID=40&md5=7f0d36991a941c1d2bb86dce2d4cc519 VL - 2022-April ID - 12 ER - TY - JOUR AB - Dental caries is the most prevalent dental disease worldwide, and neural networks and artificial intelligence are increasingly being used in the field of dentistry. This systematic review aims to identify the state of the art of neural networks in caries detection and diagnosis. A search was conducted in PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect. Data extraction was performed independently by two reviewers. The quality of the selected studies was assessed using the Cochrane Handbook tool. Thirteen studies were included. Most of the included studies employed periapical, near-infrared light transillumination, and bitewing radiography. The image databases ranged from 87 to 3000 images, with a mean of 669 images. Seven of the included studies labeled the dental caries in each image by experienced dentists. Not all of the studies detailed how caries was defined, and not all detailed the type of carious lesion detected. Each study included in this review used a different neural network and different outcome metrics. All this variability complicates the conclusions that can be made about the reliability or not of a neural network to detect and diagnose caries. A comparison between neural network and dentist results is also necessary. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. AU - Prados-Privado, M. AU - Villalón, J. G. AU - Martínez-Martínez, C. H. AU - Ivorra, C. AU - Prados-Frutos, J. C. C7 - 3579 DB - Scopus DO - 10.3390/jcm9113579 IS - 11 KW - Artificial intelligence Caries Detection Images artificial neural network computer assisted diagnosis data base dental caries diagnostic accuracy human molar tooth panoramic radiography premolar tooth Review systematic review tooth radiography transillumination M3 - Review N1 - Export Date: 28 December 2023; Cited By: 42 PY - 2020 SN - 20770383 (ISSN) SP - 1-13 ST - Dental caries diagnosis and detection using neural networks: A systematic review T2 - Journal of Clinical Medicine TI - Dental caries diagnosis and detection using neural networks: A systematic review UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104307186&doi=10.3390%2fjcm9113579&partnerID=40&md5=b505af1376d43f66ca7f7d28b9761e51 VL - 9 ID - 25 ER - TY - JOUR AB - Objectives: In recent years, periodontitis, and dental caries have become common in humans and need to be diagnosed in the early stage to prevent severe complications and tooth loss. These dental issues are diagnosed by visual inspection, measuring pocket probing depth, and radiographs findings from experienced dentists. Though a glut of machine learning (ML) algorithms has been proposed for the automated detection of periodontitis, and dental caries, determining which ML techniques are suitable for clinical practice remains under debate. This review aims to identify the research challenges by analyzing the limitations of current methods and how to address these to obtain robust systems suitable for clinical use or point-of-care testing. Methods: An extensive search of the literature published from 2015 to 2022 written in English, related to the subject of study was sought by searching the electronic databases: PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect. Results: The initial electronic search yielded 1743 titles, and 55 studies were eventually included based on the selection criteria adopted in this review. Studies selected were on ML applications for the automatic detection of periodontitis and dental caries and related dental issues: Apical lessons, Periodontal bone loss, and Vertical root fracture. Conclusion: While most of the ML-based studies use radiograph images for the detection of periodontitis and dental caries, few pieces of the literature revealed that good diagnostic accuracy could be achieved by training the ML model even with mobile photos representing the images of dental issues. Nowadays smartphones are used in every sector for different applications. Training the ML model with as many images of dental issues captured by the smartphone can achieve good accuracy, reduce the cost of clinical diagnosis, and provide user interaction. © 2023 Elsevier B.V. AU - Radha, R. C. AU - Raghavendra, B. S. AU - Subhash, B. V. AU - Rajan, J. AU - Narasimhadhan, A. V. C2 - 37595373 C7 - 105170 DB - Scopus DO - 10.1016/j.ijmedinf.2023.105170 KW - Classification Dental caries Detection Imaging techniques Machine Learning Periodontitis Humans Clinical research Diagnosis Diseases Learning algorithms Machine learning algorithms Machine learning models Machine learning techniques Machine-learning Periodontiti Pocket probing Smart phones Visual inspection alveolar bone loss diagnostic accuracy human meta analysis point of care testing Review systematic review Smartphones M3 - Review N1 - Export Date: 28 December 2023; Cited By: 1; CODEN: IJMIF PY - 2023 SN - 13865056 (ISSN) ST - Machine learning techniques for periodontitis and dental caries detection: A narrative review T2 - International Journal of Medical Informatics TI - Machine learning techniques for periodontitis and dental caries detection: A narrative review UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168012265&doi=10.1016%2fj.ijmedinf.2023.105170&partnerID=40&md5=7bf2e45df9669f14d2ca45292034acbc VL - 178 ID - 19 ER - TY - JOUR AB - STATEMENT OF PROBLEM: Artificial intelligence (AI) applications are increasing in restorative procedures. However, the current development and performance of AI in restorative dentistry applications has not yet been systematically documented and analyzed. PURPOSE: The purpose of this systematic review was to identify and evaluate the ability of AI models in restorative dentistry to diagnose dental caries and vertical tooth fracture, detect tooth preparation margins, and predict restoration failure. MATERIAL AND METHODS: An electronic systematic review was performed in 5 databases: MEDLINE/PubMed, EMBASE, World of Science, Cochrane, and Scopus. A manual search was also conducted. Studies with AI models were selected based on 4 criteria: diagnosis of dental caries, diagnosis of vertical tooth fracture, detection of the tooth preparation finishing line, and prediction of restoration failure. Two investigators independently evaluated the quality assessment of the studies by applying the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus. RESULTS: A total of 34 articles were included in the review: 29 studies included AI techniques for the diagnosis of dental caries or the elaboration of caries and postsensitivity prediction models, 2 for the diagnosis of vertical tooth fracture, 1 for the tooth preparation finishing line location, and 2 for the prediction of the restoration failure. Among the studies reviewed, the AI models tested obtained a caries diagnosis accuracy ranging from 76% to 88.3%, sensitivity ranging from 73% to 90%, and specificity ranging from 61.5% to 93%. The caries prediction accuracy among the studies ranged from 83.6% to 97.1%. The studies reported an accuracy for the vertical tooth fracture diagnosis ranging from 88.3% to 95.7%. The article using AI models to locate the finishing line reported an accuracy ranging from 90.6% to 97.4%. CONCLUSIONS: AI models have the potential to provide a powerful tool for assisting in the diagnosis of caries and vertical tooth fracture, detecting the tooth preparation margin, and predicting restoration failure. However, the dental applications of AI models are still in development. Further studies are required to assess the clinical performance of AI models in restorative dentistry. Copyright © 2021 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved. AU - Revilla-León, M. AU - Gómez-Polo, M. AU - Vyas, S. AU - Barmak, A. B. AU - Özcan, M. AU - Att, W. AU - Krishnamurthy, V. R. C2 - 33840515 DB - Scopus DO - 10.1016/j.prosdent.2021.02.010 IS - 5 KW - Artificial Intelligence Dental Caries Dental Restoration, Permanent Dentistry Humans Tooth Fractures dental restoration human procedures tooth fracture M3 - Review N1 - Export Date: 28 December 2023; Cited By: 29 PY - 2022 SN - 10976841 (ISSN) SP - 867-875 ST - Artificial intelligence applications in restorative dentistry: A systematic review T2 - The Journal of prosthetic dentistry TI - Artificial intelligence applications in restorative dentistry: A systematic review UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103943759&doi=10.1016%2fj.prosdent.2021.02.010&partnerID=40&md5=3b90c9b9e4d77e6c614ca263816371ca VL - 128 ID - 7 ER - TY - JOUR AB - We performed a systematic review to evaluate the success of machine learning algorithms in the diagnosis and prognostic prediction of dental caries. The review protocol was a priori registered in the PROSPERO, CRD42020183447. The search involved electronic bibliographic databases: PubMed/Medline, Scopus, EMBASE, Web of Science, and grey literature until December 2020. We excluded review articles, case series, case reports, editorials, letters, comments, educational methodologies, assessments of robotic devices, and articles with less than 10 participants or specimens. Two independent reviewers selected the studies and performed the assessment of the methodological quality based on standardized scales. We summarize data on the machine learning algorithms used; software; performance outcomes such as accuracy/precision, sensitivity/recall, specificity, area under the receiver operating characteristic curve (AUC), and positive/negative predictive values related to dental caries. Meta-analyses were not performed due to methodological differences. Our review included 15 studies (10 diagnostic studies and 5 prognostic prediction studies). Cross-sectional design studies were predominant (12). The most frequently used statistical measure of performance reported in diagnostic studies was AUC value, which ranged from 0.745 to 0.987. For most diagnostic studies, data from contingency tables were not available. Reported sensitivities were higher in low risk of bias prognostic prediction studies (median [IQR] of 0.996 [0.971-1.000] vs. unclear/high risk of bias studies 0.189 [0-0.340]; p value 0.025). While there were no significant differences in the specificity between these subgroups, we concluded that the use of these technologies for the diagnosis and prognostic prediction of dental caries, although promising, is at an early stage. The general applicability of the evidence was limited given that most models were developed outside the real clinical setting with a prevalence of unclear/high risk of bias. Researchers must increase the overall quality of their research protocols by providing a comprehensive report on the methods implemented. © 2022 S. Karger AG, Basel. Copyright: All rights reserved. AU - Reyes, L. T. AU - Knorst, J. K. AU - Ortiz, F. R. AU - Ardenghi, T. M. DB - Scopus DO - 10.1159/000524167 IS - 3 KW - Artificial intelligence Dental caries Diagnosis Machine learning Prognosis M3 - Review N1 - Export Date: 28 December 2023; Cited By: 5; CODEN: CAREB PY - 2022 SN - 00086568 (ISSN) SP - 161-170 ST - Machine Learning in the Diagnosis and Prognostic Prediction of Dental Caries: A Systematic Review T2 - Caries Research TI - Machine Learning in the Diagnosis and Prognostic Prediction of Dental Caries: A Systematic Review UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138407767&doi=10.1159%2f000524167&partnerID=40&md5=ff237da82e0a350b94b960d8ecc1fe2c VL - 56 ID - 6 ER - TY - JOUR AB - Background: With the advent of deep learning in modern computing there has been unprecedented progress in image processing and segmentation. Deep learning-based image pattern recognition achieved a significant place in interpreting dental radiographs towards automatic diagnosis and treatment. In context with dental imaging, deep learning-based image analysis has been able to perform dental structure segmentation, classification, and identification of several common dental diseases with significant 90% accuracy. These results open a window of hope for better diagnosis and treatment planning in dental medicine. This review systematically presents recent advances in deep learning-based dental and maxillofacial image analysis. Materials and methods: We performed an extensive literature survey using the PubMed literature repository for identifying suitable articles. We shortlisted more than 75 articles that use deep learning for dental image segmentation, object detection, classification, and other image processing-related tasks. This study includes variables such as the size of the dataset, dental imaging modality, deep learning architecture, and performance evaluation measures. Results: We have summarized recent developments and a concise overview of studies on various applications of dental and maxillofacial image analysis. We primarily discussed how deep learning techniques have been exploited in areas such as tooth detection and labeling, dental caries, plaque, periodontal condition, osteoporosis, oral lesion, anatomical landmarking, age, and gender estimation. The challenges and future research directions in the area have been extensively discussed. Conclusion: Undoubtedly remarkable progress is witnessed in dental image analysis in recent years. However, many crucial aspects still need to be addressed including standardization of data and generalization in AI-based solutions towards dental and maxillofacial image analysis for the diagnosis and better treatment aid in the field of dentistry which will open a new avenue in dental clinical practices. © 2022 Elsevier Ltd AU - Singh, N. K. AU - Raza, K. C7 - 116968 DB - Scopus DO - 10.1016/j.eswa.2022.116968 KW - Artificial Intelligence Convolutional neural network Deep learning Dental images Machine learning Diagnosis Diseases Image segmentation Object detection Dental imaging Dental radiographs Image pattern recognition Image-analysis Images processing Images segmentations Systematic Review Convolutional neural networks M3 - Review N1 - Export Date: 28 December 2023; Cited By: 21; CODEN: ESAPE PY - 2022 SN - 09574174 (ISSN) ST - Progress in deep learning-based dental and maxillofacial image analysis: A systematic review T2 - Expert Systems with Applications TI - Progress in deep learning-based dental and maxillofacial image analysis: A systematic review UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127025133&doi=10.1016%2fj.eswa.2022.116968&partnerID=40&md5=0cfc595b3af0e3e9abd2ac683e653a23 VL - 199 ID - 11 ER - TY - JOUR AB - Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019–May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detection, and segmentation tasks. According to the results, the most commonly used task type was classification (n = 51), the most commonly used dental image material was panoramic radiographs (n = 55), and the most frequently used performance metric was sensitivity/recall/true positive rate (n = 87) and accuracy (n = 69). Dataset sizes ranged from 60 to 12,179 images. Although deep learning algorithms are used as individual or at least individualized architectures, standardized architectures such as pre-trained CNNs, Faster R-CNN, YOLO, and U-Net have been used in most studies. Few studies have used the explainable AI method (n = 22) and applied tests comparing human and artificial intelligence (n = 21). Deep learning is promising for better diagnosis and treatment planning in dentistry based on the high-performance results reported by the studies. For all that, their safety should be demonstrated using a more reproducible and comparable methodology, including tests with information about their clinical applicability, by defining a standard set of tests and performance metrics. © 2023 by the authors. AU - Sivari, E. AU - Senirkentli, G. B. AU - Bostanci, E. AU - Guzel, M. S. AU - Acici, K. AU - Asuroglu, T. C7 - 2512 DB - Scopus DO - 10.3390/diagnostics13152512 IS - 15 KW - convolutional neural network deep learning dental anomalies and diseases dental diagnostics dental images alexnet alveolar bone alveolar bone loss ameloblastoma amelogenesis imperfecta area under the curve artificial intelligence bone cyst class activation mapping classification algorithm classifier cleft lip cleft palate clinical evaluation cone beam computed tomography controlled study data base data extraction densenet121 dental apical lesion dental caries dental enamel hypomineralization dental fluorosis dental health dentigerous cyst dentin dentistry diagnostic accuracy diagnostic test accuracy study dice similarity coefficient efficientdetd3 enamel enamel breakdown false negative result gingivitis granuloma human hybrid neural network hypoplasia image analysis image segmentation inceptionresnetv2 keratocyst long short term memory network mandible fracture maxillary canine impaction maxillary first molar maxillary sinus mesioden microdontia molar incisor hypomineralization object detection odontogenic cyst odontoma osteoarthritis outcome assessment panoramic radiography performance indicator periodontal disease periodontally compromised teeth periodontitis positivity rate predictive value radicular cyst residual root resnet18 resnext 101 Review segmentation algorithm segmentation task sensitivity analysis squeezenet supernumerary tooth support vector machine systematic review temporomandibular joint disorder third molar impacted teeth tooth disease tooth eruption tooth impaction tooth malformation tooth periapical disease tooth plaque transfer of learning treatment planning unit network vertical root fracture white spot lesion you only look once M3 - Review N1 - Export Date: 28 December 2023; Cited By: 2 PY - 2023 SN - 20754418 (ISSN) ST - Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review T2 - Diagnostics TI - Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167686867&doi=10.3390%2fdiagnostics13152512&partnerID=40&md5=558a5524bc309df287bf91ca6801692d VL - 13 ID - 22 ER - TY - JOUR AB - Background. Dental caries is one of the major oral health problems and is increasing rapidly among people of every age (children, men, and women). Deep learning, a field of Artificial Intelligence (AI), is a growing field nowadays and is commonly used in dentistry. AI is a reliable platform to make dental care better, smoother, and time-saving for professionals. AI helps the dentistry professionals to fulfil demands of patients and to ensure quality treatment and better oral health care. AI can also help in predicting failures of clinical cases and gives reliable solutions. In this way, it helps in reducing morbidity ratio and increasing quality treatment of dental problem in population. Objectives. The main objective of this study is to conduct a systematic review of studies concerning the association between dental caries and machine learning. The objective of this study is to design according to the PICO criteria. Materials and Methods. A systematic search for randomized trials was conducted under the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). In this study, e-search was conducted from four databases including PubMed, IEEE Xplore, Science Direct, and Google Scholar, and it involved studies from year 2008 to 2022. Result. This study fetched a total of 133 articles, from which twelve are selected for this systematic review. We analyzed different types of machine learning algorithms from which deep learning is widely used with dental caries images dataset. Neural Network Backpropagation algorithm, one of the deep learning algorithms, gives a maximum accuracy of 99%. Conclusion. In this systematic review, we concluded how deep learning has been applied to the images of teeth to diagnose the detection of dental caries with its three types (proximal, occlusal, and root caries). Considering our findings, further well-designed studies are needed to demonstrate the diagnosis of further types of dental caries that are based on progression (chronic, acute, and arrested), which tells us about the severity of caries, virginity of lesion, and extent of caries. Apart from dental caries, AI in the future will emerge as supreme technology to detect other diseases of oral region combinedly and comprehensively because AI will easily analyze big datasets that contain multiple records. © 2022 Sarena Talpur et al. AU - Talpur, S. AU - Azim, F. AU - Rashid, M. AU - Syed, S. A. AU - Talpur, B. A. AU - Khan, S. J. C2 - 35399834 C7 - 5032435 DB - Scopus DO - 10.1155/2022/5032435 KW - Algorithms Artificial Intelligence Child Dental Caries Female Humans Machine Learning Neural Networks, Computer Deep learning Dentistry Image analysis Learning algorithms Patient treatment Algorithm for diagnosis Dental care Dental problems Machine learning algorithms Meta-analysis Oral healths Randomized trial Systematic Review Systematic searches algorithm back propagation disease severity human measurement accuracy Medline meta analysis Preferred Reporting Items for Systematic Reviews and Meta-Analyses randomized controlled trial (topic) Review ScienceDirect search engine tooth radiography Diagnosis M3 - Review N1 - Export Date: 28 December 2023; Cited By: 11 PY - 2022 SN - 20402295 (ISSN) ST - Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries T2 - Journal of Healthcare Engineering TI - Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127902075&doi=10.1155%2f2022%2f5032435&partnerID=40&md5=6c9ef2c9e20e26f50330d60035d72306 VL - 2022 ID - 33 ER - TY - JOUR AB - Objectives: This overview of systematic reviews aimed to establish the current state of knowledge on the suitability of artificial intelligence (AI) in dental panoramic radiograph analysis and illustrate its changes over time. Methods: Medical databases covered by the Association for Computing Machinery, Bielefeld Academic Search Engine, Google Scholar, and PubMed engines were searched. The risk of bias was assessed using ROBIS tool. Ultimately, 12 articles were qualified for the qualitative synthesis. The results were visualized with timelines, tables, and charts. Results: In the years 1988-2023, a significant development of information technologies for the analysis of DPRs was observed. The latest analyzed AI models achieve high accuracy in detecting caries (91.5%), osteoporosis (89.29%), maxillary sinusitis (87.5%), periodontal bone loss (93.09%), and teeth identification and numbering (93.67%). The detection of periapical lesions is also characterized by high sensitivity (99.95%) and specificity (92%). However, due to the small number of heterogeneous source studies synthesized in systematic reviews, the results of this overview should be interpreted with caution. Conclusion: Currently, AI applications can significantly support dentists in dental panoramic radiograph analysis. As systematic reviews on AI become outdated quickly, their regular updating is recommended. © 2023 British Institute of Radiology. All rights reserved. AU - Turosz, N. AU - Chęcińska, K. AU - Chęciński, M. AU - Brzozowska, A. AU - Nowak, Z. AU - Sikora, M. C2 - 37665008 C7 - 20230284 DB - Scopus DO - 10.1259/dmfr.20230284 IS - 7 KW - Artificial Intelligence Deep learning Dental radiography Overview of reviews Panoramic radiographs Alveolar Bone Loss Dental Caries Humans Radiography, Panoramic Systematic Reviews as Topic human panoramic radiography systematic review (topic) M3 - Review N1 - Export Date: 28 December 2023; Cited By: 1; CODEN: DREAC PY - 2023 SN - 0250832X (ISSN) ST - Applications of artificial intelligence in the analysis of dental panoramic radiographs: an overview of systematic reviews T2 - Dentomaxillofacial Radiology TI - Applications of artificial intelligence in the analysis of dental panoramic radiographs: an overview of systematic reviews UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172425925&doi=10.1259%2fdmfr.20230284&partnerID=40&md5=9327921461afdf0fba091b3889a3635e VL - 52 ID - 1 ER - TY - CONF AB - In this study, the discrete element method (DEM) and experimental means are proposed to be used, combined with theoretical mechanics, powder mechanics, fluid mechanics, mechanical vibration and other mechanical theories. When studying the multi-factor and multi-curve superimposed vibration of multi-component mixed particles, the structure of vibrating body, vibration combination order, vibration time, vibration frequency, amplitude, particle factors and other factors affect the dynamic parameters, kinematic parameters, particle layer flow field, organization field and other laws of the separation process of multi-component mixed particles. Reveal the evolution mechanism of the breeding, growth, formation and decay of the separation configuration of multicomponent mixed particles, and carry out SPSS multivariate statistics and regression analysis to establish the corresponding mathematical model. On the basis of the above, a multi-factor and multi-curve superposition vibration separation experiment was carried out on the mixed particles of metal ore, and the separation process was effectively controlled by using MATLAB neural network tool. Finally, it is proposed to use the bionic method to observe the manually superimposed vibration modes such as round screen and dustpan, and use the bionic hand and bionic joint to design the mechanical and electrical drawings, action flow diagrams and specific procedures of the equipment, and manufacture the corresponding vibration equipment. The work will enrich the theory of vibration separation of mixed particles, and provide useful theoretical reference and practical equipment for the vibration separation process of multi-component mixed particles with different shapes, sizes and densities. © 2023 SPIE. AU - Zhang, H. AU - Juanatas, R. A. AU - Niguidula, J. D. AU - Cai, L. DB - Scopus DO - 10.1117/12.2681842 KW - Discrete element method MATLAB neural network tool SPSS multivariate statistics Superimposed vibration Vibration equipment Bionics Finite difference method Fluid mechanics Regression analysis Vibrations (mechanical) Discrete elements method Mixed particles Multicomponents Multivariate statistics Network tools Neural-networks SPSS multivariate statistic Vibration equipments Multivariant analysis N1 - Export Date: 28 December 2023; Cited By: 0; CODEN: PSISD PB - SPIE PY - 2023 SN - 0277786X (ISSN); 978-151066489-0 (ISBN) ST - A systematic review of vibration separation mechanism of multi-factor and multi-curve superposition for multi-component mixed particles T2 - Proceedings of SPIE - The International Society for Optical Engineering TI - A systematic review of vibration separation mechanism of multi-factor and multi-curve superposition for multi-component mixed particles UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170640379&doi=10.1117%2f12.2681842&partnerID=40&md5=7fed303a595601d5b5e31b335f138949 VL - 12639 ID - 4 ER -