PMID- 35367318 OWN - NLM STAT- MEDLINE DCOM- 20220608 LR - 20220720 IS - 1879-176X (Electronic) IS - 0300-5712 (Linking) VI - 122 DP - 2022 Jul TI - Deep learning for caries detection: A systematic review. PG - 104115 LID - S0300-5712(22)00172-5 [pii] LID - 10.1016/j.jdent.2022.104115 [doi] 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. CI - Copyright © 2022 Elsevier Ltd. All rights reserved. FAU - Mohammad-Rahimi, Hossein AU - Mohammad-Rahimi H AD - Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. FAU - Motamedian, Saeed Reza AU - Motamedian SR AD - Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Dentofacial Deformities Research Center, Research Institute of Dental Sciences & Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran. FAU - Rohban, Mohammad Hossein AU - Rohban MH AD - Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. FAU - Krois, Joachim AU - Krois J AD - Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany. FAU - Uribe, Sergio E AU - Uribe SE AD - Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Conservative Dentistry and Oral Health & Bioinformatics Research Unit, Riga Stradins University, Riga, Latvia; School of Dentistry, Universidad Austral de Chile, Valdivia, Chile; Baltic Biomaterials Centre of Excellence, Headquarters at Riga Technical University, Riga, Latvia. FAU - Mahmoudinia, Erfan AU - Mahmoudinia E AD - Dentofacial Deformities Research Center, Research Institute of Dental Sciences & Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran. FAU - Rokhshad, Rata AU - Rokhshad R AD - Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany. FAU - Nadimi, Mohadeseh AU - Nadimi M AD - Cardiovascular Diseases Research Center, Department of Cardiology, Heshmat Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran. FAU - Schwendicke, Falk AU - Schwendicke F AD - Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany. Electronic address: falk.schwendicke@charite.de. LA - eng PT - Journal Article PT - Review PT - Systematic Review DEP - 20220330 PL - England TA - J Dent JT - Journal of dentistry JID - 0354422 SB - IM MH - *Deep Learning MH - *Dental Caries/diagnostic imaging MH - Dental Caries Susceptibility MH - Humans MH - Reproducibility of Results MH - Sensitivity and Specificity OTO - NOTNLM OT - Artificial intelligence OT - Dental caries OT - Dentistry OT - Machine learning OT - Neural networks OT - Systematic review EDAT- 2022/04/04 06:00 MHDA- 2022/06/09 06:00 CRDT- 2022/04/03 20:24 PHST- 2022/01/02 00:00 [received] PHST- 2022/03/24 00:00 [revised] PHST- 2022/03/28 00:00 [accepted] PHST- 2022/04/04 06:00 [pubmed] PHST- 2022/06/09 06:00 [medline] PHST- 2022/04/03 20:24 [entrez] AID - S0300-5712(22)00172-5 [pii] AID - 10.1016/j.jdent.2022.104115 [doi] PST - ppublish SO - J Dent. 2022 Jul;122:104115. doi: 10.1016/j.jdent.2022.104115. Epub 2022 Mar 30. PMID- 33840515 OWN - NLM STAT- MEDLINE DCOM- 20221206 LR - 20230317 IS - 1097-6841 (Electronic) IS - 0022-3913 (Linking) VI - 128 IP - 5 DP - 2022 Nov TI - Artificial intelligence applications in restorative dentistry: A systematic review. PG - 867-875 LID - S0022-3913(21)00087-1 [pii] LID - 10.1016/j.prosdent.2021.02.010 [doi] 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. CI - Copyright © 2021 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved. FAU - Revilla-León, Marta AU - Revilla-León M AD - Assistant Professor and Assistant Program Director AEGD Residency, Department of Comprehensive Dentistry, College of Dentistry, Texas A&M University, Dallas, Texas; Affiliate Faculty Graduate Prosthodontics, Department of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, Wash; Researcher at Revilla Research Center, Madrid, Spain. FAU - Gómez-Polo, Miguel AU - Gómez-Polo M AD - Associate Professor, Department of Conservative Dentistry and Prosthodontics, School of Dentistry, Complutense University of Madrid, Madrid, Spain. Electronic address: mgomezpo@ucm.es. FAU - Vyas, Shantanu AU - Vyas S AD - Graduate Research Assistant, J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, Dallas, Texas. FAU - Barmak, Abdul Basir AU - Barmak AB AD - Assistant Professor Clinical Research and Biostatistics, Eastman Institute of Oral Health, University of Rochester Medical Center, Rochester, NY. FAU - Özcan, Mutlu AU - Özcan M AD - Professor and Head, Division of Dental Biomaterials, Clinic for Reconstructive Dentistry, Center for Dental and Oral Medicine, University of Zürich, Zürich, Switzerland. FAU - Att, Wael AU - Att W AD - Professor and Chair, Department of Prosthodontics, Tufts University School of Dental Medicine, Boston, Mass. FAU - Krishnamurthy, Vinayak R AU - Krishnamurthy VR AD - Assistant Professor, J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, College Station, Texas. LA - eng PT - Journal Article PT - Review PT - Systematic Review DEP - 20210409 PL - United States TA - J Prosthet Dent JT - The Journal of prosthetic dentistry JID - 0376364 SB - IM CIN - J Evid Based Dent Pract. 2023 Mar;23(1):101837. PMID: 36914305 MH - Humans MH - Dental Restoration, Permanent/methods MH - *Dental Caries/diagnosis/therapy MH - Artificial Intelligence MH - Dentistry MH - *Tooth Fractures EDAT- 2021/04/13 06:00 MHDA- 2022/12/07 06:00 CRDT- 2021/04/12 05:35 PHST- 2020/10/25 00:00 [received] PHST- 2021/02/03 00:00 [revised] PHST- 2021/02/04 00:00 [accepted] PHST- 2021/04/13 06:00 [pubmed] PHST- 2022/12/07 06:00 [medline] PHST- 2021/04/12 05:35 [entrez] AID - S0022-3913(21)00087-1 [pii] AID - 10.1016/j.prosdent.2021.02.010 [doi] PST - ppublish SO - J Prosthet Dent. 2022 Nov;128(5):867-875. doi: 10.1016/j.prosdent.2021.02.010. Epub 2021 Apr 9. PMID- 35399834 OWN - NLM STAT- MEDLINE DCOM- 20220412 LR - 20220516 IS - 2040-2309 (Electronic) IS - 2040-2295 (Print) IS - 2040-2295 (Linking) VI - 2022 DP - 2022 TI - Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries. PG - 5032435 LID - 10.1155/2022/5032435 [doi] LID - 5032435 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. CI - Copyright © 2022 Sarena Talpur et al. FAU - Talpur, Sarena AU - Talpur S AUID- ORCID: 0000-0001-6013-7621 AD - Department of Biomedical Engineering, Ziauddin University, Karachi, Pakistan. FAU - Azim, Fahad AU - Azim F AUID- ORCID: 0000-0001-6737-4953 AD - Department of Electrical Engineering, Ziauddin University, Karachi, Pakistan. FAU - Rashid, Munaf AU - Rashid M AUID- ORCID: 0000-0003-2063-4513 AD - Department of Software Engineering, Ziauddin University, Karachi, Pakistan. FAU - Syed, Sidra Abid AU - Syed SA AUID- ORCID: 0000-0002-0647-8270 AD - Department of Biomedical Engineering, Ziauddin University, Karachi, Pakistan. FAU - Talpur, Baby Alisha AU - Talpur BA AUID- ORCID: 0000-0002-4178-3748 AD - Liaquat University of Medical and Health Sciences, Jamshoro, Pakistan. FAU - Khan, Saad Jawaid AU - Khan SJ AUID- ORCID: 0000-0001-7351-2494 AD - Department of Biomedical Engineering, Ziauddin University, Karachi, Pakistan. LA - eng PT - Journal Article PT - Review PT - Systematic Review DEP - 20220331 PL - England TA - J Healthc Eng JT - Journal of healthcare engineering JID - 101528166 SB - IM MH - Algorithms MH - *Artificial Intelligence MH - Child MH - *Dental Caries/diagnosis MH - Female MH - Humans MH - Machine Learning MH - Neural Networks, Computer PMC - PMC8989613 COIS- The authors declare that they have no conflicts of interest regarding the publication of this paper. EDAT- 2022/04/12 06:00 MHDA- 2022/04/13 06:00 CRDT- 2022/04/11 05:19 PHST- 2021/10/07 00:00 [received] PHST- 2022/03/06 00:00 [revised] PHST- 2022/03/11 00:00 [accepted] PHST- 2022/04/11 05:19 [entrez] PHST- 2022/04/12 06:00 [pubmed] PHST- 2022/04/13 06:00 [medline] AID - 10.1155/2022/5032435 [doi] PST - epublish SO - J Healthc Eng. 2022 Mar 31;2022:5032435. doi: 10.1155/2022/5032435. eCollection 2022. PMID- 33172056 OWN - NLM STAT- PubMed-not-MEDLINE LR - 20201128 IS - 2077-0383 (Print) IS - 2077-0383 (Electronic) IS - 2077-0383 (Linking) VI - 9 IP - 11 DP - 2020 Nov 6 TI - Dental Caries Diagnosis and Detection Using Neural Networks: A Systematic Review. LID - 10.3390/jcm9113579 [doi] LID - 3579 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. FAU - Prados-Privado, María AU - Prados-Privado M AD - Asisa Dental, Research Department, C/José Abascal, 32, 28003 Madrid, Spain. AD - Department of Signal Theory and Communications, Higher Polytechnic School, Universidad de Alcala de Henares, Ctra, Madrid-Barcelona, Km. 33,600, 28805 Alcala de Henares, Spain. AD - IDIBO GROUP (Group of High-Performance Research, Development and Innovation in Dental Biomaterials of Rey Juan Carlos University), Avenida de Atenas s/n, 28922 Alcorcon, Spain. FAU - García Villalón, Javier AU - García Villalón J AD - Asisa Dental, Research Department, C/José Abascal, 32, 28003 Madrid, Spain. FAU - Martínez-Martínez, Carlos Hugo AU - Martínez-Martínez CH AD - Faculty of Medicine, Universidad Complutense de Madrid, Plaza de Ramón y Cajal, s/n, 28040 Madrid, Spain. FAU - Ivorra, Carlos AU - Ivorra C AD - Asisa Dental, Research Department, C/José Abascal, 32, 28003 Madrid, Spain. FAU - Prados-Frutos, Juan Carlos AU - Prados-Frutos JC AD - IDIBO GROUP (Group of High-Performance Research, Development and Innovation in Dental Biomaterials of Rey Juan Carlos University), Avenida de Atenas s/n, 28922 Alcorcon, Spain. AD - Department of Medical Specialties and Public Health, Faculty of Health Sciences, Universidad Rey Juan Carlos, Avenida de Atenas, 28922 Alcorcon, Spain. LA - eng GR - -/Asisa Dental/ PT - Journal Article PT - Review DEP - 20201106 PL - Switzerland TA - J Clin Med JT - Journal of clinical medicine JID - 101606588 PMC - PMC7694692 OTO - NOTNLM OT - artificial intelligence OT - caries OT - detection OT - images COIS- The authors declare no conflict of interest. EDAT- 2020/11/12 06:00 MHDA- 2020/11/12 06:01 CRDT- 2020/11/11 01:02 PHST- 2020/09/29 00:00 [received] PHST- 2020/10/30 00:00 [revised] PHST- 2020/11/03 00:00 [accepted] PHST- 2020/11/11 01:02 [entrez] PHST- 2020/11/12 06:00 [pubmed] PHST- 2020/11/12 06:01 [medline] AID - jcm9113579 [pii] AID - jcm-09-03579 [pii] AID - 10.3390/jcm9113579 [doi] PST - epublish SO - J Clin Med. 2020 Nov 6;9(11):3579. doi: 10.3390/jcm9113579. PMID- 36494110 OWN - NLM STAT- MEDLINE DCOM- 20221216 LR - 20221221 IS - 1532-3390 (Electronic) IS - 1532-3382 (Linking) VI - 22 IP - 4 DP - 2022 Dec TI - DEEP LEARNING ALGORITHMS SHOW SOME POTENTIAL AS AN ADJUNCTIVE TOOL IN CARIES DIAGNOSIS. PG - 101772 LID - S1532-3382(22)00093-8 [pii] LID - 10.1016/j.jebdp.2022.101772 [doi] 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. CI - Copyright © 2022 Elsevier Inc. All rights reserved. FAU - Hegde, Shwetha AU - Hegde S FAU - Gao, Jinlong AU - Gao J LA - eng PT - Journal Article PT - Systematic Review DEP - 20220811 PL - United States TA - J Evid Based Dent Pract JT - The journal of evidence-based dental practice JID - 101083101 SB - IM MH - Humans MH - *Deep Learning MH - *Dental Caries/diagnosis MH - Dental Care MH - Algorithms OTO - NOTNLM OT - Accuracy OT - Deep learning algorithms OT - Dental caries OT - Dental images OT - Diagnosis OT - Sensitivity OT - Specificity EDAT- 2022/12/10 06:00 MHDA- 2022/12/15 06:00 CRDT- 2022/12/09 21:02 PHST- 2022/12/09 21:02 [entrez] PHST- 2022/12/10 06:00 [pubmed] PHST- 2022/12/15 06:00 [medline] AID - S1532-3382(22)00093-8 [pii] AID - 10.1016/j.jebdp.2022.101772 [doi] PST - ppublish SO - J Evid Based Dent Pract. 2022 Dec;22(4):101772. doi: 10.1016/j.jebdp.2022.101772. Epub 2022 Aug 11. PMID- 35636386 OWN - NLM STAT- MEDLINE DCOM- 20221107 LR - 20221119 IS - 1421-976X (Electronic) IS - 0008-6568 (Linking) VI - 56 IP - 3 DP - 2022 TI - Machine Learning in the Diagnosis and Prognostic Prediction of Dental Caries: A Systematic Review. PG - 161-170 LID - 10.1159/000524167 [doi] 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. CI - © 2022 S. Karger AG, Basel. FAU - Reyes, Lilian Toledo AU - Reyes LT AD - Department of Stomatology, School of Dentistry, Federal University of Santa Maria, Santa Maria, Brazil, liliant.reyes@gmail.com. FAU - Knorst, Jessica Klöckner AU - Knorst JK AD - Department of Stomatology, School of Dentistry, Federal University of Santa Maria, Santa Maria, Brazil. FAU - Ortiz, Fernanda Ruffo AU - Ortiz FR AD - Department of Stomatology, School of Dentistry, Federal University of Santa Maria, Santa Maria, Brazil. FAU - Ardenghi, Thiago Machado AU - Ardenghi TM AD - Department of Stomatology, School of Dentistry, Federal University of Santa Maria, Santa Maria, Brazil. LA - eng PT - Research Support, Non-U.S. Gov't PT - Systematic Review DEP - 20220530 PL - Switzerland TA - Caries Res JT - Caries research JID - 0103374 SB - IM MH - Humans MH - Prognosis MH - *Dental Caries/diagnosis MH - Cross-Sectional Studies MH - Machine Learning MH - Algorithms OTO - NOTNLM OT - Artificial intelligence OT - Dental caries OT - Diagnosis OT - Machine learning OT - Prognosis EDAT- 2022/06/01 06:00 MHDA- 2022/11/08 06:00 CRDT- 2022/05/31 13:20 PHST- 2021/10/14 00:00 [received] PHST- 2022/03/13 00:00 [accepted] PHST- 2022/06/01 06:00 [pubmed] PHST- 2022/11/08 06:00 [medline] PHST- 2022/05/31 13:20 [entrez] AID - 000524167 [pii] AID - 10.1159/000524167 [doi] PST - ppublish SO - Caries Res. 2022;56(3):161-170. doi: 10.1159/000524167. Epub 2022 May 30. PMID- 36246115 OWN - NLM STAT- PubMed-not-MEDLINE LR - 20221019 IS - 1664-042X (Print) IS - 1664-042X (Electronic) IS - 1664-042X (Linking) VI - 13 DP - 2022 TI - Deep learning techniques for cancer classification using microarray gene expression data. PG - 952709 LID - 10.3389/fphys.2022.952709 [doi] LID - 952709 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. CI - Copyright © 2022 Gupta, Gupta, Shabaz and Sharma. FAU - Gupta, Surbhi AU - Gupta S AD - Department of Computer Science and Engineering Department, SMVDU, Jammu, India. AD - Model Institute of Engineering and Technology, Jammu, India. FAU - Gupta, Manoj K AU - Gupta MK AD - Department of Computer Science and Engineering Department, SMVDU, Jammu, India. FAU - Shabaz, Mohammad AU - Shabaz M AD - Model Institute of Engineering and Technology, Jammu, India. FAU - Sharma, Ashutosh AU - Sharma A AD - School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India. LA - eng PT - Systematic Review DEP - 20220930 PL - Switzerland TA - Front Physiol JT - Frontiers in physiology JID - 101549006 PMC - PMC9563992 OTO - NOTNLM OT - Rna-sequences OT - artificial intelligence OT - cancer OT - deep learning OT - gene expression COIS- The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. EDAT- 2022/10/18 06:00 MHDA- 2022/10/18 06:01 CRDT- 2022/10/17 04:26 PHST- 2022/05/25 00:00 [received] PHST- 2022/09/01 00:00 [accepted] PHST- 2022/10/17 04:26 [entrez] PHST- 2022/10/18 06:00 [pubmed] PHST- 2022/10/18 06:01 [medline] AID - 952709 [pii] AID - 10.3389/fphys.2022.952709 [doi] PST - epublish SO - Front Physiol. 2022 Sep 30;13:952709. doi: 10.3389/fphys.2022.952709. eCollection 2022. PMID- 37665008 OWN - NLM STAT- MEDLINE DCOM- 20231002 LR - 20231007 IS - 0250-832X (Print) IS - 1476-542X (Electronic) IS - 0250-832X (Linking) VI - 52 IP - 7 DP - 2023 Oct TI - Applications of artificial intelligence in the analysis of dental panoramic radiographs: an overview of systematic reviews. PG - 20230284 LID - 10.1259/dmfr.20230284 [doi] LID - 20230284 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. PROSPERO registration number: CRD42023416048. FAU - Turosz, Natalia AU - Turosz N AUID- ORCID: 0000-0001-8075-9989 AD - Institute of Public Health, Jagiellonian University Medical College, Skawińska, Poland. FAU - Chęcińska, Kamila AU - Chęcińska K AD - Department of Glass Technology and Amorphous Coatings, Faculty of Materials Science and Ceramics, AGH University of Science and Technology, Mickiewicza, Poland. FAU - Chęciński, Maciej AU - Chęciński M AD - Department of Oral Surgery, Preventive Medicine Center, Komorowskiego, Poland. FAU - Brzozowska, Anita AU - Brzozowska A AD - Preventive Medicine Center, Komorowskiego, Poland. FAU - Nowak, Zuzanna AU - Nowak Z AD - Department of Temporomandibular Disorders, Medical University of Silesia in Katowice, Katowice, Poland. FAU - Sikora, Maciej AU - Sikora M AD - Department of Maxillofacial Surgery, Hospital of the Ministry of Interior, Wojska Polskiego, Poland. AD - Department of Biochemistry and Medical Chemistr, Pomeranian Medical University, Powstańców Wielkopolskich, Poland. LA - eng PT - Journal Article PT - Review DEP - 20230904 PL - England TA - Dentomaxillofac Radiol JT - Dento maxillo facial radiology JID - 7609576 MH - Humans MH - *Alveolar Bone Loss MH - Artificial Intelligence MH - *Dental Caries MH - Radiography, Panoramic MH - Systematic Reviews as Topic PMC - PMC10552133 OTO - NOTNLM OT - Artificial Intelligence OT - Deep learning OT - Dental radiography OT - Overview of reviews OT - Panoramic radiographs EDAT- 2023/09/04 12:42 MHDA- 2023/09/26 13:42 PMCR- 2024/10/01 CRDT- 2023/09/04 06:42 PHST- 2024/10/01 00:00 [pmc-release] PHST- 2023/09/26 13:42 [medline] PHST- 2023/09/04 12:42 [pubmed] PHST- 2023/09/04 06:42 [entrez] AID - 10.1259/dmfr.20230284 [doi] PST - ppublish SO - Dentomaxillofac Radiol. 2023 Oct;52(7):20230284. doi: 10.1259/dmfr.20230284. Epub 2023 Sep 4. PMID- 28843958 OWN - NLM STAT- MEDLINE DCOM- 20181211 LR - 20220410 IS - 1878-7452 (Electronic) IS - 1878-7452 (Linking) VI - 75 IP - 2 DP - 2018 Mar-Apr TI - Avoiding Surgical Skill Decay: A Systematic Review on the Spacing of Training Sessions. PG - 471-480 LID - S1931-7204(17)30044-2 [pii] LID - 10.1016/j.jsurg.2017.08.002 [doi] AB - OBJECTIVE: Spreading training sessions over time instead of training in just 1 session leads to an improvement of long-term retention for factual knowledge. However, it is not clear whether this would also apply to surgical skills. Thus, we performed a systematic review to find out whether spacing training sessions would also improve long-term retention of surgical skills. DESIGN: We searched the Medline, PsycINFO, Embase, Eric, and Web of Science online databases. We only included articles that were randomized trials with a sample of medical trainees acquiring surgical motor skills in which the spacing effect was reported. The quality and bias of the articles were assessed using the Cochrane Collaboration's risk of bias assessment tool. RESULTS: With respect to the spacing effect, 1955 articles were retrieved. After removing duplicates and articles that did not meet the inclusion criteria, 11 articles remained. The overall quality of the experiments was "moderate." Trainees in the spaced condition scored higher in a retention test than students in the massed condition. CONCLUSIONS: Our systematic review showed evidence that spacing training sessions improves long-term surgical skills retention when compared to massed practice. However, the optimal gap between the re-study sessions is unclear. CI - Copyright © 2017 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved. FAU - Cecilio-Fernandes, Dario AU - Cecilio-Fernandes D AD - Center for Education Development and Research in Health Professions (CEDAR), University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. Electronic address: d.cecilio.fernandes@umcg.nl. FAU - Cnossen, Fokie AU - Cnossen F AD - Institute of Artificial Intelligence and Cognitive Engineering, University of Groningen, Groningen, The Netherlands. FAU - Jaarsma, Debbie A D C AU - Jaarsma DADC AD - Center for Education Development and Research in Health Professions (CEDAR), University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. FAU - Tio, René A AU - Tio RA AD - Center for Education Development and Research in Health Professions (CEDAR), University Medical Center Groningen, University of Groningen, Groningen, The Netherlands; Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. LA - eng PT - Journal Article PT - Research Support, Non-U.S. Gov't PT - Systematic Review DEP - 20170824 PL - United States TA - J Surg Educ JT - Journal of surgical education JID - 101303204 SB - IM MH - *Clinical Competence MH - Education, Medical, Graduate/*methods MH - Evidence-Based Medicine MH - Female MH - General Surgery/*education MH - Humans MH - Internship and Residency/*methods MH - Male MH - Netherlands MH - Randomized Controlled Trials as Topic MH - Retention, Psychology/physiology MH - Simulation Training/*methods MH - Time Factors OTO - NOTNLM OT - Medical Knowledge OT - Practice-Based Learning and Improvement OT - distributed practice OT - long-term retention OT - medical education OT - simulation training OT - spacing effect OT - surgical skills EDAT- 2017/08/28 06:00 MHDA- 2018/12/12 06:00 CRDT- 2017/08/28 06:00 PHST- 2017/02/10 00:00 [received] PHST- 2017/07/11 00:00 [revised] PHST- 2017/08/05 00:00 [accepted] PHST- 2017/08/28 06:00 [pubmed] PHST- 2018/12/12 06:00 [medline] PHST- 2017/08/28 06:00 [entrez] AID - S1931-7204(17)30044-2 [pii] AID - 10.1016/j.jsurg.2017.08.002 [doi] PST - ppublish SO - J Surg Educ. 2018 Mar-Apr;75(2):471-480. doi: 10.1016/j.jsurg.2017.08.002. Epub 2017 Aug 24. PMID- 33720395 OWN - NLM STAT- MEDLINE DCOM- 20210421 LR - 20220716 IS - 1469-493X (Electronic) IS - 1361-6137 (Linking) VI - 3 IP - 3 DP - 2021 Mar 15 TI - Imaging modalities to inform the detection and diagnosis of early caries. PG - CD014545 LID - 10.1002/14651858.CD014545 [doi] LID - CD014545 AB - BACKGROUND: The detection and diagnosis of caries at the earliest opportunity is fundamental to the preservation of tooth tissue and maintenance of oral health. Radiographs have traditionally been used to supplement the conventional visual-tactile clinical examination. Accurate, timely detection and diagnosis of early signs of disease could afford patients the opportunity of less invasive treatment with less destruction of tooth tissue, reduce the need for treatment with aerosol-generating procedures, and potentially result in a reduced cost of care to the patient and to healthcare services. OBJECTIVES: To determine the diagnostic accuracy of different dental imaging methods to inform the detection and diagnosis of non-cavitated enamel only coronal dental caries. SEARCH METHODS: Cochrane Oral Health's Information Specialist undertook a search of the following databases: MEDLINE Ovid (1946 to 31 December 2018); Embase Ovid (1980 to 31 December 2018); US National Institutes of Health Ongoing Trials Register (ClinicalTrials.gov, to 31 December 2018); and the World Health Organization International Clinical Trials Registry Platform (to 31 December 2018). We studied reference lists as well as published systematic review articles. SELECTION CRITERIA: We included diagnostic accuracy study designs that compared a dental imaging method with a reference standard (histology, excavation, enhanced visual examination), studies that evaluated the diagnostic accuracy of single index tests, and studies that directly compared two or more index tests. Studies reporting at both the patient or tooth surface level were included. In vitro and in vivo studies were eligible for inclusion. Studies that explicitly recruited participants with more advanced lesions that were obviously into dentine or frankly cavitated were excluded. We also excluded studies that artificially created carious lesions and those that used an index test during the excavation of dental caries to ascertain the optimum depth of excavation. DATA COLLECTION AND ANALYSIS: Two review authors extracted data independently and in duplicate using a standardised data extraction form and quality assessment based on QUADAS-2 specific to the clinical context. Estimates of diagnostic accuracy were determined using the bivariate hierarchical method to produce summary points of sensitivity and specificity with 95% confidence regions. Comparative accuracy of different radiograph methods was conducted based on indirect and direct comparisons between methods. Potential sources of heterogeneity were pre-specified and explored visually and more formally through meta-regression. MAIN RESULTS: We included 104 datasets from 77 studies reporting a total of 15,518 tooth sites or surfaces. The most frequently reported imaging methods were analogue radiographs (55 datasets from 51 studies) and digital radiographs (42 datasets from 40 studies) followed by cone beam computed tomography (CBCT) (7 datasets from 7 studies). Only 17 studies were of an in vivo study design, carried out in a clinical setting. No studies were considered to be at low risk of bias across all four domains but 16 studies were judged to have low concern for applicability across all domains. The patient selection domain had the largest number of studies judged to be at high risk of bias (43 studies); the index test, reference standard, and flow and timing domains were judged to be at high risk of bias in 30, 12, and 7 studies respectively. Studies were synthesised using a hierarchical bivariate method for meta-analysis. There was substantial variability in the results of the individual studies, with sensitivities that ranged from 0 to 0.96 and specificities from 0 to 1.00. For all imaging methods the estimated summary sensitivity and specificity point was 0.47 (95% confidence interval (CI) 0.40 to 0.53) and 0.88 (95% CI 0.84 to 0.92), respectively. In a cohort of 1000 tooth surfaces with a prevalence of enamel caries of 63%, this would result in 337 tooth surfaces being classified as disease free when enamel caries was truly present (false negatives), and 43 tooth surfaces being classified as diseased in the absence of enamel caries (false positives). Meta-regression indicated that measures of accuracy differed according to the imaging method (Chi(2)(4) = 32.44, P < 0.001), with the highest sensitivity observed for CBCT, and the highest specificity observed for analogue radiographs. None of the specified potential sources of heterogeneity were able to explain the variability in results. No studies included restored teeth in their sample or reported the inclusion of sealants. We rated the certainty of the evidence as low for sensitivity and specificity and downgraded two levels in total for risk of bias due to limitations in the design and conduct of the included studies, indirectness arising from the in vitro studies, and the observed inconsistency of the results. AUTHORS' CONCLUSIONS: The design and conduct of studies to determine the diagnostic accuracy of methods to detect and diagnose caries in situ are particularly challenging. Low-certainty evidence suggests that imaging for the detection or diagnosis of early caries may have poor sensitivity but acceptable specificity, resulting in a relatively high number of false-negative results with the potential for early disease to progress. If left untreated, the opportunity to provide professional or self-care practices to arrest or reverse early caries lesions will be missed. The specificity of lesion detection is however relatively high, and one could argue that initiation of non-invasive management (such as the use of topical fluoride), is probably of low risk. CBCT showed superior sensitivity to analogue or digital radiographs but has very limited applicability to the general dental practitioner. However, given the high-radiation dose, and potential for caries-like artefacts from existing restorations, its use cannot be justified in routine caries detection. Nonetheless, if early incidental carious lesions are detected in CBCT scans taken for other purposes, these should be reported. CBCT has the potential to be used as a reference standard in diagnostic studies of this type. Despite the robust methodology applied in this comprehensive review, the results should be interpreted with some caution due to shortcomings in the design and execution of many of the included studies. Future research should evaluate the comparative accuracy of different methods, be undertaken in a clinical setting, and focus on minimising bias arising from the use of imperfect reference standards in clinical studies. CI - Copyright © 2021 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd. FAU - Walsh, Tanya AU - Walsh T AD - Division of Dentistry, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK. FAU - Macey, Richard AU - Macey R AD - Division of Dentistry, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK. FAU - Riley, Philip AU - Riley P AD - Cochrane Oral Health, Division of Dentistry, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK. FAU - Glenny, Anne-Marie AU - Glenny AM AD - Division of Dentistry, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK. FAU - Schwendicke, Falk AU - Schwendicke F AD - Department of Oral Diagnostics, Digital Health and Heatlh Research Services, Charité - Universitätsmedizin Berlin, Berlin, Germany. FAU - Worthington, Helen V AU - Worthington HV AD - Cochrane Oral Health, Division of Dentistry, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK. FAU - Clarkson, Janet E AU - Clarkson JE AD - Division of Oral Health Sciences, Dundee Dental School, University of Dundee, Dundee, UK. FAU - Ricketts, David AU - Ricketts D AD - Dundee Dental School, University of Dundee, Dundee, UK. FAU - Su, Ting-Li AU - Su TL AD - Division of Dentistry, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK. FAU - Sengupta, Anita AU - Sengupta A AD - Division of Dentistry, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK. LA - eng PT - Journal Article PT - Meta-Analysis PT - Research Support, Non-U.S. Gov't PT - Systematic Review DEP - 20210315 PL - England TA - Cochrane Database Syst Rev JT - The Cochrane database of systematic reviews JID - 100909747 SB - IM MH - Adult MH - Bias MH - Child MH - *Cone-Beam Computed Tomography/statistics & numerical data MH - *Datasets as Topic MH - Dental Caries/*diagnostic imaging MH - Dentition, Permanent MH - False Negative Reactions MH - False Positive Reactions MH - Humans MH - Radiography, Dental/*methods/statistics & numerical data MH - Radiography, Dental, Digital/statistics & numerical data MH - Reference Standards MH - Sensitivity and Specificity MH - Tooth, Deciduous PMC - PMC8441255 COIS- Tanya Walsh: none known. I am Statistical Editor with Cochrane Oral Health.
Richard Macey: none known.
Philip Riley: none known. I am Deputy Co‐ordinating Editor of Cochrane Oral Health.
Anne‐Marie Glenny: none known. I am Joint Co‐ordinating Editor of Cochrane Oral Health.
Falk Schwendicke: I have a conflict of interest when it comes to artificial intelligence (AI)‐based diagnostics. This, however, is beyond the remit of this review.
Helen V Worthington: none know. I am Emeritus Co‐ordinating Editor of Cochrane Oral Health.
Janet E Clarkson: none known. I am Joint Co‐ordinating Editor of Cochrane Oral Health.
David Ricketts: none known.
Ting‐Li Su: none known.
Anita Sengupta: none known. EDAT- 2021/03/16 06:00 MHDA- 2021/04/22 06:00 CRDT- 2021/03/15 13:18 PHST- 2021/03/15 13:18 [entrez] PHST- 2021/03/16 06:00 [pubmed] PHST- 2021/04/22 06:00 [medline] AID - CD014545 [pii] AID - 10.1002/14651858.CD014545 [doi] PST - epublish SO - Cochrane Database Syst Rev. 2021 Mar 15;3(3):CD014545. doi: 10.1002/14651858.CD014545.