FN Clarivate Analytics Web of Science
VR 1.0
PT J
AU Mohammad-Rahimi, Hossein
   Motamedian, Saeed Reza
   Rohban, Mohammad Hossein
   Krois, Joachim
   Uribe, Sergio E.
   Mahmoudinia, Erfan
   Rokhshad, Rata
   Nadimi, Mohadeseh
   Schwendicke, Falk
TI Deep learning for caries detection: A systematic review
SO JOURNAL OF DENTISTRY
VL 122
AR 104115
DI 10.1016/j.jdent.2022.104115
EA MAY 2022
DT Review
PD JUL 2022
PY 2022
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.
RI Motamedian, Saeed Reza/V-3294-2018; Uribe, Sergio E./C-9579-2011; Krois, Joachim/; Rokhshad, Rata/; Schwendicke, Falk/; Mohammad-Rahimi, Hossein/
OI Motamedian, Saeed Reza/0000-0001-7337-2145; Uribe, Sergio
   E./0000-0003-0684-2025; Krois, Joachim/0000-0002-6010-8940; Rokhshad,
   Rata/0000-0001-9668-7684; Schwendicke, Falk/0000-0003-1223-1669;
   Mohammad-Rahimi, Hossein/0000-0002-4971-5926
Z8 1
ZR 0
ZA 0
ZB 5
TC 36
ZS 0
Z9 37
C1 ITU WHO Focus Grp Hlth, Top Grp Dent Diagnost & Digital Dent, Berlin,
   Germany
C1 Sharif Univ Technol, Dept Comp Engn, Tehran, Iran
C1 Shahid Beheshti Univ Med Sci, Res Inst Dent Sci & Dept Orthodont,
   Dentofacial Deform Res Ctr, Sch Dent, Tehran, Iran
C1 Charite Univ Med Berlin, Dept Oral Diagnost, Digital Hlth & Hlth Serv
   Res, Berlin, Germany
C1 Riga Stradins Univ, Dept Conservat Dent & Oral Hlth, Riga, Latvia
C1 Riga Stradins Univ, Bioinformat Res Unit, Riga, Latvia
C1 Univ Austral Chile, Sch Dent, Valdivia, Chile
C1 Headquarters Riga Tech Univ, Balt Biomat Ctr Excellence, Riga, Latvia
C1 Guilan Univ Med Sci, Heshmat Hosp, Cardiovasc Dis Res Ctr, Sch Med,
   Rasht, Iran
C3 ITU WHO Focus Grp Hlth
C3 Headquarters Riga Tech Univ
C3 Guilan Univ Med Sci
SN 0300-5712
EI 1879-176X
DA 2022-06-14
UT WOS:000804944200002
PM 35367318
ER

PT J
AU Al-Namankany, Abeer
TI Influence of Artificial Intelligence-Driven Diagnostic Tools on
   Treatment Decision-Making in Early Childhood Caries: A Systematic Review
   of Accuracy and Clinical Outcomes.
SO Dentistry journal
VL 11
IS 9
DI 10.3390/dj11090214
DT Journal Article; Review
PD 2023 Sep 12
PY 2023
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.
RI Al-Namankany, Abeer/AHB-1583-2022
OI Al-Namankany, Abeer/0000-0001-9335-5940
ZS 0
Z8 0
ZA 0
ZB 0
TC 0
ZR 0
Z9 0
C1 Paediatric Dentistry and Orthodontics Department, College of Dentistry,
   Taibah University, P.O. Box 41141, Almadinah Almunawwarah 38008, Saudi
   Arabia.
EI 2304-6767
DA 2023-09-28
UT MEDLINE:37754334
PM 37754334
ER

PT J
AU Talpur, Sarena
   Azim, Fahad
   Rashid, Munaf
   Syed, Sidra Abid
   Talpur, Baby Alisha
   Khan, Saad Jawaid
TI Uses of Different Machine Learning Algorithms for Diagnosis of Dental
   Caries
SO JOURNAL OF HEALTHCARE ENGINEERING
VL 2022
AR 5032435
DI 10.1155/2022/5032435
DT Review
PD MAR 31 2022
PY 2022
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.
OI Azim, Choudhry Fahad/0000-0001-6737-4953; Rashid, Sheikh Muhammad
   Munaf/0000-0003-2063-4513; Syed, Sidra Abid/0000-0002-0647-8270
TC 6
Z8 0
ZS 0
ZA 0
ZB 1
ZR 0
Z9 7
C1 Ziauddin Univ, Dept Biomed Engn, Karachi, Pakistan
C1 Ziauddin Univ, Dept Elect Engn, Karachi, Pakistan
C1 Ziauddin Univ, Dept Software Engn, Karachi, Pakistan
C1 Liaquat Univ Med & Hlth Sci, Jamshoro, Pakistan
SN 2040-2295
EI 2040-2309
DA 2022-05-27
UT WOS:000793539700012
PM 35399834
ER

PT B
AU Shindé, Manila
Z2  
TI Evaluation of Performance of Deep Learning Algorithms in Detecting and
   Diagnosing Dental Carious Lesions Using Intraoral Radiographic Imaging:
   A Systematic Review and Meta-Analysis
DT Dissertation/Thesis
PD Jan 01 2022
PY 2022
ZA 0
TC 0
ZR 0
ZS 0
Z8 0
ZB 0
Z9 0
C1 The University of Iowa, Oral Science, Iowa, United States
C3 The University of Iowa
BN 9798845425768
UT PQDT:68523276
ER

PT J
AU Prados-Privado, Maria
   Garcia Villalon, Javier
   Martinez-Martinez, Carlos Hugo
   Ivorra, Carlos
   Prados-Frutos, Juan Carlos
TI Dental Caries Diagnosis and Detection Using Neural Networks: A
   Systematic Review
SO JOURNAL OF CLINICAL MEDICINE
VL 9
IS 11
AR 3579
DI 10.3390/jcm9113579
DT Review
PD NOV 2020
PY 2020
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.
OI Prados-Privado, Maria/0000-0003-0833-0680; Prados Frutos, Juan
   Carlos/0000-0003-1464-0968
Z8 1
ZR 0
ZB 5
ZS 0
TC 34
ZA 0
Z9 36
C1 Asisa Dent, Res Dept, C Jose Abascal,32, Madrid 28003, Spain
C1 Univ Alcala, Dept Signal Theory & Commun, Higher Polytech Sch, Km
   33,600, Alcala De Henares 28805, Spain
C1 Rey Juan Carlos Univ, IDIBO Grp, Grp High Performance Res Dev & Innovat
   Dent Bioma, Ave Atenas S-N, Alcorcon 28922, Spain
C1 Univ Complutense Madrid, Fac Med, Plaza Ramon & Cajal,S-N, Madrid 28040,
   Spain
C1 Univ Rey Juan Carlos, Fac Hlth Sci, Dept Med Specialties & Publ Hlth,
   Ave Atenas, Alcorcon 28922, Spain
C3 Asisa Dent
EI 2077-0383
DA 2020-12-10
UT WOS:000593158900001
PM 33172056
ER

PT J
AU Forouzeshfar, Parsa
   Safaei, Ali A.
   Ghaderi, Foad
   Kamangar, Sedighesadat Hashemi
   Kaviani, Hanieh
   Haghi, Sahebeh
TI Dental caries diagnosis using neural networks and deep learning: a
   systematic review
SO MULTIMEDIA TOOLS AND APPLICATIONS
DI 10.1007/s11042-023-16599-w
EA SEP 2023
DT Article; Early Access
PY 2023
AB Dental caries is one of the oral health problems and the most common
   chronic infectious disease of childhood, and neural networks and
   artificial intelligence are increasingly being used in the field of
   dentistry. This review study aims to review studies published in the
   field of artificial intelligence and neural networks and dentistry. A
   search for studies in four databases, including Springer, ScienceDirect,
   PubMed (MedLine), and Institute of Electrical and Electronics Engineers
   (IEEE) was done. Finally, 28 studies were reviewed, most of which used
   Bitewing and Periapical images for the classification and detection of
   dental caries. The image databases ranged from 55 to 3000 and several
   evaluation metrics were used in the selected studies. The research
   questions were designed and reviewed based on PICOS (P stands for
   patient or problem, I stands for intervention, C stands for control or
   comparison, and O stands for outcomes). The majority of the studies also
   used pre-processing and data augmentation methods. The diversity between
   the networks used and the output evaluation criteria have made direct
   research comparisons challenging. The main focus of this research was on
   caries detection using deep learning methods and neural networks,
   especially convolutional neural networks that are suitable for images.
   The traditional methods of detecting caries, other than the methods
   based on artificial intelligence, have not been investigated in this
   research. Also, the main caries were interproximal and proximal caries
   in molars and premolars. The main difference between this and previous
   works is the use of more up-to-date articles (2016 to 2023) studies with
   an organized manner of reviewing, which is based on the types of images
   used.
OI Safaei, Ali Asghar/0000-0003-1985-8720
ZR 0
Z8 0
ZB 0
ZA 0
ZS 0
TC 0
Z9 0
C1 Tarbiat Modares Univ, Fac Math Sci, Dept Data Sci, Tehran, Iran
C1 Tarbiat Modares Univ, Fac Med Sci, Dept Med Informat, Tehran, Iran
C1 Tarbiat Modares Univ, Fac Interdisciplinary Sci & Technol, Dept Data
   Sci, Tehran, Iran
C1 Tarbiat Modares Univ, Elect & Comp Engn Dept, Human Comp Interact Lab,
   Tehran, Iran
C1 Univ Tehran Med Sci, Dent Sch, Restorat Dept, Tehran, Iran
C1 Univ Tehran Med Sci, Sch Dent, Dept Oral & Maxillofacial Radiol, Tehran,
   Iran
C1 Univ Tehran Med Sci, Dent Sch, Dept Operat Dent, Tehran, Iran
SN 1380-7501
EI 1573-7721
DA 2023-09-25
UT WOS:001060763900013
ER

PT J
AU Butcher, Mark C.
   Short, Bryn
   Veena, Chandra Lekha Ramalingam
   Bradshaw, Dave
   Pratten, Jonathan R.
   McLean, William
   Shaban, Suror Mohamad Ahmad
   Ramage, Gordon
   Delaney, Christopher
TI Meta-analysis of caries microbiome studies can improve upon disease
   prediction outcomes
SO APMIS
VL 130
IS 12
BP 763
EP 777
DI 10.1111/apm.13272
EA SEP 2022
DT Article
PD DEC 2022
PY 2022
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.
RI Ramage, Gordon/JSK-1399-2023; Ramage, Gordon/AAX-1315-2020; Ramalingam Veena, Chandra Lekha/
OI Ramage, Gordon/0000-0002-0932-3514; Ramalingam Veena, Chandra
   Lekha/0000-0003-0487-9991
TC 3
ZR 0
Z8 0
ZA 0
ZB 1
ZS 0
Z9 3
C1 Univ Glasgow, Coll Med Vet & Life Sci, Oral Sci Res Grp, Glasgow Dent
   Sch,Sch Med Dent & Nursing, Glasgow, Lanark, Scotland
C1 Haleon, R&D Innovat, Weybridge, Surrey, England
C3 Haleon
SN 0903-4641
EI 1600-0463
DA 2022-09-25
UT WOS:000855746800001
PM 36050830
ER

PT J
AU Moharrami, Mohammad
   Farmer, Julie
   Singhal, Sonica
   Watson, Erin
   Glogauer, Michael
   Johnson, Alistair E. W.
   Schwendicke, Falk
   Quinonez, Carlos
TI Detecting dental caries on oral photographs using artificial
   intelligence: A systematic review
SO ORAL DISEASES
DI 10.1111/odi.14659
EA JUL 2023
DT Review; Early Access
PY 2023
AB ObjectivesThis systematic review aimed at evaluating the performance of
   artificial intelligence (AI) models in detecting dental caries on oral
   photographs. MethodsMethodological 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.
   ResultsOut 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. ConclusionAutomatic 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.
RI Farmer, Julie/AAJ-8013-2021; Watson, Erin/
OI Farmer, Julie/0000-0001-5361-2180; Watson, Erin/0000-0002-2096-7732
ZR 0
Z8 0
ZS 0
ZA 0
ZB 0
TC 2
Z9 2
C1 Univ Toronto, Fac Dent, Toronto, ON, Canada
C1 ITU WHO Focus Grp Hlth, Top Grp Dent Diagnost & Digital Dent, Geneva,
   Switzerland
C1 Publ Hlth Ontario, Hlth Promot Chron Dis & Injury Prevent Dept, Toronto,
   ON, Canada
C1 Princess Margaret Canc Ctr, Dept Dent Oncol, Toronto, ON, Canada
C1 Mt Sinai Hosp, Ctr Adv Dent Res & Care, Dept Dent, Toronto, ON, Canada
C1 Charite Univ Med Berlin, Oral Diagnost Digital Hlth & Hlth Serv Res,
   Berlin, Germany
C1 Hosp Sick Children, Program Child Hlth Evaluat Sci, Toronto, ON, Canada
C1 Western Univ, Schulich Sch Med & Dent, London, ON, Canada
C1 Univ Toronto, Fac Dent, 124 Edward St, Toronto, ON M5G 1X5, Canada
C3 ITU WHO Focus Grp Hlth
C3 Publ Hlth Ontario
SN 1354-523X
EI 1601-0825
DA 2023-07-08
UT WOS:001017636400001
PM 37392423
ER

PT J
AU Khanagar, Sanjeev B.
   Al-ehaideb, Ali
   Maganur, Prabhadevi C.
   Vishwanathaiah, Satish
   Patil, Shankargouda
   Baeshen, Hosam A.
   Sarode, Sachin C.
   Bhandi, Shilpa
TI Developments, application, and performance of artificial intelligence in
   dentistry - A systematic review
SO JOURNAL OF DENTAL SCIENCES
VL 16
IS 1
BP 508
EP 522
DI 10.1016/j.jds.2020.06.019
DT Review
PD JAN 2021
PY 2021
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 perform-ing 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 2000eMarch 15, 2020).After applying inclusion and exclusion
   criteria, 43 articles were read in full and critically analyzed. Quality
   analysis was performed using QUA-DAS-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 net-works (CNNs) and artificial
   neural networks (ANNs). These AI models have been used in detec-tion and
   diagnosis of dental caries, vertical root fractures, apical lesions,
   salivary gland diseases, maxillary sinusitis, maxillofacial cysts,
   cervical lymph nodes metastasis, osteopo-rosis, cancerous lesions,
   alveolar bone loss, predicting orthodontic extractions, need for
   or-thodontic 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 per-formance
   and accuracy. (C) 2020 Association for Dental Sciences of the Republic
   of China. Publishing services by Else-vier B.V.
RI Vishwanathaiah, Satish/AAD-5770-2021; Sarode, Sachin/I-5048-2014; Patil, shankargouda/GWC-7516-2022; Patil, Shankargouda/G-5256-2013; KHANAGAR, SANJEEV/
OI Vishwanathaiah, Satish/0000-0002-8376-297X; Sarode,
   Sachin/0000-0003-1856-0957; Patil, shankargouda/0000-0001-7246-5497;
   KHANAGAR, SANJEEV/0000-0002-4098-7773
ZS 1
ZA 0
ZR 0
TC 132
ZB 14
Z8 6
Z9 141
C1 King Saud Bin Abdulaziz Univ Hlth Sci, Coll Dent, Prevent Dent Sci Dept,
   Riyadh, Saudi Arabia
C1 King Abdullah Int Med Res Ctr, Riyadh, Saudi Arabia
C1 Minist Natl Guard Hlth Affairs, King Abdulaziz Med City, Dent Serv,
   Riyadh, Saudi Arabia
C1 Jazan Univ, Dept Prevent Dent Sci, Div Pedodont, Coll Dent, Jazan, Saudi
   Arabia
C1 Jazan Univ, Dept Maxillofacial Surg & Diagnost Sci, Div Oral Pathol,
   Coll Dent, Jazan, Saudi Arabia
C1 King Abdulaziz Univ, Coll Dent, Dept Orthodont, Orthodont, Jeddah, Saudi
   Arabia
C1 Dr DY Patil Vidyapeeth, Dr DY Patil Dent Coll & Hosp, Dept Oral &
   Maxillofacial Pathol, Pune 411018, Maharashtra, India
C1 Jazan Univ, Dept Restorat Dent Sci, Div Operat Dent, Coll Dent, Jazan,
   Saudi Arabia
SN 1991-7902
EI 2213-8862
DA 2021-01-01
UT WOS:000604780000026
PM 33384840
ER

PT J
AU Alqutaibi, Ahmed Yaseen
   Aboalrejal, Afaf Noman
TI ARTIFICIAL INTELLIGENCE (AI) AS AN AID IN RESTORATIVE DENTISTRY IS
   PROMISING, BUT STILL A WORK IN PROGRESS
SO JOURNAL OF EVIDENCE-BASED DENTAL PRACTICE
VL 23
IS 1
AR 101837
DI 10.1016/j.jebdp.2023.101837
EA MAR 2023
DT Article
PD MAR 2023
PY 2023
AB Selection Criteria Besides manual search, an electronic search was
   conducted in 5 databases: MED-LINE, Web of Science, EMBASE, Scopus, and
   Cochrane. The eligibility crite-ria included clinical and in vitro
   studies that evaluated the diagnostic perfor-mance of artificial
   intelligence (AI) models in restorative dentistry to detect dental
   caries and vertical tooth fractures, identify tooth preparation margins,
   and pre-dict restoration failure. On the other hand, letters to editors,
   studies related to robotics in dentistry, radiographic enhancement
   investigations, and age estima-tion model studies were excluded. Two
   reviewers independently screened the title and abstract, performed data
   ex-traction, and assessed the risk of bias in relevant articles; the
   discussion with the third reviewer addressed any disagreement. The
   Joanna Briggs Institute JBI Crit-ical Appraisal Checklist for
   Quasi-Experimental Evaluation was used to appraise included studies
   critically for their quality. Key Study Factor A review of studies in
   the field of restorative dentistry that developed AI models for
   detecting dental caries (no = 29), vertical tooth fracture (no = 2), or
   the tooth finishing line (no = 1), besides studies of AI models that
   predict restoration failure (no = 2), were evaluated. Main Outcome
   Measure The diagnostic accuracy based on the sensitivity and specificity
   of AI models as a tool for detecting dental caries, vertical tooth
   fracture, the tooth finishing line, and predicting restoration failure.
RI Alqutaibi, Ahmed Yaseen/AAG-4872-2020
OI Alqutaibi, Ahmed Yaseen/0000-0001-6536-8269
ZA 0
ZB 0
Z8 0
ZS 0
ZR 0
TC 1
Z9 1
C1 Taibah Univ, Coll Dent, Dept Prosthodont & Implant Dent, Medina, Saudi
   Arabia
C1 Ibb Univ, Coll Dent, Dept Prosthodont, Ibb, Yemen
C1 Ibb Univ, Fac Oral & Dent Med, Dept Oral Biol, Ibb, Yemen
C3 Ibb Univ
C3 Ibb Univ
SN 1532-3382
EI 1532-3390
DA 2023-04-07
UT WOS:000955195300001
PM 36914305
ER

PT J
AU Musri, Nabilla
   Christie, Brenda
   Ichwan, Solachuddin Jauhari Arief
   Cahyanto, Arief
TI Deep learning convolutional neural network algorithms for the early
   detection and diagnosis of dental caries on periapical radiographs: A
   systematic review.
SO Imaging science in dentistry
VL 51
IS 3
BP 237
EP 242
DI 10.5624/isd.20210074
DT Journal Article
PD 2021-Sep
PY 2021
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.
RI Cahyanto, Arief/L-2410-2016; Musri, Nabilla/; Christie, Brenda/
OI Cahyanto, Arief/0000-0003-2222-5895; Musri, Nabilla/0000-0003-0559-6571;
   Christie, Brenda/0000-0002-4321-9518
Z8 0
ZA 0
ZR 0
ZB 0
ZS 0
TC 6
Z9 7
C1 Faculty of Dentistry, Padjadjaran University, Bandung, Indonesia.
C1 Faculty of Dentistry, International Islamic University Malaysia,
   Kuantan, Malaysia.
C1 Department of Dental Materials Science and Technology, Faculty of
   Dentistry, Padjadjaran University, Bandung, Indonesia.
C1 Oral Biomaterials Study Centre, Faculty of Dentistry, Padjadjaran
   University, Bandung, Indonesia.
C1 Department of Restorative Dentistry, Faculty of Dentistry, University of
   Malaya, Kuala Lumpur, Malaysia.
SN 2233-7822
DA 2021-10-29
UT MEDLINE:34621650
PM 34621650
ER

PT J
AU Hegde, Shwetha
   Gao, Jinlong
TI DEEP LEARNING ALGORITHMS SHOW SOME POTENTIAL AS AN ADJUNCTIVE TOOL IN
   CARIES DIAGNOSIS
SO JOURNAL OF EVIDENCE-BASED DENTAL PRACTICE
VL 22
IS 4
AR 101772
DI 10.1016/j.jebdp.2022.101772
EA DEC 2022
DT Editorial Material
PD DEC 2022
PY 2022
OI Gao, Jinlong/0000-0002-5153-5393
Z8 0
ZS 0
TC 0
ZR 0
ZB 0
ZA 0
Z9 0
C1 Univ Sydney, Sydney Dent Sch, Dentomaxillofacial Radiol, Chalmers St,
   Surry Hills, NSW 2010, Australia
C1 Univ Sydney, Fac Med & Hlth, Sydney Dent Sch, Chalmers St, Surry Hills,
   NSW 2010, Australia
SN 1532-3382
EI 1532-3390
DA 2023-01-15
UT WOS:000899822800010
PM 36494110
ER

PT J
AU Singh, Nripendra Kumar
   Raza, Khalid
TI Progress in deep learning-based dental and maxillofacial image analysis:
   A systematic review
SO EXPERT SYSTEMS WITH APPLICATIONS
VL 199
AR 116968
DI 10.1016/j.eswa.2022.116968
EA MAR 2022
DT Review
PD AUG 1 2022
PY 2022
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 seg-mentation, object detection,
   classification, and other image processing-related tasks. This study
   includes vari-ables 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.
RI Kumar Singh, Nripendra/GWQ-3806-2022; Raza, Khalid/I-4931-2012; Singh, Nripendra Kumar/
OI Raza, Khalid/0000-0002-3646-6828; Singh, Nripendra
   Kumar/0000-0002-4531-9985
ZS 0
ZR 0
ZB 1
TC 12
ZA 0
Z8 0
Z9 12
C1 Jamia Millia Islamia, Dept Comp Sci, New Delhi 110025, India
SN 0957-4174
EI 1873-6793
DA 2022-06-10
UT WOS:000793149000009
ER

PT J
AU Reyes, Lilian Toledo
   Knorst, Jessica Klockner
   Ortiz, Fernanda Ruffo
   Ardenghi, Thiago Machado
TI Machine Learning in the Diagnosis and Prognostic Prediction of Dental
   Caries: A Systematic Review
SO CARIES RESEARCH
VL 56
IS 3
BP 161
EP 170
DI 10.1159/000524167
DT Review
PD NOV 2022
PY 2022
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. (C) 2022 S. Karger AG, Basel
RI Knorst, Jessica/V-4640-2019; Ortiz, Fernanda Ruffo/O-2557-2016; ARDENGHI, THIAGO/
OI Knorst, Jessica/0000-0001-7792-8032; Ortiz, Fernanda
   Ruffo/0000-0003-0759-9661; ARDENGHI, THIAGO/0000-0002-5109-740X
ZR 0
TC 3
ZA 0
ZS 0
Z8 0
ZB 1
Z9 3
C1 Univ Fed Santa Maria, Sch Dent, Dept Stomatol, Santa Maria, Brazil
SN 0008-6568
EI 1421-976X
DA 2022-12-06
UT WOS:000888852800003
PM 35636386
ER

PT J
AU Dhiman, Poonam
   Kaur, Amandeep
   Balasaraswathi, V. R.
   Gulzar, Yonis
   Alwan, Ali A.
   Hamid, Yasir
TI Image Acquisition, Preprocessing and Classification of Citrus Fruit
   Diseases: A Systematic Literature Review
SO SUSTAINABILITY
VL 15
IS 12
AR 9643
DI 10.3390/su15129643
DT Review
PD JUN 2023
PY 2023
AB Different kinds of techniques are evaluated and analyzed for various
   classification models for the detection of diseases of citrus fruits.
   This paper aims to systematically review the papers that focus on the
   prediction, detection, and classification of citrus fruit diseases that
   have employed machine learning, deep learning, and statistical
   techniques. Additionally, this paper explores the present state of the
   art of the concept of image acquisition, digital image processing,
   feature extraction, and classification approaches, and each one is
   discussed separately. A total of 78 papers are selected after applying
   primary selection criteria, inclusion/exclusion criteria, and quality
   assessment criteria. We observe that the following are widely used in
   the selected studies: hyperspectral imaging systems for the image
   acquisition process, thresholding for image processing, support vector
   machine (SVM) models as machine learning (ML) models, convolutional
   neural network (CNN) architectures as deep learning models, principal
   component analysis (PCA) as a statistical model, and classification
   accuracy as evaluation parameters. Moreover, the color feature is the
   most popularly used feature for the RGB color space. From the review
   studies that performed comparative analyses, we find that the best
   techniques that outperformed other techniques in their respective
   categories are as follows: SVM among the ML methods, ANN among the
   neural network networks, CNN among the deep learning methods, and linear
   discriminant analysis (LDA) among the statistical techniques.This study
   concludes with meta-analysis, limitations, and future research
   directions.
RI Gulzar, Yonis/X-6394-2019; Kaur, Amandeep/IYJ-2622-2023; hamid, yasir/; ALWAN, ALI/; Kaur, Amandeep/
OI Gulzar, Yonis/0000-0002-6515-1569; Kaur, Amandeep/0000-0002-9825-4951;
   hamid, yasir/0000-0003-1334-2651; ALWAN, ALI/0000-0003-3279-9366; Kaur,
   Amandeep/0000-0002-7282-8902
ZB 2
Z8 0
ZR 0
ZS 0
ZA 0
TC 12
Z9 12
C1 Govt PG Coll, Dept Higher Educ, Ambala 133001, India
C1 Chitkara Univ, Inst Engn & Technol, Rajpura 713104, Punjab, India
C1 SRM Inst Sci & Technol, Sch Comp, Dept Networking & Commun,
   Kattankullattur 462003, India
C1 King Faisal Univ, Coll Business Adm, Dept Management Informat Syst, Al
   Hasa 31982, Saudi Arabia
C1 Ramapo Coll, Sch Theoret, Mahwah, NJ 07430 USA
C1 Ramapo Coll, Sch Appl Sci, Mahwah, NJ 07430 USA
C1 Abu Dhabi Polytech, Dept Informat Secur & Engn Technol, Abu Dhabi
   111499, U Arab Emirates
C3 Govt PG Coll
C3 SRM Inst Sci & Technol
C3 Abu Dhabi Polytech
EI 2071-1050
DA 2023-07-07
UT WOS:001015801500001
ER

PT J
AU Khanagar, Sanjeev Balappa
   Alfouzan, Khalid
   Alkadi, Lubna
   Albalawi, Farraj
   Iyer, Kiran
   Awawdeh, Mohammed
TI Performance of Artificial Intelligence (AI) Models Designed for
   Application in Pediatric Dentistry-A Systematic Review
SO APPLIED SCIENCES-BASEL
VL 12
IS 19
AR 9819
DI 10.3390/app12199819
DT Review
PD OCT 2022
PY 2022
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.
RI Iyer, Kiran/AAS-7076-2021; awawdeh, mohammed/HOC-0358-2023; Alkadi, Lubna/; KHANAGAR, SANJEEV/
OI Iyer, Kiran/0000-0003-3551-870X; Alkadi, Lubna/0000-0003-3206-8547;
   KHANAGAR, SANJEEV/0000-0002-4098-7773
ZS 0
TC 0
ZA 0
Z8 0
ZR 0
ZB 0
Z9 0
C1 King Saud Bin Abdulaziz Univ Hlth Sci, Coll Dent, Prevent Dent Sci Dept,
   Riyadh 11426, Saudi Arabia
C1 Minist Natl Guard Hlth Affairs, King Abdullah Int Med Res Ctr, Riyadh
   11481, Saudi Arabia
C1 King Saud Bin Abdulaziz Univ Hlth Sci, Coll Dent, Restorat & Prosthet
   Dent Sci Dept, Riyadh 11426, Saudi Arabia
EI 2076-3417
DA 2022-10-20
UT WOS:000866659600001
ER

PT J
AU Khanagar, Sanjeev B.
   Alfouzan, Khalid
   Awawdeh, Mohammed
   Alkadi, Lubna
   Albalawi, Farraj
   Alfadley, Abdulmohsen
TI Application and Performance of Artificial Intelligence Technology in
   Detection, Diagnosis and Prediction of Dental Caries (DC)-A Systematic
   Review
SO DIAGNOSTICS
VL 12
IS 5
AR 1083
DI 10.3390/diagnostics12051083
DT Review
PD MAY 2022
PY 2022
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 electronic 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.
RI Alfadley, Abdulmohsen/JCO-4622-2023; awawdeh, mohammed/HOC-0358-2023; Alfadley, Abdulmohsen/; Alkadi, Lubna/; KHANAGAR, SANJEEV/
OI Alfadley, Abdulmohsen/0000-0002-5868-034X; Alkadi,
   Lubna/0000-0003-3206-8547; KHANAGAR, SANJEEV/0000-0002-4098-7773
TC 8
Z8 0
ZB 0
ZS 0
ZA 0
ZR 0
Z9 8
C1 King Saud bin Abdulaziz Univ Hlth Sci, Prevent Dent Sci Dept, Coll Dent,
   Riyadh 11426, Saudi Arabia
C1 King Abdullah Int Med Res Ctr, Minist Natl Guard Hlth Affairs, Riyadh
   11481, Saudi Arabia
C1 King Saud bin Abdulaziz Univ Hlth Sci, Restorat & Prosthet Dent Sci
   Dept, Coll Dent, Riyadh 11426, Saudi Arabia
EI 2075-4418
DA 2022-06-09
UT WOS:000803406500001
PM 35626239
ER

PT J
AU Revilla-Leon, Marta
   Gomez-Polo, Miguel
   Vyas, Shantanu
   Barmak, Abdul Basir
   Ozcan, Mutlu
   Att, Wael
   Krishnamurthy, Vinayak R.
TI Artificial intelligence applications in restorative dentistry: A
   systematic review
SO JOURNAL OF PROSTHETIC DENTISTRY
VL 128
IS 5
BP 867
EP 875
DI 10.1016/j.prosdent.2021.02.010
EA NOV 2022
DT Review
PD NOV 2022
PY 2022
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. (J Prosthet Dent 2022;128:867-75)
RI Revilla-León, Marta/AAH-2117-2019; BARMAK, ABDUL BASIR/; Att, Wael/; Vyas, Shantanu/; Gomez-Polo, Miguel/
OI Revilla-León, Marta/0000-0003-2854-1135; BARMAK, ABDUL
   BASIR/0000-0002-7067-9712; Att, Wael/0000-0002-4879-0895; Vyas,
   Shantanu/0000-0003-2314-2821; Gomez-Polo, Miguel/0000-0001-8614-8484
ZA 0
ZS 0
ZB 1
ZR 0
TC 22
Z8 1
Z9 23
C1 Texas A&M Univ, Coll Dent, Dept Comprehens Dent, Dallas, TX USA
C1 Univ Washington, Affiliate Fac Grad Prosthodont, Sch Dent, Dept Restorat
   Dent, Seattle, WA USA
C1 Revilla Res Ctr, Madrid, Spain
C1 Univ Complutense Madrid, Sch Dent, Dept Conservat Dent & Prosthodont,
   Ramon y Cajal S-N, Madrid 28040, Spain
C1 Texas A&M Univ, J Mike Walker Dept Mech Engn 66, Dallas, TX USA
C1 Univ Rochester, Eastman Inst Oral Hlth, Clin Res & Biostat, Med Ctr,
   Rochester, NY USA
C1 Univ Zurich, Ctr Dent & Oral Med, Div Dent Biomat, Clin Reconstruct
   Dent, Zurich, Switzerland
C1 Tufts Univ, Dept Prosthodont, Sch Dent Med, Boston, MA USA
C1 Texas A&M Univ, J Mike Walker Dept Mech Engn 66, College Stn, TX USA
C3 Revilla Res Ctr
SN 0022-3913
EI 1097-6841
DA 2023-01-06
UT WOS:000892488100007
PM 33840515
ER

PT J
AU Goel, Lavika
   Nagpal, Jyoti
TI A Systematic Review of Recent Machine Learning Techniques for Plant
   Disease Identification and Classification
SO IETE TECHNICAL REVIEW
VL 40
IS 3
BP 423
EP 439
DI 10.1080/02564602.2022.2121772
EA SEP 2022
DT Review
PD MAY 4 2023
PY 2023
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%.
OI Goel, Lavika/0000-0002-3200-2604
ZB 0
ZS 0
Z8 0
TC 0
ZR 0
ZA 0
Z9 0
C1 Malaviya Natl Inst Technol, Jaipur, Rajasthan, India
SN 0256-4602
EI 0974-5971
DA 2022-09-30
UT WOS:000857333100001
ER

PT J
AU Bentsen, Niclas Scott
TI Carbon debt and payback time - Lost in the forest?
SO RENEWABLE & SUSTAINABLE ENERGY REVIEWS
VL 73
BP 1211
EP 1217
DI 10.1016/j.rser.2017.02.004
DT Review
PD JUN 2017
PY 2017
AB In later years the potential contribution of forest bioenergy to
   mitigate climate change has been increasingly questioned due to temporal
   displacement between CO2 emissions 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.
RI Bentsen, Niclas Scott/E-4766-2010
OI Bentsen, Niclas Scott/0000-0002-5130-0818
TC 41
Z8 1
ZS 0
ZA 0
ZR 0
ZB 17
Z9 42
C1 Univ Copenhagen, Fac Sci, Dept Geosci & Nat Resource Management,
   Rolighedsvej 23, DK-1958 Frederiksberg C, Denmark
SN 1364-0321
DA 2017-05-31
UT WOS:000401204700091
ER

PT J
AU Olajide, Olufemi Olatunde
Z2  
TI Developing Caries Inequalities Risk Prediction Tools for Children Under
   the Age of Six
DT Dissertation/Thesis
PD Jan 01 2019
PY 2019
ZA 0
ZR 0
ZS 0
Z8 0
TC 0
ZB 0
Z9 0
C1 University of Central Lancashire (United Kingdom), England
C3 University of Central Lancashire (United Kingdom)
UT PQDT:66949609
ER

PT J
AU Agarwal, Vikram
   Kelley, David R.
TI The genetic and biochemical determinants of mRNA degradation rates in
   mammals
SO GENOME BIOLOGY
VL 23
IS 1
AR 245
DI 10.1186/s13059-022-02811-x
DT Article
PD NOV 23 2022
PY 2022
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.
ZR 0
ZA 0
ZB 5
ZS 0
TC 9
Z8 0
Z9 10
C1 Calico Life Sci LLC, San Francisco, CA 94080 USA
C1 Sanofi Pasteur Inc, mRNA Ctr Excellence, Waltham, MA 02451 USA
C3 Calico Life Sci LLC
C3 Sanofi Pasteur Inc
SN 1474-760X
DA 2022-12-04
UT WOS:000886995500001
PM 36419176
ER

PT J
AU Oishi, Wakana
   Sano, Daisuke
TI Estimation of alkali dosage and contact time for treating human excreta
   containing viruses as an emergency response: a systematic review
SO FRONTIERS IN PUBLIC HEALTH
VL 11
AR 1286595
DI 10.3389/fpubh.2023.1286595
DT Review
PD NOV 10 2023
PY 2023
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.
RI Sano, Daisuke/E-5819-2011
OI Sano, Daisuke/0000-0001-8075-6972
TC 0
Z8 0
ZA 0
ZS 0
ZB 0
ZR 0
Z9 0
C1 Tohoku Univ, Grad Sch Engn, Dept Civil & Environm Engn, Sendai, Miyagi,
   Japan
C1 Tohoku Univ, Grad Sch Environm Studies, Dept Frontier Sci Adv Environm,
   Sendai, Japan
EI 2296-2565
DA 2023-12-12
UT WOS:001107273000001
PM 38026419
ER

EF