{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T16:28:57Z","timestamp":1792513737664,"version":"4.5.0"},"reference-count":76,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T00:00:00Z","timestamp":1650326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Machine learning (ML) is a key component of artificial intelligence (AI). The terms machine learning, artificial intelligence, and deep learning are erroneously used interchangeably as they appear as monolithic nebulous entities. This technology offers immense possibilities and opportunities to advance diagnostics in the field of medicine and dentistry. This necessitates a deep understanding of AI and its essential components, such as machine learning (ML), artificial neural networks (ANN), and deep learning (DP). Aim: This review aims to enlighten clinicians regarding AI and its applications in the diagnosis of oral diseases, along with the prospects and challenges involved. Review results: AI has been used in the diagnosis of various oral diseases, such as dental caries, maxillary sinus diseases, periodontal diseases, salivary gland diseases, TMJ disorders, and oral cancer through clinical data and diagnostic images. Larger data sets would enable AI to predict the occurrence of precancerous conditions. They can aid in population-wide surveillance and decide on referrals to specialists. AI can efficiently detect microfeatures beyond the human eye and augment its predictive power in critical diagnosis. Conclusion: Although studies have recognized the benefit of AI, the use of artificial intelligence and machine learning has not been integrated into routine dentistry. AI is still in the research phase. The coming decade will see immense changes in diagnosis and healthcare built on the back of this research. Clinical significance: This paper reviews the various applications of AI in dentistry and illuminates the shortcomings faced while dealing with AI research and suggests ways to tackle them. Overcoming these pitfalls will aid in integrating AI seamlessly into dentistry.<\/jats:p>","DOI":"10.3390\/diagnostics12051029","type":"journal-article","created":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T22:07:26Z","timestamp":1650406046000},"page":"1029","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":191,"title":["Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7246-5497","authenticated-orcid":false,"given":"Shankargouda","family":"Patil","sequence":"first","affiliation":[{"name":"Department of Maxillofacial Surgery and Diagnostic Sciences, Division of Oral Pathology, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0774-5550","authenticated-orcid":false,"given":"Sarah","family":"Albogami","sequence":"additional","affiliation":[{"name":"Department of Biotechnology, College of Science, Taif University, Taif 21944, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3579-0906","authenticated-orcid":false,"given":"Jagadish","family":"Hosmani","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Dental Sciences, Oral Pathology Division, Faculty of Dentistry, College of Dentistry, King Khalid University, Abha 61411, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheetal","family":"Mujoo","sequence":"additional","affiliation":[{"name":"Division of Oral Medicine & Radiology, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mona Awad","family":"Kamil","sequence":"additional","affiliation":[{"name":"Department of Preventive Dental Science, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manawar Ahmad","family":"Mansour","sequence":"additional","affiliation":[{"name":"Department of Prosthetic Dental Sciences, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hina Naim","family":"Abdul","sequence":"additional","affiliation":[{"name":"Department of Prosthetic Dental Sciences, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shilpa","family":"Bhandi","sequence":"additional","affiliation":[{"name":"Department of Restorative Dental Sciences, Division of Operative Dentistry, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiek S. S. J.","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Multi-Omics and Drug Discovery Lab, Chettinad Academy of Research and Education, Chennai 600130, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,19]]},"reference":[{"key":"ref_1","unstructured":"Ensmenger, N., and Nilsson, N.J. (2010). The Quest for Artificial Intelligence: A History of Ideas and Achievements. Xv + 562 pp., Index, Cambridge University Press."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Turing, A.M. (1937). On Computable Numbers, with an Application to the Entscheidungsproblem, London Mathematical Society.","DOI":"10.1112\/plms\/s2-42.1.230"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1145\/360018.360022","article-title":"Computer Science as Empirical Inquiry","volume":"19","author":"Newell","year":"1976","journal-title":"Commun. ACM"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.jds.2020.06.019","article-title":"Developments, Application, and Performance of Artificial Intelligence in Dentistry\u2014A Systematic Review","volume":"16","author":"Khanagar","year":"2021","journal-title":"J. Dent. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s10994-006-8919-x","article-title":"Machine Learning and Games","volume":"63","author":"Bowling","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"594","DOI":"10.4103\/ejd.ejd_325_18","article-title":"History and Application of Artificial Neural Networks in Dentistry","volume":"12","author":"Park","year":"2018","journal-title":"Eur. J. Dent."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A Fast Learning Algorithm for Deep Belief Nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1007\/s42979-021-00815-1","article-title":"Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications, and Research Directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.ajodo.2015.07.030","article-title":"New Approach for the Diagnosis of Extractions with Neural Network Machine Learning","volume":"149","author":"Jung","year":"2016","journal-title":"Am. J. Orthod. Dentofac. Orthop."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"159.e1","DOI":"10.1016\/j.forsciint.2015.12.025","article-title":"An Automatic Method for Skeletal Patterns Classification Using Craniomaxillary Variables on a Colombian Population","volume":"261","author":"Perez","year":"2016","journal-title":"Forensic Sci. Int."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"187.e1","DOI":"10.1016\/j.forsciint.2017.10.004","article-title":"Use of Automated Learning Techniques for Predicting Mandibular Morphology in Skeletal Class I, II and III","volume":"281","author":"Jaque","year":"2017","journal-title":"Forensic Sci. Int."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1016\/j.joen.2012.05.004","article-title":"The Reliability of Artificial Neural Network in Locating Minor Apical Foramen: A Cadaver Study","volume":"38","author":"Saghiri","year":"2012","journal-title":"J. Endod."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1111\/j.1365-2591.2011.01970.x","article-title":"A New Approach for Locating the Minor Apical Foramen Using an Artificial Neural Network","volume":"45","author":"Saghiri","year":"2012","journal-title":"Int. Endod. J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"27855","DOI":"10.1038\/srep27855","article-title":"An Ontology-Driven, Case-Based Clinical Decision Support Model for Removable Partial Denture Design","volume":"6","author":"Chen","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"816719","DOI":"10.1155\/2015\/816719","article-title":"The Prediction in Computer Color Matching of Dentistry Based on GA+BP Neural Network","volume":"2015","author":"Li","year":"2015","journal-title":"Comput. Math. Methods Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"540306","DOI":"10.1155\/2015\/540306","article-title":"Modelling the Longevity of Dental Restorations by Means of a CBR System","volume":"2015","author":"Aliaga","year":"2015","journal-title":"BioMed Res. Int."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.cmpb.2015.11.004","article-title":"Decision Support System for Predicting Color Change after Tooth Whitening","volume":"125","author":"Thanathornwong","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"416","DOI":"10.4103\/1119-3077.151785","article-title":"Diagnosis of Periodontal Diseases Using Different Classification Algorithms: A Preliminary Study","volume":"18","author":"Ozden","year":"2015","journal-title":"Niger. J. Clin. Pract."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nakano, Y., Suzuki, N., and Kuwata, F. (2018). Predicting Oral Malodour Based on the Microbiota in Saliva Samples Using a Deep Learning Approach. BMC Oral Health, 18.","DOI":"10.1186\/s12903-018-0591-6"},{"key":"ref_20","first-page":"7","article-title":"Predicting Recurrent Aphthous Ulceration Using Genetic Algorithms-Optimized Neural Networks","volume":"3","author":"Alsmadi","year":"2010","journal-title":"Adv. Appl. Bioinform. Chem."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1111\/j.1600-9657.2012.01148.x","article-title":"Performance of an Artificial Neural Network for Vertical Root Fracture Detection: An Ex Vivo Study","volume":"29","author":"Kositbowornchai","year":"2013","journal-title":"Dent. Traumatol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"168","DOI":"10.3109\/14015439.2011.606227","article-title":"Artificial Neural Network Analysis to Assess Hypernasality in Patients Treated for Oral or Oropharyngeal Cancer","volume":"36","author":"Kuik","year":"2011","journal-title":"Logop. Phoniatr. Vocol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chang, S.W., Abdul-Kareem, S., Merican, A.F., and Zain, R.B. (2013). Oral Cancer Prognosis Based on Clinicopathologic and Genomic Markers Using a Hybrid of Feature Selection and Machine Learning Methods. BMC Bioinform., 14.","DOI":"10.1186\/1471-2105-14-170"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1111\/adj.12812","article-title":"Promises and Perils of Artificial Intelligence in Dentistry","volume":"66","author":"Pethani","year":"2021","journal-title":"Aust. Dent. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1177\/0022034520969115","article-title":"Application of Artificial Intelligence in Dentistry","volume":"100","author":"Shan","year":"2021","journal-title":"J. Dent. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1016\/j.tripleo.2008.03.002","article-title":"An Artificial Multilayer Perceptron Neural Network for Diagnosis of Proximal Dental Caries","volume":"106","author":"Devito","year":"2008","journal-title":"Oral Surg. Oral Med. Oral Pathol. Oral Radiol. Endodontol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.jdent.2018.07.015","article-title":"Detection and Diagnosis of Dental Caries Using a Deep Learning-Based Convolutional Neural Network Algorithm","volume":"77","author":"Lee","year":"2018","journal-title":"J. Dent."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1111\/ger.12432","article-title":"Application of Machine Learning for Diagnostic Prediction of Root Caries","volume":"36","author":"Hung","year":"2019","journal-title":"Gerodontology"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1177\/0022034519871884","article-title":"Caries Detection with Near-Infrared Transillumination Using Deep Learning","volume":"98","author":"Casalegno","year":"2019","journal-title":"J. Dent. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"103425","DOI":"10.1016\/j.jdent.2020.103425","article-title":"Detecting Caries Lesions of Different Radiographic Extension on Bitewings Using Deep Learning","volume":"100","author":"Cantu","year":"2020","journal-title":"J. Dent."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Park, Y.H., Kim, S.H., and Choi, Y.Y. (2021). Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18168613"},{"key":"ref_32","first-page":"105781K","article-title":"Automatic Quantification Framework to Detect Cracks in Teeth","volume":"10578","author":"Paniagua","year":"2018","journal-title":"Proc. SPIE Int. Soc. Opt. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/s11282-019-00409-x","article-title":"Evaluation of an Artificial Intelligence System for Detecting Vertical Root Fracture on Panoramic Radiography","volume":"36","author":"Fukuda","year":"2019","journal-title":"Oral Radiol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1007\/s11548-021-02431-z","article-title":"Automating Periodontal Bone Loss Measurement via Dental Landmark Localisation","volume":"16","author":"Danks","year":"2021","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"S159","DOI":"10.1002\/JPER.18-0006","article-title":"Staging and Grading of Periodontitis: Framework and Proposal of a New Classification and Case Definition","volume":"89","author":"Tonetti","year":"2018","journal-title":"J. Periodontol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7531","DOI":"10.1038\/s41598-020-64509-z","article-title":"Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis","volume":"10","author":"Chang","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s11282-018-0363-7","article-title":"Deep-Learning Classification Using Convolutional Neural Network for Evaluation of Maxillary Sinusitis on Panoramic Radiography","volume":"35","author":"Murata","year":"2018","journal-title":"Oral Radiol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1097\/RLI.0000000000000503","article-title":"Deep Learning in Diagnosis of Maxillary Sinusitis Using Conventional Radiography","volume":"54","author":"Kim","year":"2019","journal-title":"Investig. Radiol."},{"key":"ref_39","first-page":"20200171","article-title":"Performance of Deep Learning Object Detection Technology in the Detection and Diagnosis of Maxillary Sinus Lesions on Panoramic Radiographs","volume":"50","author":"Kuwana","year":"2021","journal-title":"Dentomaxillofacial Radiol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hung, K.F., Ai, Q.Y.H., King, A.D., Bornstein, M.M., Wong, L.M., and Leung, Y.Y. (2022). Automatic Detection and Segmentation of Morphological Changes of the Maxillary Sinus Mucosa on Cone-Beam Computed Tomography Images Using a Three-Dimensional Convolutional Neural Network. Clin. Oral Investig., online ahead of print.","DOI":"10.1007\/s00784-021-04365-x"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1259\/dmfr.20190019","article-title":"Preliminary Study on the Application of Deep Learning System to Diagnosis of Sj\u00f6gren\u2019s Syndrome on CT Images","volume":"48","author":"Kise","year":"2019","journal-title":"Dentomaxillofacial Radiol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"151869","DOI":"10.1016\/j.anndiagpath.2021.151869","article-title":"A Tree-Based Machine Learning Model to Approach Morphologic Assessment of Malignant Salivary Gland Tumors","volume":"56","year":"2022","journal-title":"Ann. Diagn. Pathol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3355","DOI":"10.21873\/invivo.12633","article-title":"Prediction of Recurrence by Machine Learning in Salivary Gland Cancer Patients After Adjuvant (Chemo)Radiotherapy","volume":"35","author":"Valentini","year":"2021","journal-title":"Vivo"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.jss.2020.12.053","article-title":"Machine Learning Models for Predicting Facial Nerve Palsy in Parotid Gland Surgery for Benign Tumors","volume":"262","author":"Echaniz","year":"2021","journal-title":"J. Surg. Res."},{"key":"ref_45","first-page":"259","article-title":"Artificial Neural Network Learns to Differentiate Normal TMJs and Nonreducing Displaced Disks after Training on Incisor-Point Chewing Movements","volume":"21","author":"Radke","year":"2003","journal-title":"Cranio J. Craniomandib. Pract."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.joms.2011.03.069","article-title":"Use of Artificial Neural Network in Differentiation of Subgroups of Temporomandibular Internal Derangements: A Preliminary Study","volume":"70","author":"Bas","year":"2012","journal-title":"J. Oral Maxillofac. Surg."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"20140279","DOI":"10.1259\/dmfr.20140279","article-title":"Bayesian Belief Network Analysis Applied to Determine the Progression of Temporomandibular Disorders Using MRI","volume":"44","author":"Iwasaki","year":"2015","journal-title":"Dentomaxillofacial Radiol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"10246","DOI":"10.1038\/s41598-021-89742-y","article-title":"Artificial Intelligence in Detecting Temporomandibular Joint Osteoarthritis on Orthopantomogram","volume":"11","author":"Choi","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"6656773","DOI":"10.1155\/2021\/6656773","article-title":"Development and Validation of a Magnetic Resonance Imaging-Based Machine Learning Model for TMJ Pathologies","volume":"2021","author":"Orhan","year":"2021","journal-title":"BioMed Res. Int."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"20210318","DOI":"10.1259\/dmfr.20210318","article-title":"Artificial Intelligence and Infrared Thermography as Auxiliary Tools in the Diagnosis of Temporomandibular Disorder","volume":"51","author":"Paulino","year":"2022","journal-title":"Dentomaxillofacial Radiol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1259\/dmfr\/31116116","article-title":"Identification of Post-Menopausal Women at Risk of Osteoporosis by Trained General Dental Practitioners Using Panoramic Radiographs","volume":"36","author":"Taguchi","year":"2014","journal-title":"Dentomaxillofacial Radiol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.tripleo.2007.09.013","article-title":"Assessment of the Relationship between the Mandibular Cortex on Panoramic Radiographs and the Risk of Bone Fracture and Vascular Disease in 80-Year-Olds","volume":"106","author":"Okabe","year":"2008","journal-title":"Oral Surg. Oral Med. Oral Pathol. Oral Radiol. Endod."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1111\/j.1600-0722.1994.tb01156.x","article-title":"Pantomography in Assessment of the Osteoporosis Risk Group","volume":"102","author":"Klemetti","year":"1994","journal-title":"Eur. J. Oral Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1259\/dmfr.25.5.9161180","article-title":"Usefulness of Panoramic Radiography in the Diagnosis of Postmenopausal Osteoporosis in Women. Width and Morphology of Inferior Cortex of the Mandible","volume":"25","author":"Taguchi","year":"2014","journal-title":"Dentomaxillofacial Radiol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"20170344","DOI":"10.1259\/dmfr.20170344","article-title":"Osteoporosis Detection in Panoramic Radiographs Using a Deep Convolutional Neural Network-Based Computer-Assisted Diagnosis System: A Preliminary Study","volume":"48","author":"Lee","year":"2019","journal-title":"Dentomaxillofacial Radiol."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Lee, K.S., Jung, S.K., Ryu, J.J., Shin, S.W., and Choi, J. (2020). Evaluation of Transfer Learning with Deep Convolutional Neural Networks for Screening Osteoporosis in Dental Panoramic Radiographs. J. Clin. Med., 9.","DOI":"10.3390\/jcm9020392"},{"key":"ref_57","unstructured":"(2022, January 31). Cancer. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/cancer."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1002\/bip.20473","article-title":"Principal Component Analysis and Artificial Neural Network Analysis of Oral Tissue Fluorescence Spectra: Classification of Normal Premalignant and Malignant Pathological Conditions","volume":"82","author":"Nayak","year":"2006","journal-title":"Biopolymers"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Uthoff, R.D., Song, B., Sunny, S., Patrick, S., Suresh, A., Kolur, T., Keerthi, G., Spires, O., Anbarani, A., and Wilder-Smith, P. (2018). Point-of-Care, Smartphone-Based, Dual-Modality, Dual-View, Oral Cancer Screening Device with Neural Network Classification for Low-Resource Communities. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0207493"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"11979","DOI":"10.1038\/s41598-017-12320-8","article-title":"Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity Using Deep Learning","volume":"7","author":"Aubreville","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_61","first-page":"4893","article-title":"Oral Cancer Prediction Using Gene Expression Profiling and Machine Learning","volume":"12","author":"Shams","year":"2017","journal-title":"Int. J. Appl. Eng. Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1007\/s00432-018-02834-7","article-title":"Computer-Assisted Medical Image Classification for Early Diagnosis of Oral Cancer Employing Deep Learning Algorithm","volume":"145","author":"Jeyaraj","year":"2019","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"6994","DOI":"10.1038\/s41598-019-43372-7","article-title":"Deep Learning-Based Survival Prediction of Oral Cancer Patients","volume":"9","author":"Kim","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"104068","DOI":"10.1016\/j.ijmedinf.2019.104068","article-title":"Comparison of Supervised Machine Learning Classification Techniques in Prediction of Locoregional Recurrences in Early Oral Tongue Cancer","volume":"136","author":"Alabi","year":"2020","journal-title":"Int. J. Med. Inform."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1111\/jop.13157","article-title":"Application of Artificial Intelligence and Machine Learning for Prediction of Oral Cancer Risk","volume":"50","author":"Alhazmi","year":"2021","journal-title":"J. Oral Pathol. Med."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1111\/jop.13089","article-title":"Machine Learning and Treatment Outcome Prediction for Oral Cancer","volume":"49","author":"Chu","year":"2020","journal-title":"J. Oral Pathol. Med."},{"key":"ref_67","first-page":"462","article-title":"Deep Learning Classification Method to Detect and Diagnose the Cancer Regions in Oral MRI Images","volume":"21","author":"Kirubabai","year":"2021","journal-title":"Med. Leg. Update"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.oooo.2018.10.002","article-title":"Contrast-Enhanced Computed Tomography Image Assessment of Cervical Lymph Node Metastasis in Patients with Oral Cancer by Using a Deep Learning System of Artificial Intelligence","volume":"127","author":"Ariji","year":"2019","journal-title":"Oral Surg. Oral Med. Oral Pathol. Oral Radiol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1007\/s11282-019-00391-4","article-title":"CT Evaluation of Extranodal Extension of Cervical Lymph Node Metastases in Patients with Oral Squamous Cell Carcinoma Using Deep Learning Classification","volume":"36","author":"Ariji","year":"2019","journal-title":"Oral Radiol."},{"key":"ref_70","unstructured":"(2022, March 28). General Data Protection Regulation (GDPR)\u2014Official Legal Text. Available online: https:\/\/gdpr-info.eu\/."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Hulsen, T. (2020). Sharing Is Caring\u2014Data Sharing Initiatives in Healthcare. Int. J. Environ. Res. Public Health, 17.","DOI":"10.20944\/preprints202003.0141.v1"},{"key":"ref_72","first-page":"373","article-title":"A Privacy-Preserving Infrastructure for Analyzing Personal Health Data in a Vertically Partitioned Scenario","volume":"264","author":"Sun","year":"2019","journal-title":"Stud. Health Technol. Inform."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1093\/ije\/dyu188","article-title":"DataSHIELD: Taking the Analysis to the Data, Not the Data to the Analysis","volume":"43","author":"Gaye","year":"2014","journal-title":"Int. J. Epidemiol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1177\/0022034520915714","article-title":"Artificial Intelligence in Dentistry: Chances and Challenges","volume":"99","author":"Schwendicke","year":"2020","journal-title":"J. Dent. Res."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1590\/1807-3107bor-2021.vol35.0094","article-title":"Demystifying Artificial Intelligence and Deep Learning in Dentistry","volume":"35","author":"Rodrigues","year":"2021","journal-title":"Braz. Oral Res."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"375","DOI":"10.17219\/acem\/115083","article-title":"The Ways of Using Machine Learning in Dentistry","volume":"29","author":"MacHoy","year":"2020","journal-title":"Adv. Clin. Exp. 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