This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was "artificial intelligence" AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011-2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.

Department of Genetics Cancer Research Institute Biomedical Research Center Slovak Academy of Sciences Dubravska Cesta 9 84505 Bratislava Slovakia

Department of Histology and Embryology Faculty of Medicine Comenius University in Bratislava 81372 Bratislava Slovakia

Department of Oral and Maxillofacial Surgery Faculty of Medicine Comenius University in Bratislava and University Hospital 81372 Bratislava Slovakia

Department of Orthodontics and Cleft Anomalies Faculty Hospital Kralovske Vinohrady Dental Clinic 3rd Medical Faculty Charles University 10034 Prague Czech Republic

Department of Simulation and Virtual Medical Education Faculty of Medicine Comenius University in Bratislava Sasinkova 4 81272 Bratislava Slovakia

Department of Stomatology and Maxillofacial Surgery Faculty of Medicine Comenius University in Bratislava 81250 Bratislava Slovakia

Department of Stomatology and Maxillofacial Surgery Jessenius Faculty of Medicine in Martin Comenius University Bratislava Kollárova 2 03659 Martin Slovakia

Zobrazit více v PubMed

Obermeyer Z., Emanuel E.J. Predicting the Future—Big Data, Machine Learning, and Clinical Medicine. N. Engl. J. Med. 2016;375:1216. doi: 10.1056/NEJMp1606181. PubMed DOI PMC

Gharavi S.M.H., Faghihimehr A. Clinical Application of Artificial Intelligence in PET Imaging of Head and Neck Cancer. PET Clin. 2022;17:65–76. doi: 10.1016/j.cpet.2021.09.004. PubMed DOI

Patil S., Albogami S., Hosmani J., Mujoo S., Kamil M.A., Mansour M.A., Abdul H.N., Bhandi S., Ahmed S.S.S.J. Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls. Diagnostics. 2022;12:1029. doi: 10.3390/diagnostics12051029. PubMed DOI PMC

Rasteau S., Ernenwein D., Savoldelli C., Bouletreau P. Artificial Intelligence for Oral and Maxillo-Facial Surgery: A Narrative Review. J. Stomatol. Oral Maxillofac. Surg. 2022;123:276–282. doi: 10.1016/j.jormas.2022.01.010. PubMed DOI

Monterubbianesi R., Tosco V., Vitiello F., Orilisi G., Fraccastoro F., Putignano A., Orsini G. Augmented, Virtual and Mixed Reality in Dentistry: A Narrative Review on the Existing Platforms and Future Challenges. Appl. Sci. 2022;12:877. doi: 10.3390/app12020877. DOI

Oshida Y. Artificial Intelligence for Medicine. Artif. Intell. Med. 2021 doi: 10.1515/9783110717853/HTML. DOI

Carrillo-Perez F., Pecho O.E., Morales J.C., Paravina R.D., della Bona A., Ghinea R., Pulgar R., del Mar Pérez M., Herrera L.J. Applications of Artificial Intelligence in Dentistry: A Comprehensive Review. J. Esthet. Restor. Dent. 2022;34:259–280. doi: 10.1111/jerd.12844. PubMed DOI

Pethani F. Promises and Perils of Artificial Intelligence in Dentistry. Aust. Dent. J. 2021;66:124–135. doi: 10.1111/adj.12812. PubMed DOI

Nguyen T.T. Use of Artificial Intelligence in Dentistry: Current Clinical Trends and Research Advances. J. Can. Dent. Assoc. 2021 PubMed

Thurzo A., Jančovičová V., Hain M., Thurzo M., Novák B., Kosnáčová H., Lehotská V., Moravanský N., Varga I. Human Remains Identification Using Micro-CT, Spectroscopic and A.I. Methods in Forensic Experimental Reconstruction of Dental Patterns After Concentrated Acid Significant Impact. Molecules. 2022;27:4035. doi: 10.3390/molecules27134035. PubMed DOI PMC

Thurzo A., Kosnáčová H.S., Kurilová V., Kosmeľ S., Beňuš R., Moravanský N., Kováč P., Kuracinová K.M., Palkovič M., Varga I. Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy. Healthcare. 2021;9:1545. doi: 10.3390/healthcare9111545. PubMed DOI PMC

Obermeyer Z., Powers B., Vogeli C., Mullainathan S. Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations. Science (1979) 2019;366:447–453. doi: 10.1126/science.aax2342. PubMed DOI

Zhang Y., Weng Y., Lund J., Faust O., Su L., Acharya R. Applications of Explainable Artificial Intelligence in Diagnosis and Surgery. Diagnostics. 2022;12:237. doi: 10.3390/diagnostics12020237. PubMed DOI PMC

Kavya R., Christopher J., Panda S., Lazarus Y.B. Machine Learning and XAI Approaches for Allergy Diagnosis. Biomed. Signal Process. Control. 2021;69:102681. doi: 10.1016/j.bspc.2021.102681. DOI

Barredo Arrieta A., Díaz-Rodríguez N., del Ser J., Bennetot A., Tabik S., Barbado A., Garcia S., Gil-Lopez S., Molina D., Benjamins R., et al. Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI. Inf. Fusion. 2020;58:82–115. doi: 10.1016/j.inffus.2019.12.012. DOI

Wolf T.G., Campus G. Changing Dental Profession—Modern Forms and Challenges in Dental Practice. Int. J. Environ. Res. Public Health. 2021;18:1945. doi: 10.3390/ijerph18041945. PubMed DOI PMC

Neville P., van der Zande M. Dentistry, e-Health and Digitalisation: A Critical Narrative Review of the Dental Literature on Digital Technologies with Insights from Health and Technology Studies. Community Dent. Health. 2020;37:51–58. doi: 10.1922/CDH_4664NEVILLE08. PubMed DOI

Tele-Orthodontics and the Future of Dental Digitalization|Dentistry IQ. [(accessed on 1 November 2021)]. Available online: https://www.dentistryiq.com/practice-management/practice-management-software/article/14035741/teleorthodontics-and-the-future-of-dental-digitalization.

Putra R.H., Doi C., Yoda N., Astuti E.R., Sasaki K. Current Applications and Development of Artificial Intelligence for Digital Dental Radiography. Dentomaxillofac. Radiol. 2022;51:51. doi: 10.1259/dmfr.20210197. PubMed DOI PMC

Alauddin M.S., Baharuddin A.S., Ghazali M.I.M. The Modern and Digital Transformation of Oral Health Care: A Mini Review. Healthcare. 2021;9:118. doi: 10.3390/healthcare9020118. PubMed DOI PMC

Joda T., Bornstein M.M., Jung R.E., Ferrari M., Waltimo T., Zitzmann N.U. Recent Trends and Future Direction of Dental Research in the Digital Era. Int. J. Environ. Res. Public Health. 2020;17:1987. doi: 10.3390/ijerph17061987. PubMed DOI PMC

Revilla-León M., Gómez-Polo M., Barmak A.B., Inam W., Kan J.Y.K., Kois J.C., Akal O. Artificial Intelligence Models for Diagnosing Gingivitis and Periodontal Disease: A Systematic Review. J. Prosthet. Dent. 2022 doi: 10.1016/j.prosdent.2022.01.026. PubMed DOI

Baniulyte G., Ali K. Artificial Intelligence—Can It Be Used to Outsmart Oral Cancer? Evid. Based Dent. 2022;23:12–13. doi: 10.1038/s41432-022-0238-y. PubMed DOI

Heo M.S., Kim J.E., Hwang J.J., Han S.S., Kim J.S., Yi W.J., Park I.W. Dmfr 50th Anniversary: Review Article Artificial Intelligence in Oral and Maxillofacial Radiology: What Is Currently Possible? Dentomaxillofac. Radiol. 2020;50:50. doi: 10.1259/DMFR.20200375/ASSET/IMAGES/LARGE/DMFR.20200375.G009.JPEG. PubMed DOI PMC

Schwendicke F., Samek W., Krois J. Artificial Intelligence in Dentistry: Chances and Challenges. J. Dent. Res. 2020;99:769–774. doi: 10.1177/0022034520915714. PubMed DOI PMC

Ferro A.S., Nicholson K., Koka S. Innovative Trends in Implant Dentistry Training and Education: A Narrative Review. J. Clin. Med. 2019;8:1618. doi: 10.3390/jcm8101618. PubMed DOI PMC

Prados-Privado M., Villalón J.G., Martínez-Martínez C.H., Ivorra C. Dental Images Recognition Technology and Applications: A Literature Review. Appl. Sci. 2020;10:2856. doi: 10.3390/app10082856. DOI

Hung M., Hon E.S., Ruiz-Negron B., Lauren E., Moffat R., Su W., Xu J., Park J., Prince D., Cheever J., et al. Exploring the Intersection between Social Determinants of Health and Unmet Dental Care Needs Using Deep Learning. Int. J. Environ. Res. Public Health. 2020;17:7286. doi: 10.3390/ijerph17197286. PubMed DOI PMC

Prados-Privado M., Villalón J.G., Martínez-Martínez C.H., Ivorra C., Prados-Frutos J.C. Dental Caries Diagnosis and Detection Using Neural Networks: A Systematic Review. J. Clin. Med. 2020;9:3579. doi: 10.3390/jcm9113579. PubMed DOI PMC

Müller A., Mertens S.M., Göstemeyer G., Krois J., Schwendicke F. Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative Study. J. Clin. Med. 2021;10:1612. doi: 10.3390/jcm10081612. PubMed DOI PMC

Moran M., Faria M., Giraldi G., Bastos L., Oliveira L., Conci A. Classification of Approximal Caries in Bitewing Radiographs Using Convolutional Neural Networks. Sensors. 2021;21:5192. doi: 10.3390/s21155192. PubMed DOI PMC

Hassani H., Andi P.A., Ghodsi A., Norouzi K., Komendantova N., Unger S. Shaping the Future of Smart Dentistry: From Artificial Intelligence (AI) to Intelligence Augmentation (IA) IoT. 2021;2:510–523. doi: 10.3390/iot2030026. DOI

Roongruangsilp P., Khongkhunthian P. The Learning Curve of Artificial Intelligence for Dental Implant Treatment Planning: A Descriptive Study. Appl. Sci. 2021;11:10159. doi: 10.3390/app112110159. DOI

Gajic M., Vojinovic J., Kalevski K., Pavlovic M., Kolak V., Vukovic B., Mladenovic R., Aleksic E. Analysis of the Impact of Oral Health on Adolescent Quality of Life Using Standard Statistical Methods and Artificial Intelligence Algorithms. Children. 2021;8:1156. doi: 10.3390/children8121156. PubMed DOI PMC

Zaborowicz M., Zaborowicz K., Biedziak B., Garbowski T. Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters. Sensors. 2022;22:637. doi: 10.3390/s22020637. PubMed DOI PMC

Zadrożny Ł., Regulski P., Brus-Sawczuk K., Czajkowska M., Parkanyi L., Ganz S., Mijiritsky E. Artificial Intelligence Application in Assessment of Panoramic Radiographs. Diagnostics. 2022;12:224. doi: 10.3390/diagnostics12010224. PubMed DOI PMC

De Angelis F., Pranno N., Franchina A., di Carlo S., Brauner E., Ferri A., Pellegrino G., Grecchi E., Goker F., Stefanelli L.V. Artificial Intelligence: A New Diagnostic Software in Dentistry: A Preliminary Performance Diagnostic Study. Int. J. Environ. Res. Public Health. 2022;19:1728. doi: 10.3390/ijerph19031728. PubMed DOI PMC

Mudrak J. Artificial Intelligence and Deep Learning in Dental Radiology. [(accessed on 28 December 2021)]. Available online: https://www.oralhealthgroup.com/features/artificial-intelligence-and-deep-learning-in-dental-radiology-a-way-forward-in-point-of-care-radiology/

Ezhov M., Gusarev M., Golitsyna M., Yates J.M., Kushnerev E., Tamimi D., Aksoy S., Shumilov E., Sanders A., Orhan K. Clinically Applicable Artificial Intelligence System for Dental Diagnosis with CBCT. Sci. Rep. 2021;11:15006. doi: 10.1038/s41598-021-94093-9. PubMed DOI PMC

Van Rijn R.R., de Luca A. Three Reasons Why Artificial Intelligence Might Be the Radiologist’s Best Friend. Radiology. 2020;296:159–160. doi: 10.1148/radiol.2020200855. PubMed DOI

Bose S., Sur J., Khan F., Tuzoff D. The Scope of Artificial Intelligence in Oral Radiology- A Review. Int. J. Med. Health Sci. 2020;9:67–72.

Orhan K., Bayrakdar I.S., Ezhov M., Kravtsov A., Özyürek T. Evaluation of Artificial Intelligence for Detecting Periapical Pathosis on Cone-Beam Computed Tomography Scans. Int. Endod. J. 2020;53:680–689. doi: 10.1111/iej.13265. PubMed DOI

Makaremi M., Lacaule C., Mohammad-Djafari A. Deep Learning and Artificial Intelligence for the Determination of the Cervical Vertebra Maturation Degree from Lateral Radiography. Entropy. 2019;21:1222. doi: 10.3390/e21121222. DOI

Tanikawa C., Yamashiro T. Development of Novel Artificial Intelligence Systems to Predict Facial Morphology after Orthognathic Surgery and Orthodontic Treatment in Japanese Patients. Sci. Rep. 2021;11:15853. doi: 10.1038/s41598-021-95002-w. PubMed DOI PMC

Zhang C., Chen Z., Liu J., Wu M., Yang J., Zhu Y., Lu W.W., Ruan C. 3D-Printed Pre-Tapped-Hole Scaffolds Facilitate One-Step Surgery of Predictable Alveolar Bone Augmentation and Simultaneous Dental Implantation. Compos. Part B Eng. 2021;229:109461. doi: 10.1016/j.compositesb.2021.109461. DOI

Bayrakdar S.K., Orhan K., Bayrakdar I.S., Bilgir E., Ezhov M., Gusarev M., Shumilov E. A Deep Learning Approach for Dental Implant Planning in Cone-Beam Computed Tomography Images. BMC Med. Imaging. 2021;21:1–9. doi: 10.1186/S12880-021-00618-Z/TABLES/3. PubMed DOI PMC

Mashouri P., Skreta M., Phillips J., McAllister D., Roy M., Senkaiahliyan S., Brudno M., Singh D. 3D Photography Based Neural Network Craniosynostosis Triaging System. Proc. Mach. Learn. Res. 2020;136:226–237.

Ajmera D.H., Singh P., Leung Y.Y., Gu M. Three-Dimensional Evaluation of Soft-Tissue Response to Osseous Movement after Orthognathic Surgery in Patients with Facial Asymmetry: A Systematic Review. J. Craniomaxillofac. Surg. 2021;49:763–774. doi: 10.1016/j.jcms.2021.04.010. PubMed DOI

Thurzo A., Kurilová V., Varga I. Artificial Intelligence in Orthodontic Smart Application for Treatment Coaching and Its Impact on Clinical Performance of Patients Monitored with AI-TeleHealth System. Healthcare. 2021;9:1695. doi: 10.3390/healthcare9121695. PubMed DOI PMC

Mucchi L., Jayousi S., Gant A., Paoletti E., Zoppi P. Tele-Monitoring System for Chronic Diseases Management: Requirements and Architecture. Int. J. Environ. Res. Public Health. 2021;18:7459. doi: 10.3390/ijerph18147459. PubMed DOI PMC

Kravitz N., Burris B., Butler D., Dabney C. Teledentistry, Do-It-Yourself Orthodontics, and Remote Treatment Monitoring. J. Clin. Orthod. 2016;50:718–726. PubMed

Morris R.S., Hoye L.N., Elnagar M.H., Atsawasuwan P., Galang-Boquiren M.T., Caplin J., Viana G.C., Obrez A., Kusnoto B. Accuracy of Dental Monitoring 3D Digital Dental Models Using Photograph and Video Mode. Am. J. Orthod. Dentofac. Orthop. 2019;156:420–428. doi: 10.1016/j.ajodo.2019.02.014. PubMed DOI

Park J.H., Rogowski L., Kim J.H., al Shami S., Howell S.E.I. Teledentistry Platforms for Orthodontics. J. Clin. Pediatr. Dent. 2021;45:48–53. doi: 10.17796/1053-4625-45.1.9. PubMed DOI

Mandall N., O’Brien K., Brady J., Worthington H., Harvey L. Teledentistry for Screening New Patient Orthodontic Referrals. Part 1: A Randomised Controlled Trial. Br. Dent. J. 2005;199:659–662. doi: 10.1038/sj.bdj.4812930. PubMed DOI

Turchetta B., Fishman L., Subtelny J. Facial Growth Prediction: A Comparison of Methodologies. Am. J. Orthod. Dentofac. Orthop. 2007;132:439–449. doi: 10.1016/j.ajodo.2005.10.026. PubMed DOI

Cao K., Choi K., Jung H., Duan L. Deep Learning for Facial Beauty Prediction. Information. 2020;11:391. doi: 10.3390/info11080391. DOI

Sajedi H., Pardakhti N. Age Prediction Based on Brain MRI Image: A Survey. J. Med. Syst. 2019;43:279. doi: 10.1007/s10916-019-1401-7. PubMed DOI

Iyer T.J., Rahul K., Nersisson R., Zhuang Z., Joseph Raj A.N., Refayee I. Machine Learning-Based Facial Beauty Prediction and Analysis of Frontal Facial Images Using Facial Landmarks and Traditional Image Descriptors. Comput. Intell. Neurosci. 2021;2021:4423407. doi: 10.1155/2021/4423407. PubMed DOI PMC

Artificial Intelligence in Orthodontics: Prediction and Planning|International Journal of Advances in Scientific Research. [(accessed on 7 April 2022)]. Available online: https://ssjournals.com/index.php/ijasr/article/view/5672.

Kishimoto T., Goto T., Matsuda T., Iwawaki Y., Ichikawa T. Application of Artificial Intelligence in the Dental Field: A Literature Review. J. Prosthodont. Res. 2022;66:19–28. doi: 10.2186/jpr.JPR_D_20_00139. PubMed DOI

Shan T., Tay F.R., Gu L. Application of Artificial Intelligence in Dentistry. J. Dent. Res. 2021;100:232–244. doi: 10.1177/0022034520969115. PubMed DOI

Nissan J., Kosan E., Krois J., Wingenfeld K., Deuter C.E., Gaudin R., Schwendicke F. Patients’ Perspectives on Artificial Intelligence in Dentistry: A Controlled Study. J. Clin. Med. 2022;11:2143. doi: 10.3390/JCM11082143. PubMed DOI PMC

Kim S.-H., Kim K.B., Choo H. New Frontier in Advanced Dentistry: CBCT, Intraoral Scanner, Sensors, and Artificial Intelligence in Dentistry. Sensors. 2022;22:2942. doi: 10.3390/s22082942. PubMed DOI PMC

Ossowska A., Kusiak A., Świetlik D. Artificial Intelligence in Dentistry—Narrative Review. Int. J. Environ. Res. Public Health. 2022;19:3449. doi: 10.3390/ijerph19063449. PubMed DOI PMC

Ibrahim H., Liu X., Rivera S.C., Moher D., Chan A.W., Sydes M.R., Calvert M.J., Denniston A.K. Reporting Guidelines for Clinical Trials of Artificial Intelligence Interventions: The SPIRIT-AI and CONSORT-AI Guidelines. Trials. 2021;22:11. doi: 10.1186/s13063-020-04951-6. PubMed DOI PMC

Campbell J.P., Lee A.Y., Abràmoff M., Keane P.A., Ting D.S.W., Lum F., Chiang M.F. Reporting Guidelines for Artificial Intelligence in Medical Research. Ophthalmology. 2020;127:1596–1599. doi: 10.1016/j.ophtha.2020.09.009. PubMed DOI PMC

Ibrahim H., Liu X., Denniston A.K. Reporting Guidelines for Artificial Intelligence in Healthcare Research. Clin. Exp. Ophthalmol. 2021;49:470–476. doi: 10.1111/ceo.13943. PubMed DOI

Sounderajah V., Ashrafian H., Golub R.M., Shetty S., de Fauw J., Hooft L., Moons K., Collins G., Moher D., Bossuyt P.M., et al. Developing a Reporting Guideline for Artificial Intelligence-Centred Diagnostic Test Accuracy Studies: The STARD-AI Protocol. BMJ Open. 2021;11:e047709. doi: 10.1136/bmjopen-2020-047709. PubMed DOI PMC

Page M.J., McKenzie J.E., Bossuyt P.M., Boutron I., Hoffmann T.C., Mulrow C.D., Shamseer L., Tetzlaff J.M., Akl E.A., Brennan S.E., et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ. 2021;372:n71. doi: 10.1136/bmj.n71. PubMed DOI PMC

Shamseer L., Moher D., Clarke M., Ghersi D., Liberati A., Petticrew M., Shekelle P., Stewart L.A., Altman D.G., Booth A., et al. Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015: Elaboration and Explanation. BMJ. 2015;349:7647. doi: 10.1136/bmj.g7647. PubMed DOI

Moher D., Shamseer L., Clarke M., Ghersi D., Liberati A., Petticrew M., Shekelle P., Stewart L.A., Estarli M., Barrera E.S.A., et al. Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015 Statement. Rev. Esp. De Nutr. Hum. Y Diet. 2016;20:148–160. doi: 10.1186/2046-4053-4-1. DOI

Alonso-Coello P., Schünemann H.J., Moberg J., Brignardello-Petersen R., Akl E.A., Davoli M., Treweek S., Mustafa R.A., Rada G., Rosenbaum S., et al. GRADE Evidence to Decision (EtD) Frameworks: A Systematic and Transparent Approach to Making Well Informed Healthcare Choices. 1: Introduction. BMJ. 2016;353 doi: 10.1136/bmj.i2016. PubMed DOI

Harzing A.W. Publish or Perish. [(accessed on 9 April 2022)]. Available online: https://harzing.com/resources/publish-or-perish.

The A., Chaushu G., Wai Kan Yeung A. The Diagnostic Relevance and Interfaces Covered by Mach Band Effect in Dentistry: An Analysis of the Literature. Healthcare. 2022;10:632. doi: 10.3390/HEALTHCARE10040632. PubMed DOI PMC

Cantrell A., Booth A., Chambers D. A Systematic Review Case Study of Urgent and Emergency Care Configuration Found Citation Searching of Web of Science and Google Scholar of Similar Value. Health Info. Libr. J. 2022:1–16. doi: 10.1111/hir.12428. PubMed DOI

Lee J.H., Kim D.H., Jeong S.N., Choi S.H. Detection and Diagnosis of Dental Caries Using a Deep Learning-Based Convolutional Neural Network Algorithm. J. Dent. 2018;77:106–111. doi: 10.1016/j.jdent.2018.07.015. PubMed DOI

Lee J.H., Kim D.H., Jeong S.N., Choi S.H. Diagnosis and Prediction of Periodontally Compromised Teeth Using a Deep Learning-Based Convolutional Neural Network Algorithm. J. Periodontal Implant Sci. 2018;48:114–123. doi: 10.5051/jpis.2018.48.2.114. PubMed DOI PMC

Ekert T., Krois J., Meinhold L., Elhennawy K., Emara R., Golla T., Schwendicke F. Deep Learning for the Radiographic Detection of Apical Lesions. J. Endod. 2019;45:917–922.e5. doi: 10.1016/j.joen.2019.03.016. PubMed DOI

Schwendicke F., Golla T., Dreher M., Krois J. Convolutional Neural Networks for Dental Image Diagnostics: A Scoping Review. J. Dent. 2019;91:103226. doi: 10.1016/j.jdent.2019.103226. PubMed DOI

Hwang J.J., Jung Y.H., Cho B.H., Heo M.S. An Overview of Deep Learning in the Field of Dentistry. Imaging Sci. Dent. 2019;49:1–7. doi: 10.5624/isd.2019.49.1.1. PubMed DOI PMC

Fourcade A., Khonsari R.H. Deep Learning in Medical Image Analysis: A Third Eye for Doctors. J. Stomatol. Oral Maxillofac. Surg. 2019;120:279–288. doi: 10.1016/j.jormas.2019.06.002. PubMed DOI

Hiraiwa T., Ariji Y., Fukuda M., Kise Y., Nakata K., Katsumata A., Fujita H., Ariji E. A Deep-Learning Artificial Intelligence System for Assessment of Root Morphology of the Mandibular First Molar on Panoramic Radiography. Dentomaxillofac. Radiol. 2019;48:20180218. doi: 10.1259/dmfr.20180218. PubMed DOI PMC

Murata M., Ariji Y., Ohashi Y., Kawai T., Fukuda M., Funakoshi T., Kise Y., Nozawa M., Katsumata A., Fujita H., et al. Deep-Learning Classification Using Convolutional Neural Network for Evaluation of Maxillary Sinusitis on Panoramic Radiography. Oral Radiol. 2019;35:301–307. doi: 10.1007/s11282-018-0363-7. PubMed DOI

Almangush A., Mäkitie A.A., Triantafyllou A., de Bree R., Strojan P., Rinaldo A., Hernandez-Prera J.C., Suárez C., Kowalski L.P., Ferlito A., et al. Staging and Grading of Oral Squamous Cell Carcinoma: An Update. Oral Oncol. 2020;107:104799. doi: 10.1016/j.oraloncology.2020.104799. PubMed DOI

Faber J., Faber C., Faber P. Artificial Intelligence in Orthodontics. APOS Trends Orthod. 2019;9:201–205. doi: 10.25259/APOS_123_2019. DOI

Rousseau M., Retrouvey J.M. Machine Learning in Orthodontics: Automated Facial Analysis of Vertical Dimension for Increased Precision and Efficiency. Am. J. Orthod. Dentofac. Orthop. 2022;161:445–450. doi: 10.1016/j.ajodo.2021.03.017. PubMed DOI

Monill-González A., Rovira-Calatayud L., d’Oliveira N.G., Ustrell-Torrent J.M. Artificial Intelligence in Orthodontics: Where Are We Now? A Scoping Review. Orthod. Craniofacial Res. 2021;24:6–15. doi: 10.1111/ocr.12517. PubMed DOI

Thrall J.H., Li X., Li Q., Cruz C., Do S., Dreyer K., Brink J. Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success. J. Am. Coll. Radiol. 2018;15:504–508. doi: 10.1016/j.jacr.2017.12.026. PubMed DOI

Hirschfeld W.J., Moyers R.E., Ann D.D.S. Prediction of Craniofacial Growth: The State of the Art. Am. J. Orthod. 1971;60:435–444. doi: 10.1016/0002-9416(71)90112-6. PubMed DOI

Kaźmierczak S., Juszka Z., Vandevska-Radunovic V., Maal T.J.J., Fudalej P., Mańdziuk J. Prediction of the Facial Growth Direction Is Challenging. Springer; Cham, Switzerland: 2021. pp. 665–673.

Hansa I., Katyal V., Ferguson D.J., Vaid N. Outcomes of Clear Aligner Treatment with and without Dental Monitoring: A Retrospective Cohort Study. Am. J. Orthod. Dentofac. Orthop. 2021;159:453–459. doi: 10.1016/j.ajodo.2020.02.010. PubMed DOI

Caruso S., Caruso S., Pellegrino M., Skafi R., Nota A., Tecco S. A Knowledge-Based Algorithm for Automatic Monitoring of Orthodontic Treatment: The Dental Monitoring System. Two Cases. Sensors. 2021;21:1856. doi: 10.3390/s21051856. PubMed DOI PMC

Impellizzeri A., Horodinsky M., Barbato E., Polimeni A., Salah P., Galluccio G. Dental Monitoring Application: It Is a Valid Innovation in the Orthodontics Practice? Clin. Ter. 2020;171:260–267. doi: 10.7417/CT.2020.2224. PubMed DOI

Roisin L.-C., Brézulier D., Sorel O. Remotely-Controlled Orthodontics: Fundamentals and Description of the Dental Monitoring System. J. Dentofac. Anom. Orthod. 2016;19:408. doi: 10.1051/odfen/2016021. DOI

Hansa I., Semaan S.J., Vaid N.R. Clinical Outcomes and Patient Perspectives of Dental Monitoring® GoLive® with Invisalign®—a Retrospective Cohort Study. Prog. Orthod. 2020;21:16. doi: 10.1186/s40510-020-00316-6. PubMed DOI PMC

Saccomanno S., Quinzi V., Albani A., D’Andrea N., Marzo G., Macchiarelli G. Utility of Teleorthodontics in Orthodontic Emergencies during the COVID-19 Pandemic: A Systematic Review. Healthcare. 2022;10:1108. doi: 10.3390/healthcare10061108. PubMed DOI PMC

Thurzo A., Kočiš F., Novák B., Czako L., Varga I. Three-Dimensional Modeling and 3D Printing of Biocompatible Orthodontic Power-Arm Design with Clinical Application. Appl. Sci. 2021;11:9693. doi: 10.3390/app11209693. DOI

Thurzo A., Urbanová W., Novák B., Waczulíková I., Varga I. Utilization of a 3D Printed Orthodontic Distalizer for Tooth-Borne Hybrid Treatment in Class II Unilateral Malocclusions. Materials. 2022;15:1740. doi: 10.3390/ma15051740. PubMed DOI PMC

Fatima A., Shahid A.R., Raza B., Madni T.M., Janjua U.I. State-of-the-Art Traditional to the Machine- and Deep-Learning-Based Skull Stripping Techniques, Models, and Algorithms. J. Digit. Imaging. 2020;33:1443–1464. doi: 10.1007/s10278-020-00367-5. PubMed DOI PMC

Herath B., Suresh S., Downing D., Cometta S., Tino R., Castro N.J., Leary M., Schmutz B., Wille M.-L., Hutmacher D.W. Mechanical and Geometrical Study of 3D Printed Voronoi Scaffold Design for Large Bone Defects. Mater. Des. 2021;212:110224. doi: 10.1016/j.matdes.2021.110224. DOI

Thurzo A., Urbanová W., Waczulíková I., Kurilová V., Mriňáková B., Kosnáčová H., Gális B., Varga I., Matajs M., Novák B. Dental Care and Education Facing Highly Transmissible SARS-CoV-2 Variants: Prospective Biosafety Setting: Prospective, Single-Arm, Single-Center Study. Int. J. Environ. Res. Public Health. 2022;19:7693. doi: 10.3390/ijerph19137693. PubMed DOI PMC

MacHoy M.E., Szyszka-Sommerfeld L., Vegh A., Gedrange T., Woźniak K. The Ways of Using Machine Learning in Dentistry. Adv. Clin. Exp. Med. 2020;29:375–384. doi: 10.17219/acem/115083. PubMed DOI

Choi J.W., Park H., In-Hwan Kim B.S., Kim N., Kwon S.-M., Lee J.Y. Surgery-First Orthognathic Approach to Correct Facial Asymmetry: Artificial Intelligence–Based Cephalometric Analysis. Plast. Reconstr. Surg. 2022;149:496e–499e. doi: 10.1097/PRS.0000000000008818. PubMed DOI

Joshi V.K. Dental Treatment Planning and Management for the Mouth Cancer Patient. Oral Oncol. 2010;46:475–479. doi: 10.1016/j.oraloncology.2010.03.010. PubMed DOI

Chang S.W., Abdul-Kareem S., Merican A.F., Zain R.B. Oral Cancer Prognosis Based on Clinicopathologic and Genomic Markers Using a Hybrid of Feature Selection and Machine Learning Methods. BMC Bioinform. 2013;14:170. doi: 10.1186/1471-2105-14-170. PubMed DOI PMC

Kumar V., Gu Y., Basu S., Berglund A., Eschrich S.A., Schabath M.B., Forster K., Aerts H.J.W.L., Dekker A., Fenstermacher D., et al. Radiomics: The Process and the Challenges. Magn. Reson. Imaging. 2012;30:1234–1248. doi: 10.1016/j.mri.2012.06.010. PubMed DOI PMC

Patcas R., Bernini D.A.J., Volokitin A., Agustsson E., Rothe R., Timofte R. Applying Artificial Intelligence to Assess the Impact of Orthognathic Treatment on Facial Attractiveness and Estimated Age. Int. J. Oral Maxillofac. Surg. 2019;48:77–83. doi: 10.1016/j.ijom.2018.07.010. PubMed DOI

Bouletreau P., Makaremi M., Ibrahim B., Louvrier A., Sigaux N. Artificial Intelligence: Applications in Orthognathic Surgery. J. Stomatol. Oral Maxillofac. Surg. 2019;120:347–354. doi: 10.1016/j.jormas.2019.06.001. PubMed DOI

Arık S., Ibragimov B., Xing L. Fully Automated Quantitative Cephalometry Using Convolutional Neural Networks. J. Med. Imaging. 2017;4:014501. doi: 10.1117/1.JMI.4.1.014501. PubMed DOI PMC

Kang S.H., Jeon K., Kim H.-J., Seo J.K., Lee S.-H. Automatic Three-Dimensional Cephalometric Annotation System Using Three-Dimensional Convolutional Neural Networks: A Developmental Trial. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 2019;8:210–218. doi: 10.1080/21681163.2019.1674696. DOI

Yang C., Rangarajan A., Ranka S. Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer’s Disease Classification. AMIA Annu. Symp. Proc. 2018;2018:1571. PubMed PMC

Schwendicke F., Rossi J.G., Göstemeyer G., Elhennawy K., Cantu A.G., Gaudin R., Chaurasia A., Gehrung S., Krois J. Cost-Effectiveness of Artificial Intelligence for Proximal Caries Detection. J. Dent. Res. 2021;100:369–376. doi: 10.1177/0022034520972335. PubMed DOI PMC

Shimada Y., Bakhsh T.A., Schlenz M.A., Schupp B., Schmidt A., Wöstmann B., Baresel I., Krämer N., Schulz-Weidner N. New Caries Diagnostic Tools in Intraoral Scanners: A Comparative In Vitro Study to Established Methods in Permanent and Primary Teeth. Sensors. 2022;22:2156. doi: 10.3390/S22062156. PubMed DOI PMC

Bayrakdar I.S., Orhan K., Akarsu S., Çelik Ö., Atasoy S., Pekince A., Yasa Y., Bilgir E., Sağlam H., Aslan A.F., et al. Deep-Learning Approach for Caries Detection and Segmentation on Dental Bitewing Radiographs. Oral Radiol. 2021 doi: 10.1007/s11282-021-00577-9. PubMed DOI

Yasa Y., Çelik Ö., Bayrakdar I.S., Pekince A., Orhan K., Akarsu S., Atasoy S., Bilgir E., Odabaş A., Aslan A.F. An Artificial Intelligence Proposal to Automatic Teeth Detection and Numbering in Dental Bite-Wing Radiographs. Acta Odontol. Scand. 2021;79:275–281. doi: 10.1080/00016357.2020.1840624. PubMed DOI

Kelleher M., Bishop K. Tooth Surface Loss: An. Overview. British. Dental. 1999;186:61–66. doi: 10.1038/sj.bdj.4800020a2. PubMed DOI

Al Haidan A., Abu-Hammad O., Dar-Odeh N. Predicting Tooth Surface Loss Using Genetic Algorithms-Optimized Artificial Neural Networks. Comput. Math. Methods Med. 2014;2014:106236. doi: 10.1155/2014/106236. PubMed DOI PMC

Meghil M.M., Rajpurohit P., Awad M.E., McKee J., Shahoumi L.A., Ghaly M. Artificial Intelligence in Dentistry. Dent. Rev. 2022;2:100009. doi: 10.1016/j.dentre.2021.100009. DOI

Ahmed N., Abbasi M.S., Zuberi F., Qamar W., bin Halim M.S., Maqsood A., Alam M.K. Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry—A Systematic Review. BioMed Res. Int. 2021;2021:9751564. doi: 10.1155/2021/9751564. PubMed DOI PMC

Farhadian M., Shokouhi P., Torkzaban P. A Decision Support System Based on Support Vector Machine for Diagnosis of Periodontal Disease. BMC Res. Notes. 2020;13:337. doi: 10.1186/s13104-020-05180-5. PubMed DOI PMC

Chen W.P., Chang S.H., Tang C.Y., Liou M.L., Tsai S.J.J., Lin Y.L. Composition Analysis and Feature Selection of the Oral Microbiota Associated with Periodontal Disease. BioMed Res. Int. 2018;2018 :3130607. doi: 10.1155/2018/3130607. PubMed DOI PMC

Li W., Chen Y., Sun W., Brown M., Zhang X., Wang S., Miao L. A Gingivitis Identification Method Based on Contrast-Limited Adaptive Histogram Equalization, Gray-Level Co-Occurrence Matrix, and Extreme Learning Machine. Int. J. Imaging Syst. Technol. 2019;29:77–82. doi: 10.1002/ima.22298. DOI

Akther S., Saleheen N., Samiei S.A., Shetty V., Ertin E., Kumar S. MORAL: An mHealth Model for Inferring Oral Hygiene Behaviors in-the-wild Using Wrist-worn Inertial Sensors. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2019;3:1–25. doi: 10.1145/3314388. PubMed DOI

Boreak N. Effectiveness of Artifcial Intelligence Applications Designed for Endodontic Diagnosis, Decision-Making, and Prediction of Prognosis: A Systematic Review. J. Contemp. Dent. Pract. 2020;21:926–934. doi: 10.5005/jp-journals-10024-2894. PubMed DOI

Keskin C., Keles A. Digital Applications in Endodontics: An Update and Review. J. Exp. Clin. Med. 2021;38:168–174. doi: 10.52142/omujecm.38.si.dent.15. DOI

Aminoshariae A., Kulild J., Nagendrababu V. Artificial Intelligence in Endodontics: Current Applications and Future Directions. J. Endod. 2021;47:1352–1357. doi: 10.1016/j.joen.2021.06.003. PubMed DOI

Li C.W., Lin S.Y., Chou H.S., Chen T.Y., Chen Y.A., Liu S.Y., Liu Y.L., Chen C.A., Huang Y.C., Chen S.L., et al. Detection of Dental Apical Lesions Using Cnns on Periapical Radiograph. Sensors. 2021;21:7049. doi: 10.3390/s21217049. PubMed DOI PMC

Pauwels R., Brasil D.M., Yamasaki M.C., Jacobs R., Bosmans H., Freitas D.Q., Haiter-Neto F. Artificial Intelligence for Detection of Periapical Lesions on Intraoral Radiographs: Comparison between Convolutional Neural Networks and Human Observers. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 2021;131:610–616. doi: 10.1016/j.oooo.2021.01.018. PubMed DOI

Setzer F.C., Shi K.J., Zhang Z., Yan H., Yoon H., Mupparapu M., Li J. Artificial Intelligence for the Computer-Aided Detection of Periapical Lesions in Cone-Beam Computed Tomographic Images. J. Endod. 2020;46:987–993. doi: 10.1016/j.joen.2020.03.025. PubMed DOI

Fukuda M., Inamoto K., Shibata N., Ariji Y., Yanashita Y., Kutsuna S., Nakata K., Katsumata A., Fujita H., Ariji E. Evaluation of an Artificial Intelligence System for Detecting Vertical Root Fracture on Panoramic Radiography. Oral Radiol. 2020;36:337–343. doi: 10.1007/s11282-019-00409-x. PubMed DOI

Liao W.C., Chen C.H., Pan Y.H., Chang M.C., Jeng J.H. Vertical Root Fracture in Non-Endodontically and Endodontically Treated Teeth: Current Understanding and Future Challenge. J. Pers. Med. 2021;11:1375. doi: 10.3390/jpm11121375. PubMed DOI PMC

Shafi N., Bukhari F., Iqbal W., Almustafa K.M., Asif M., Nawaz Z. Cleft Prediction before Birth Using Deep Neural Network. Health Inform. J. 2020;26:2568–2585. doi: 10.1177/1460458220911789. PubMed DOI

Zhang S.J., Meng P., Zhang J., Jia P., Lin J., Wang X., Chen F., Wei X. Machine Learning Models for Genetic Risk Assessment of Infants with Non-Syndromic Orofacial Cleft. Genom. Proteom. Bioinform. 2018;16:354–364. doi: 10.1016/j.gpb.2018.07.005. PubMed DOI PMC

Baker N.C., Sipes N.S., Franzosa J., Belair D.G., Abbott B.D., Judson R.S., Knudsen T.B. Characterizing Cleft Palate Toxicants Using ToxCast Data, Chemical Structure, and the Biomedical Literature. Birth Defects Res. 2020;112:19–39. doi: 10.1002/bdr2.1581. PubMed DOI PMC

Jurek J., Wójtowicz W., Wójtowicz A. Syntactic Pattern Recognition-Based Diagnostics of Fetal Palates. Pattern Recognit. Lett. 2020;133:144–150. doi: 10.1016/j.patrec.2020.02.023. DOI

Kuwada C., Ariji Y., Kise Y., Funakoshi T., Fukuda M., Kuwada T., Gotoh K., Ariji E. Detection and Classification of Unilateral Cleft Alveolus with and without Cleft Palate on Panoramic Radiographs Using a Deep Learning System. Sci. Rep. 2021;11:16044. doi: 10.1038/s41598-021-95653-9. PubMed DOI PMC

Zhang Y., Pei Y., Chen S., Guo Y., Ma G., Xu T., Zha H. Volumetric Registration-Based Cleft Volume Estimation of Alveolar Cleft Grafting Procedures; Proceedings of the International Symposium on Biomedical Imaging; Iowa City, IA, USA. 3–7 April 2020; pp. 99–103.

Ortiz-Posadas M.R., Vega-Alvarado L., Toni B. A Similarity Function to Evaluate the Orthodontic Condition in Patients with Cleft Lip and Palate. Med. Hypotheses. 2004;63:35–41. doi: 10.1016/j.mehy.2004.01.027. PubMed DOI

Tanikawa C., Lee C., Lim J., Oka A., Yamashiro T. Clinical Applicability of Automated Cephalometric Landmark Identification: Part I-Patient-Related Identification Errors. Orthod. Craniofacial Res. 2021;24:43–52. doi: 10.1111/ocr.12501. PubMed DOI

Alam M.K., Alfawzan A.A. Dental Characteristics of Different Types of Cleft and Non-Cleft Individuals. Front. Cell Dev. Biol. 2020;8:789. doi: 10.3389/fcell.2020.00789. PubMed DOI PMC

Lim J., Tanikawa C., Kogo M., Yamashiro C. Determination of Prognostic Factors for Orthognathic Surgery in Children with Cleft Lip and or Palate. Orthod. Craniofacial Res. 2021;24:153–162. doi: 10.1111/ocr.12477. PubMed DOI

Jihee S., Yang I.-H., Choi J.-Y., Lee J.-H., Baek S.-H. Three-Dimensional Facial Soft Tissue Changes After Orthognathic Surgery in Cleft Patients Using Artificial Intelligence-Assisted Landmark Autodigitization. J. Craniofacial Surg. 2021;32:2695–2700. PubMed

Pietruski P., Majak M., Debski T., Antoszewski B. A Novel Computer System for the Evaluation of Nasolabial Morphology, Symmetry and Aesthetics after Cleft Lip and Palate Treatment. Part 1: General Concept and Validation. J. Cranio-Maxillofac. Surg. 2017;45:491–504. doi: 10.1016/j.jcms.2017.01.024. PubMed DOI

Patcas R., Timofte R., Volokitin A., Agustsson E., Eliades T., Eichenberger M., Bornstein M.M. Facial Attractiveness of Cleft Patients: A Direct Comparison between Artificial-Intelligence-Based Scoring and Conventional Rater Groups. Eur. J. Orthod. 2019;41:428–433. doi: 10.1093/ejo/cjz007. PubMed DOI

Wang X., Yang S., Tang M., Yin H., Huang H., He L. HypernasalityNet: Deep Recurrent Neural Network for Automatic Hypernasality Detection. Int. J. Med. Inform. 2019;129:1–12. doi: 10.1016/j.ijmedinf.2019.05.023. PubMed DOI

Gugsch C., Dannhauer K.H., Fuchs M. Stimmanalytische Beurteilung Des Therapieverlaufs Bei Patienten Mit Lippen-Kiefer-Gaumen-Spalten—Eine Pilotstudie. J. Orofac. Orthop. 2008;69:257–267. doi: 10.1007/s00056-008-0702-0. PubMed DOI

Bernauer S.A., Zitzmann N.U., Joda T. The Use and Performance of Artificial Intelligence in Prosthodontics: A Systematic Review. Sensors. 2021;21:6628. doi: 10.3390/s21196628. PubMed DOI PMC

Sacher M., Schulz G., Deyhle H., Jäger K., Müller B. Accuracy of Commercial Intraoral Scanners. J. Med. Imaging. 2021;8:035501. doi: 10.1117/1.JMI.8.3.035501. PubMed DOI PMC

Amornvit P., Rokaya D., Sanohkan S. Comparison of Accuracy of Current Ten Intraoral Scanners. [(accessed on 6 March 2022)];BioMed Res. Int. 2021 Available online: https://www.hindawi.com/journals/bmri/2021/2673040/ PubMed PMC

Celeghin G., Franceschetti G., Mobilio N., Fasiol A., Catapano S., Corsalini M., Grande F. Complete-Arch Accuracy of Four Intraoral Scanners: An In Vitro Study. Healthcare. 2021;9:246. doi: 10.3390/healthcare9030246. PubMed DOI PMC

ITero Element|ITero Intraoral Scanner. [(accessed on 6 March 2022)]. Available online: https://global.itero.com/products/itero_element.

3Shape® Introduces TRIOS® AI Scanning! CADpro Academy. [(accessed on 6 March 2022)]. Available online: https://cadproacademy.com/news/3shape%C2%AE-introduces-trios%C2%AE-ai-scanning.

Cabanes-Gumbau G., Palma J.C., Kois J.C., Revilla-León M. Transferring the Tooth Preparation Finish Line on Intraoral Digital Scans to Dental Software Programs: A Dental Technique. J. Prosthet. Dent. 2022 doi: 10.1016/j.prosdent.2021.10.009. PubMed DOI

Logozzo S., Franceschini G., Kilpela A., Caponi M., Governi L., Blois L. A Comparative Analysis of Intraoral 3d Digital Scanners for Restorative Dentistry. Internet J. Med. Technol. 2011;5:1–18. doi: 10.5580/1b90. DOI

Oğuz E.İ., Kılıçarslan M.A., Ocak M., Bilecenoğlu B., Ekici Z. Marginal and Internal Fit of Feldspathic Ceramic CAD/CAM Crowns Fabricated via Different Extraoral Digitization Methods: A Micro-Computed Tomography Analysis. Odontology. 2021;109:440–447. doi: 10.1007/s10266-020-00560-6. PubMed DOI

Leeson D. The Digital Factory in Both the Modern Dental Lab and Clinic. Dent. Mater. 2020;36:43–52. doi: 10.1016/j.dental.2019.10.010. PubMed DOI

AI Isn’t Science Fiction: It’s Digital Design’s Reality|July 2021|Inside Dental Technology. [(accessed on 6 March 2022)]. Available online: https://www.aegisdentalnetwork.com/idt/2021/07/ai-isnt-science-fiction-its-digital-designs-reality.

Lerner H., Mouhyi J., Admakin O., Mangano F. Artificial Intelligence in Fixed Implant Prosthodontics: A Retrospective Study of 106 Implant-Supported Monolithic Zirconia Crowns Inserted in the Posterior Jaws of 90 Patients. BMC Oral Health. 2020;20:80. doi: 10.1186/s12903-020-1062-4. PubMed DOI PMC

Yamaguchi S., Lee C., Karaer O., Ban S., Mine A., Imazato S. Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI. J. Dent. Res. 2019;98:1234–1238. doi: 10.1177/0022034519867641. PubMed DOI

Takahashi T., Nozaki K., Gonda T., Ikebe K. A System for Designing Removable Partial Dentures Using Artificial Intelligence. Part 1. Classification of Partially Edentulous Arches Using a Convolutional Neural Network. J. Prosthodont. Res. 2020;65:115–118. doi: 10.2186/jpr.JPOR_2019_354. PubMed DOI

Takaichi A., Fueki K., Murakami N., Ueno T., Inamochi Y., Wada J., Arai Y., Wakabayashi N. A Systematic Review of Digital Removable Partial Dentures. Part II: CAD/CAM Framework, Artificial Teeth, and Denture Base. J. Prosthodont. Res. 2021;66:53–67. doi: 10.2186/jpr.JPR_D_20_00117. PubMed DOI

Marinello C., Brugger R. Digital Removable Complete Denture—An Overview. Curr. Oral Health Rep. 2021;8:117–131. doi: 10.1007/s40496-021-00299-1. DOI

Hein S., Modrić D., Westland S., Tomeček M. Objective Shade Matching, Communication, and Reproduction by Combining Dental Photography and Numeric Shade Quantification. J. Esthet. Restor. Dent. 2020;33:107–117. doi: 10.1111/jerd.12641. PubMed DOI

Medit I500’s Artificial Intelligence Custom Implant Identification Using The Analog Impression Abutment As A Scanbody|CAD-Ray.Com. [(accessed on 6 March 2022)]. Available online: https://www.cad-ray.com/medit-i500s-artificial-intellegence-custom-implant-identification-using-the-analog-impression-abutment-as-a-scanbdoy/

Revilla-León M., Gomez-Polo M., Vyas S., Barmak A.B., Gallucci G., Att W., Krishnamurthy V. Artificial Intelligence Applications in Implant Dentistry: A Systematic Review. J. Prosthet. Dent. 2021 doi: 10.1016/j.prosdent.2021.05.008. PubMed DOI

Lee S., Kim J.E. Evaluating the Precision of Automatic Segmentation of Teeth, Gingiva and Facial Landmarks for 2D Digital Smile Design Using Real-Time Instance Segmentation Network. J. Clin. Med. 2022;11:852. doi: 10.3390/jcm11030852. PubMed DOI PMC

Alvari G., Furlanello C., Venuti P. Is Smiling the Key? Machine Learning Analytics Detect Subtle Patterns in Micro-Expressions of Infants with ASD. J. Clin. Med. 2021;10:1776. doi: 10.3390/jcm10081776. PubMed DOI PMC

Spagnuolo G., Sorrentino R. The Role of Digital Devices in Dentistry: Clinical Trends and Scientific Evidences. J. Clin. Med. 2020;9:1692. doi: 10.3390/jcm9061692. PubMed DOI PMC

Scattarelli P., Smaniotto P., Leuci S., Cervino G., Gisotti M. The Digital Integrated Workflow in the Aesthetic Management of the Smile: A Case Report. Prosthesis. 2020;2:196–210. doi: 10.3390/prosthesis2030017. DOI

Coachman C., Calamita M., Sesma N. Dynamic Documentation of the Smile and the 2D/3D Digital Smile Design Process. Int. J. Periodontics Restor. Dent. 2017;37:183–193. doi: 10.11607/prd.2911. PubMed DOI

Mendoza G., Cornejo H., Villanueva M., Alva R., Souza A. Periodontal Plastic Surgery for Esthetic Crown Lengthening by Using Data Merging and a CAD-CAM Surgical Guide. J. Prosthet. Dent. 2020 doi: 10.1016/j.prosdent.2020.09.041. PubMed DOI

Jafri Z., Ahmad N., Sawai M., Sultan N., Bhardwaj A. Digital Smile Design-An Innovative Tool in Aesthetic Dentistry. J. Oral Biol. Craniofacial Res. 2020;10:194–198. doi: 10.1016/j.jobcr.2020.04.010. PubMed DOI PMC

Jreige C., Kimura R., Segundo Â., Coachman C., Sesma N. Esthetic Treatment Planning with Digital Animation of the Smile Dynamics: A Technique to Create a 4-Dimensional Virtual Patient. J. Prosthet. Dent. 2021 doi: 10.1016/j.prosdent.2020.10.015. PubMed DOI

Rajkomar A., Dean J., Kohane I. Machine Learning in Medicine. N. Engl. J. Med. 2019;380:1347–1358. doi: 10.1056/NEJMra1814259. PubMed DOI

Kaplan A., Cao H., FitzGerald J.M., Iannotti N., Yang E., Kocks J.W.H., Kostikas K., Price D., Reddel H.K., Tsiligianni I., et al. Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis. J. Allergy Clin. Immunol. Pract. 2021;9:2255–2261. doi: 10.1016/j.jaip.2021.02.014. PubMed DOI

Messinger A.I., Luo G., Deterding R.R. The Doctor Will See You Now: How Machine Learning and Artificial Intelligence Can Extend Our Understanding and Treatment of Asthma. J. Allergy Clin. Immunol. 2020;145:476–478. doi: 10.1016/j.jaci.2019.12.898. PubMed DOI PMC

Chen Y., Wang W., Guo Y., Zhang H., Chen Y., Xie L. A Single-Center Validation of the Accuracy of a Photoplethysmography-Based Smartwatch for Screening Obstructive Sleep Apnea. Nat. Sci. Sleep. 2021;13:1533–1544. doi: 10.2147/NSS.S323286. PubMed DOI PMC

Chen X., Xiao Y., Tang Y., Fernandez-Mendoza J., Cao G. ApneaDetector: Detecting Sleep Apnea with Smartwatches. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2021;5:1–22. doi: 10.1145/3463514. DOI

Matava C., Pankiv E., Ahumada L., Weingarten B., Simpao A. Artificial Intelligence, Machine Learning and the Pediatric Airway. Paediatr. Anaesth. 2020;30:264–268. doi: 10.1111/pan.13792. PubMed DOI

Topalovic M., Das N., Burgel P.R., Daenen M., Derom E., Haenebalcke C., Janssen R., Kerstjens H.A.M., Liistro G., Louis R., et al. Artificial Intelligence Outperforms Pulmonologists in the Interpretation of Pulmonary Function Tests. Eur. Respir. J. 2019;53:1801660. doi: 10.1183/13993003.01660-2018. PubMed DOI

Lu M.T., Ivanov A., Mayrhofer T., Hosny A., Aerts H.J.W.L., Hoffmann U. Deep Learning to Assess Long-Term Mortality from Chest Radiographs. JAMA Netw. Open. 2019;2:e197416. doi: 10.1001/jamanetworkopen.2019.7416. PubMed DOI PMC

Nam J.G., Park S., Hwang E.J., Lee J.H., Jin K.N., Lim K.Y., Vu T.H., Sohn J.H., Hwang S., Goo J.M., et al. Development and Validation of Deep Learning-Based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs. Radiology. 2019;290:218–228. doi: 10.1148/radiol.2018180237. PubMed DOI

Hwang E.J., Park S., Jin K.N., Kim J.I., Choi S.Y., Lee J.H., Goo J.M., Aum J., Yim J.J., Park C.M., et al. Development and Validation of a Deep Learning-Based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs. Clin. Infect. Dis. 2019;69:739–747. doi: 10.1093/cid/ciy967. PubMed DOI PMC

Das N., Topalovic M., Janssens W. Artificial Intelligence in Diagnosis of Obstructive Lung Disease: Current Status and Future Potential. Curr. Opin. Pulm. Med. 2018;24:117–123. doi: 10.1097/MCP.0000000000000459. PubMed DOI

Lovejoy C.A., Phillips E., Maruthappu M. Application of Artificial Intelligence in Respiratory Medicine: Has the Time Arrived? Respirology. 2019;24:1136–1137. doi: 10.1111/resp.13676. PubMed DOI

Ardila D., Kiraly A.P., Bharadwaj S., Choi B., Reicher J.J., Peng L., Tse D., Etemadi M., Ye W., Corrado G., et al. End-to-End Lung Cancer Screening with Three-Dimensional Deep Learning on Low-Dose Chest Computed Tomography. Nat. Med. 2019;25:954–961. doi: 10.1038/s41591-019-0447-x. PubMed DOI

Gonem S., Janssens W., Das N., Topalovic M. Applications of Artificial Intelligence and Machine Learning in Respiratory Medicine. Thorax. 2020;75:695–701. doi: 10.1136/thoraxjnl-2020-214556. PubMed DOI

Exarchos K.P., Beltsiou M., Votti C.A., Kostikas K. REVIEW ASTHMA Artificial Intelligence Techniques in Asthma: A Systematic Review and Critical Appraisal of the Existing Literature. Eur. Respir. J. 2020;56:2000521. doi: 10.1183/13993003.00521-2020. PubMed DOI

Abdelkarim A. Cone-Beam Computed Tomography in Orthodontics. Dent. J. 2019;7:89. doi: 10.3390/dj7030089. PubMed DOI PMC

Aboudara C., Nielsen I., Huang J.C., Maki K., Miller A.J., Hatcher D. Comparison of Airway Space with Conventional Lateral Headfilms and 3-Dimensional Reconstruction from Cone-Beam Computed Tomography. Am. J. Orthod. Dentofac. Orthop. 2009;135:468–479. doi: 10.1016/j.ajodo.2007.04.043. PubMed DOI

Richert R., Ducret M., Alliot-Licht B., Bekhouche M., Gobert S., Farges J.C. A Critical Analysis of Research Methods and Experimental Models to Study Pulpitis. Int. Endod. J. 2022;55:14–36. doi: 10.1111/iej.13683. PubMed DOI

Joda T., Zitzmann N.U. Personalized Workflows in Reconstructive Dentistry—Current Possibilities and Future Opportunities. Clin. Oral Investig. 2022;26:4283–4290. doi: 10.1007/s00784-022-04475-0. PubMed DOI PMC

Umer F., Habib S. Critical Analysis of Artificial Intelligence in Endodontics: A Scoping Review. J. Endod. 2022;48:152–160. doi: 10.1016/j.joen.2021.11.007. PubMed DOI

Khanagar S.B., Al-ehaideb A., Maganur P.C., Vishwanathaiah S., Patil S., Baeshen H.A., Sarode S.C., Bhandi S. Developments, Application, and Performance of Artificial Intelligence in Dentistry—A Systematic Review. J. Dent. Sci. 2021;16:508–522. doi: 10.1016/j.jds.2020.06.019. PubMed DOI PMC

Le V.N.T., Kang J., Oh I.-S., Kim J.-G., Yang Y.-M., Lee D.-W. Effectiveness of Human–Artificial Intelligence Collaboration in Cephalometric Landmark Detection. J. Pers. Med. 2022;12:387. doi: 10.3390/jpm12030387. PubMed DOI PMC

Shimizu Y., Tanikawa C., Kajiwara T., Nagahara H., Yamashiro T. The Validation of Orthodontic Artificial Intelligence Systems That Perform Orthodontic Diagnoses and Treatment Planning. Eur. J. Orthod. 2022:cjab083. doi: 10.1093/ejo/cjab083. PubMed DOI

Del Real A., del Real O., Sardina S., Oyonarte R. Use of Automated Artificial Intelligence to Predict the Need for Orthodontic Extractions. Korean J. Orthod. 2022;52:102. doi: 10.4041/kjod.2022.52.2.102. PubMed DOI PMC

Mohammad-Rahimi H., Motamedian S.R., Rohban M.H., Krois J., Uribe S., Nia E.M., Rokhshad R., Nadimi M., Schwendicke F. Deep Learning for Caries Detection: A Systematic Review: DL for Caries Detection. J. Dent. 2022;122:104115. doi: 10.1016/j.jdent.2022.104115. PubMed DOI

Futyma-Gabka K., Rózylo-Kalinowska I. The Use of Artificial Intelligence in Radiological Diagnosis and Detection of Dental Caries: A Systematic Review. J. Stomatol. 2021;74:262–266. doi: 10.5114/jos.2021.111664. DOI

Singh N.K., Raza K. Progress in Deep Learning-Based Dental and Maxillofacial Image Analysis: A Systematic Review. Expert Syst. Appl. 2022;199:116968. doi: 10.1016/j.eswa.2022.116968. DOI

Farook T.H., bin Jamayet N., Abdullah J.Y., Alam M.K. Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review. Pain Res. Manag. 2021;2021:6659133. doi: 10.1155/2021/6659133. PubMed DOI PMC

Devlin H., Williams T., Graham J., Ashley M. The ADEPT Study: A Comparative Study of Dentists’ Ability to Detect Enamel-Only Proximal Caries in Bitewing Radiographs with and without the Use of AssistDent Artificial Intelligence Software. Br. Dent. J. 2021;231:481–485. doi: 10.1038/s41415-021-3526-6. PubMed DOI PMC

NCT05096624 Impact of Complete Removable Prosthetic Rehabilitations Performed by an Innovative DDTENS Protocol, on the Quality of Masticatory Function and the Management of Completely Edentulous Patients. [(accessed on 6 March 2022)];2021 Available online: https://clinicaltrials.gov/show/NCT05096624.

Mixed reality-based technology to visualize and facilitate treatment planning of impacted teeth: Proof of concept

Pierre Robin Sequence and 3D Printed Personalized Composite Appliances in Interdisciplinary Approach