Lin H, Chen J, Hu Y, Li W. Embracing technological revolution: A panorama of machine learning in dentistry. Med Oral Patol Oral Cir Bucal. 2024 Nov 1;29 (6):e742-9.
doi:10.4317/medoral.26679
https://dx.doi.org/doi:10.4317/medoral.26679
1. Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Biol. 2021;23:40-55. |
PMid:34518686 |
2. Khanagar SB, Al-ehaideb A, Maganur PC, Vishwanathaiah S, Patil S, Baeshen HA, et al. Developments, application, and performance of artificial intelligence in dentistry - A systematic review. J Dent Sci. 2021;16:508-22. |
PMid:33384840 PMCid:PMC7770297 |
3. Kuhnisch J, Meyer O, Hesenius M, Hickel R, Gruhn V. Caries Detection on Intraoral Images Using Artificial Intelligence. J Dent Res. 2022;101:158-65. |
PMid:34416824 PMCid:PMC8808002 |
4. Zhu H, Cao Z, Lian L, Ye G, Gao H, Wu J. CariesNet: a deep learning approach for segmentation of multi-stage caries lesion from oral panoramic X-ray image. Neural Comput Appl. 2022. |
PMid:35017793 PMCid:PMC8736291 |
5. Lee S, Oh SI, Jo J, Kang S, Shin Y, Park JW. Deep learning for early dental caries detection in bitewing radiographs. Sci Rep. 2021;11:16807. |
PMid:34413414 PMCid:PMC8376948 |
6. Toledo Reyes L, Knorst JK, Ortiz FR, Brondani B, Emmanuelli B, Saraiva Guedes R, et al. Early Childhood Predictors for Dental Caries: A Machine Learning Approach. J Dent Res. 2023;102:999-1006. |
PMid:37246832 |
7. Hu Z, Cao D, Hu Y, Wang B, Zhang Y, Tang R, et al. Diagnosis of in vivo vertical root fracture using deep learning on cone-beam CT images. BMC Oral Health. 2022;22:382. |
PMid:36064682 PMCid:PMC9446797 |
8. Yuce F, Öziç MÜ, Tassoker M. Detection of pulpal calcifications on bite-wing radiographs using deep learning. Clin Oral Investig. 2022;27:2679-89. |
PMid:36564651 |
9. Zheng L, Wang H, Mei L, Chen Q, Zhang Y, Zhang H. Artificial intelligence in digital cariology: a new tool for the diagnosis of deep caries and pulpitis using convolutional neural networks. Ann Transl Med. 2021;9:763. |
PMid:34268376 PMCid:PMC8246233 |
10. Ver Berne J, Saadi SB, Politis C, Jacobs R. A deep learning approach for radiological detection and classification of radicular cysts and periapical granulomas. J Dent. 2023;135:104581. |
PMid:37295547 |
11. Yang S, Lee H, Jang B, Kim KD, Kim J, Kim H, et al. Development and Validation of a Visually Explainable Deep Learning Model for Classification of C-shaped Canals of the Mandibular Second Molars in Periapical and Panoramic Dental Radiographs. J Endod. 2022;48:914-21. |
PMid:35427635 |
12. Li W, Liang Y, Zhang X, Liu C, He L, Miao L, et al. A deep learning approach to automatic gingivitis screening based on classification and localization in RGB photos. Sci Rep. 2021;11:16831. |
PMid:34413332 PMCid:PMC8376991 |
13. Kim EH, Kim S, Kim HJ, Jeong Ho, Lee J, Jang J, et al. Prediction of Chronic Periodontitis Severity Using Machine Learning Models Based On Salivary Bacterial Copy Number. Front Cell Infect Microbiol. 2020;10:571515. |
PMid:33304856 PMCid:PMC7701273 |
14. Troiano G, Nibali L, Petsos H, Eickholz P, Saleh MHA, Santamaria P, et al. Development and international validation of logistic regression and machine‐learning models for the prediction of 10‐year molar loss. J Clin Periodontol. 2022;50:348-57. |
PMid:36305042 |
15. Lee CT, Kabir T, Nelson J, Sheng S, Meng HW, Van Dyke TE, et al. Use of the deep learning approach to measure alveolar bone level. J Clin Periodontol. 2021;49:260-9. |
PMid:34879437 PMCid:PMC9026777 |
16. Idrees M, Farah CS, Shearston K, Kujan O. A machine‐learning algorithm for the reliable identification of oral lichen planus. J Oral Pathol Med. 2021;50:946-53. |
PMid:34358361 |
17. Zhou L, Wang H, Zhang H, Wang F, Wang W, Cao Q, et al. Diagnostic markers and potential therapeutic agents for Sjögren's syndrome screened through multiple machine learning and molecular docking. Clin Exp Immunol. 2023;212:224-38. |
PMid:36988140 PMCid:PMC10243915 |
18. Cai X, Li L, Yu F, Guo R, Zhou X, Zhang F, et al. Development of a Pathomics-Based Model for the Prediction of Malignant Transformation in Oral Leukoplakia. Lab Invest. 2023;103:100173. |
PMid:37164265 |
19. Kim MJ, Kim PJ, Kim HG, Kho HS. Prediction of treatment outcome in burning mouth syndrome patients using machine learning based on clinical data. Sci Rep. 2021;11:15396. |
PMid:34321575 PMCid:PMC8319111 |
20. Suhail S, Harris K, Sinha G, Schmidt M, Durgekar S, Mehta S, et al. Learning Cephalometric Landmarks for Diagnostic Features Using Regression Trees. Bioengineering (Basel). 2022;9:617. |
PMid:36354530 PMCid:PMC9687964 |
21. Suhail Y, Upadhyay M, Chhibber A, Kshitiz. Machine Learning for the Diagnosis of Orthodontic Extractions: A Computational Analysis Using Ensemble Learning. Bioengineering (Basel). 2020;7:55. |
PMid:32545428 PMCid:PMC7355468 |
22. Prasad J, Mallikarjunaiah DR, Shetty A, Gandedkar N, Chikkamuniswamy AB, Shivashankar PC. Machine Learning Predictive Model as Clinical Decision Support System in Orthodontic Treatment Planning. Dent J (Basel). 2022;11:1. |
PMid:36661538 PMCid:PMC9858447 |
23. El Bsat AR, Shammas E, Asmar D, Sakr GE, Zeno KG, Macari AT, et al. Semantic Segmentation of Maxillary Teeth and Palatal Rugae in Two-Dimensional Images. Diagnostics (Basel). 2022;12:2176. |
PMid:36140577 PMCid:PMC9498073 |
24. Park YS, Choi JH, Kim Y, Choi SH, Lee JH, Kim KH, et al. Deep Learning-Based Prediction of the 3D Postorthodontic Facial Changes. J Dent Res. 2022;101:1372-9. |
PMid:35774018 |
25. Wang X, Zhao X, Song G, Niu J, Xu T. Machine Learning-Based Evaluation on Craniodentofacial Morphological Harmony of Patients After Orthodontic Treatment. Front Physiol. 2022;13:862847. |
PMid:35615666 PMCid:PMC9124867 |
26. Kim H, Shim E, Park J, Kim YJ, Lee U, Kim Y. Web-based fully automated cephalometric analysis by deep learning. Comput Methods Programs Biomed. 2020;194:105513. |
PMid:32403052 |
27. Engels P, Meyer O, Schönewolf J, Schlickenrieder A, Hickel R, Hesenius M, et al. Automated detection of posterior restorations in permanent teeth using artificial intelligence on intraoral photographs. J Dent. 2022;121:104124. |
PMid:35395346 |
28. 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. 2021;65:115-8. |
PMid:32938860 |
29. Cui Q, Chen Q, Liu P, Liu D, Wen Z. Clinical decision support model for tooth extraction therapy derived from electronic dental records. J Prosthet Dent. 2021;126:83-90. |
PMid:32703604 |
30. Yu D, Hu J, Feng Z, Song M, Zhu H. Deep learning based diagnosis for cysts and tumors of jaw with massive healthy samples. Sci Rep. 2022;12:1855. |
PMid:35115624 PMCid:PMC8814152 |
31. Fu Q, Chen Y, Li Z, Jing Q, Hu C, Liu H, et al. A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study. EClinicalMedicine. 2020;27:100558. |
PMid:33150326 PMCid:PMC7599313 |
32. McRae MP, Modak SS, Simmons GW, Trochesset DA, Kerr AR, Thornhill MH, et al. Point‐of‐care oral cytology tool for the screening and assessment of potentially malignant oral lesions. Cancer Cytopathol. 2020;128:207-20. |
PMid:32032477 PMCid:PMC7078980 |
33. Howard FM, Kochanny S, Koshy M, Spiotto M, Pearson AT. Machine Learning-Guided Adjuvant Treatment of Head and Neck Cancer. JAMA Netw Open. 2020;3:e2025881. |
PMid:33211108 PMCid:PMC7677764 |
34. Schwendicke F, Samek W, Krois J. Artificial Intelligence in Dentistry: Chances and Challenges. J Dent Res. 2020;99:769-74. |
PMid:32315260 PMCid:PMC7309354 |
35. Wiens J, Shenoy ES. Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology. Clin Infect Dis. 2018;66:149-53. |
PMid:29020316 PMCid:PMC5850539 |
36. Narwane SV, Sawarkar SD. Is handling unbalanced datasets for machine learning uplifts system performance?: A case of diabetic prediction. Diabetes Metab Syndr. 2022;16:102609. |
PMid:36099677 |
37. Magrabi F, Ammenwerth E, McNair JB, De Keizer NF, Hypponen H, Nykanen P, et al. Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications. Yearb Med Inform. 2019;28:128-34. |
PMid:31022752 PMCid:PMC6697499 |