Artificial Intelligence in Endodontics: Current Applications and Future Directions

被引:78
|
作者
Aminoshariae, Anita [1 ]
Kulild, Jim [2 ]
Nagendrababu, Venkateshbabu [3 ]
机构
[1] Case Sch Dent Med, Dept Endodont, 10900 Euclid Ave, Cleveland, OH 44124 USA
[2] Univ Missouri, Sch Dent, Dept Endodont, Kansas City, MO USA
[3] Univ Sharjah, Coll Dent Med, Dept Prevent & Restorat Dent, Sharjah, U Arab Emirates
关键词
Artificial intelligence; artificial neural networks; convolutional neural networks; endodontics; BEAM COMPUTED-TOMOGRAPHY; MINOR APICAL FORAMEN; DIAGNOSIS; LESIONS; RADIOGRAPHY; NETWORK;
D O I
10.1016/j.joen.2021.06.003
中图分类号
R78 [口腔科学];
学科分类号
1003 ;
摘要
Introduction: Artificial intelligence (AI) has the potential to replicate human intelligence to perform prediction and complex decision making in health care and has significantly increased its presence and relevance in various tasks and applications in dentistry, especially endodontics. The aim of this review was to discuss the current endodontic applications of AI and potential future directions. Methods: Articles that have addressed the applications of AI in endodontics were evaluated for information pertinent to include in this narrative review. Results: AI models (eg, convolutional neural networks and/or artificial neural networks) have demonstrated various applications in endodontics such as studying root canal system anatomy, detecting periapical lesions and root fractures, determining working length measurements, predicting the viability of dental pulp stem cells, and predicting the success of retreatment procedures. The future of this technology was discussed in light of helping with scheduling, treating patients, drug-drug interactions, diagnosis with prognostic values, and robotic-assisted endodontic surgery. Conclusions: AI demonstrated accuracy and precision in terms of detection, determination, and disease prediction in endodontics. AI can contribute to the improvement of diagnosis and treatment that can lead to an increase in the success of endodontic treatment outcomes. However, it is still necessary to further verify the reliability, applicability, and cost-effectiveness of AI models before transferring these models into day-to-day clinical practice. (J Endod 2021;47:1352-1357.)
引用
收藏
页码:1352 / 1357
页数:6
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