Artificial Intelligence in Endodontics: Current Applications and Future Directions

IntroductionArtificial 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.MethodsArticles that have addressed the applications of AI in endodontics were evaluated for information pertinent to include in this narrative review.ResultsAI 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.ConclusionsAI 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.     

Artificial Intelligence for Fast and Accurate 3-Dimensional Tooth Segmentation on Cone-beam Computed Tomography

IntroductionTooth segmentation on cone-beam computed tomographic (CBCT) imaging is a labor-intensive task considering the limited contrast resolution and potential disturbance by various artifacts. Fully automated tooth segmentation cannot be achieved by merely relying on CBCT intensity variations. This study aimed to develop and validate an artificial intelligence (AI)-driven tool for automated tooth segmentation on CBCT imaging.MethodsA total of 433 Digital Imaging and Communications in Medicine images of single- and double-rooted teeth randomly selected from 314 anonymized CBCT scans were imported and manually segmented. An AI-driven tooth segmentation algorithm based on a feature pyramid network was developed to automatically detect and segment teeth, replacing manual user contour placement. The AI-driven tool was evaluated based on volume comparison, intersection over union, the Dice score coefficient, morphologic surface deviation, and total segmentation time.ResultsOverall, AI-driven and clinical reference segmentations resulted in very similar segmentation volumes. The mean intersection over union for full-tooth segmentation was 0.87 (±0.03) and 0.88 (±0.03) for semiautomated (SA) (clinical reference) versus fully automated AI-driven (F-AI) and refined AI-driven (R-AI) tooth segmentation, respectively. R-AI and F-AI segmentation showed an average median surface deviation from SA segmentation of 9.96 μm (±59.33 μm) and 7.85 μm (±69.55 μm), respectively. SA segmentations of single- and double-rooted teeth had a mean total time of 6.6 minutes (±76.15 seconds), F-AI segmentation of 0.5 minutes (±8.64 seconds, 12 times faster), and R-AI segmentation of 1.2 minutes (±33.02 seconds, 6 times faster).ConclusionsThis study showed a unique fast and accurate approach for AI-driven automated tooth segmentation on CBCT imaging. These results may open doors for AI-driven applications in surgical and treatment planning in oral health care.     

Evaluation of automated cephalometric analysis based on the latest deep learning method

ObjectivesTo compare an automated cephalometric analysis based on the latest deep learning method of automatically identifying cephalometric landmarks (AI) with previously published AI according to the test style of the worldwide AI challenges at the International Symposium on Biomedical Imaging conferences held by the Institute of Electrical and Electronics Engineers (IEEE ISBI).Materials and MethodsThis latest AI was developed by using a total of 1983 cephalograms as training data. In the training procedures, a modification of a contemporary deep learning method, YOLO version 3 algorithm, was applied. Test data consisted of 200 cephalograms. To follow the same test style of the AI challenges at IEEE ISBI, a human examiner manually identified the IEEE ISBI-designated 19 cephalometric landmarks, both in training and test data sets, which were used as references for comparison. Then, the latest AI and another human examiner independently detected the same landmarks in the test data set. The test results were compared by the measures that appeared at IEEE ISBI: the success detection rate (SDR) and the success classification rates (SCR).ResultsSDR of the latest AI in the 2-mm range was 75.5% and SCR was 81.5%. These were greater than any other previous AIs. Compared to the human examiners, AI showed a superior success classification rate in some cephalometric analysis measures.ConclusionsThis latest AI seems to have superior performance compared to previous AI methods. It also seems to demonstrate cephalometric analysis comparable to human examiners.     

Critical Analysis of Artificial Intelligence in Endodontics: A Scoping Review

IntroductionArtificial intelligence (AI) comprises computational models that mimic the human brain to perform various diagnostic tasks in clinical practice. The aim of this scoping review was to systematically analyze the AI algorithms and models used in endodontics and identify the source quality and type of evidence.MethodsA literature search was conducted in October 2020 to identify the relevant literature in English language in the 4 major health sciences databases, ie, MEDLINE, Dentistry & Oral Science, CINAHL Plus, and Cochrane Library. Our review questions were the following: what are the different AI algorithms and models used in endodontics?, what are the datasets being used?, what type of performance metrics were reported?, and what diagnostic performance measures were used?. The quality of the included studies was evaluated by a modified Quality Assessment of Studies of Diagnostic Accuracy risk (QUADAS) tool.ResultsOut of 300 studies, 12 articles met our inclusion criteria and were subjected to final analysis. Among the included studies, 6 studies focused on periapical pathology, and 3 studies investigated vertical root fractures. Most studies (n = 10) used neural networks, among which convolutional neural networks were commonly used. The datasets that were mostly studied were radiographs. Out of 12 studies, only 3 studies achieved a high score according to the modified QUADAS tool.ConclusionsAI models had acceptable performance, ie, accuracy >90% in executing various diagnostic tasks. The scientific reporting of AI-related research is irregular. The endodontic community needs to implement recommended guidelines to improve the weaknesses in the current planning and reporting of AI-related research to improve its scientific vigor.