Kunz, Felix and Stellzig-Eisenhauer, Angelika and Zeman, Florian and Boldt, Julian (2020) Artificial intelligence in orthodontics Evaluation of a fully automated cephalometric analysis using a customized convolutional neural network. JOURNAL OF OROFACIAL ORTHOPEDICS-FORTSCHRITTE DER KIEFERORTHOPADIE, 81 (1). pp. 52-68. ISSN 1434-5293, 1615-6714
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Purpose The aim of this investigation was to create an automated cephalometric X-ray analysis using a specialized artificial intelligence (AI) algorithm. We compared the accuracy of this analysis to the current gold standard (analyses performed by human experts) to evaluate precision and clinical application of such an approach in orthodontic routine. Methods For training of the network, 12 experienced examiners identified 18 landmarks on a total of 1792 cephalometric X-rays. To evaluate quality of the predictions of the AI, both AI and each examiner analyzed 12 commonly used orthodontic parameters on a basis of 50 cephalometric X-rays that were not part of the training data for the AI. Median values of the 12 examiners for each parameter were defined as humans' gold standard and compared to the AI's predictions. Results There were almost no statistically significant differences between humans' gold standard and the AI's predictions. Differences between the two analyses do not seem to be clinically relevant. Conclusions We created an AI algorithm able to analyze unknown cephalometric X-rays at almost the same quality level as experienced human examiners (current gold standard). This study is one of the first to successfully enable implementation of AI into dentistry, in particular orthodontics, satisfying medical requirements.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | LANDMARKING; IMAGES; Deep learning; Algorithms; Machine learning; Medical imaging; Cephalometric X-rays |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Zentren des Universitätsklinikums Regensburg > Zentrum für Klinische Studien |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 08 Apr 2021 06:53 |
| Last Modified: | 08 Apr 2021 06:53 |
| URI: | https://pred.uni-regensburg.de/id/eprint/45522 |
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