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The optimal diagnostic assistance system for predicting three-dimensional contact between mandibular third molars and
Motoki Fukuda1, Daisuke Nomoto2, Michihito Nozawa3
1Department of Oral Radiology, Osaka Dental University, 1-5-17 Otemae, Chuo-ku, Osaka, Japan. fukuda-m@cc.osaka-dent.ac.jp.
Objectives:
This study aimed to identify the most effective diagnostic assistance system for assessing the relationship between mandibular third molars (M3M) and mandibular canals (MC) using panoramic radiographs.
Methods:
In total, 2,103 M3M were included from patients in whom the M3M and MC overlapped on panoramic radiographs. All M3M were classified into high-risk and low-risk groups based on the degree of contact with the MC observed on computed tomography. The contact classification was evaluated using four machine learning models (Prediction One software, AdaBoost, XGBoost, and random forest), three convolutional neural networks (CNNs) (EfficientNet-B0, ResNet18, and Inception v3), and three human observers (two radiologists and one oral surgery resident). Receiver operating characteristic curves were plotted; the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated. Factors contributing to prediction of high-risk cases by machine learning models were identified.
Results:
Machine learning models demonstrated AUC values ranging from 0.84 to 0.88, with accuracy ranging from 0.81 to 0.88 and sensitivity of 0.80, indicating consistently strong performance. Among the CNNs, ResNet18 achieved the best performance, with an AUC of 0.83. The human observers exhibited AUC values between 0.67 and 0.80. Three factors were identified as contributing to prediction of high-risk cases by machine learning models: increased root radiolucency, diversion of the MC, and narrowing of the MC.
Conclusion:
Machine learning models demonstrated strong performance in predicting the three-dimensional relationship between the M3M and MC.
