Deep-learning network for automated evaluation of root-canal filling radiographic quality
Liuli Jin, Bingran Du, Zineng Xu, Hailong Bai, Peng Ding, Ze Zhang, Yaopeng Pan, Yuan Lin, Zhiwen Li, Xiaohui Rausch‐Fan, Fei Hu, Xueyang Zhang
Abstract
BACKGROUND: Deep-learning networks are promising techniques in dentistry. This study developed and validated a deep-learning network, You Only Look Once (YOLO) v5, for the automatic evaluation of root-canal filling quality on periapical radiographs. METHODS: YOLOv5 was developed using 1,008 periapical radiographs (training set: 806, validation set: 101, testing set: 101) from one center and validated on an external data set of 500 periapical radiographs from another center. We compared the network's performance with that of inexperienced endodontist in terms of recall, precision, F1 scores, and Kappa values, using the results from specialists as the gold standard. We also compared the evaluation durations between the manual method and the network. RESULTS: On the external test data set, the YOLOv5 network performed better than inexperienced endodontist in terms of overall comprehensive performance. The F1 index values of the network for correct and incorrect filling were 92.05% and 82.93%, respectively. The network outperformed the inexperienced endodontist in all tooth regions, especially in the more difficult-to-assess upper molar regions. Notably, the YOLOv5 network evaluated images 150-220 times faster than manual evaluation. CONCLUSIONS: The YOLOv5 deep learning network provided clinicians with a new, relatively accurate and efficient auxiliary tool for assessing the radiological quality of root canal fillings, enhancing work efficiency with large sample sizes. However, its use should be complemented by clinical expertise for accurate evaluations.