Automatic detection of orthodontically induced external root resorption based on deep convolutional neural networks using CBCT images.

Orthodontically-induced external root resorption (OIERR) is among the most common risks in orthodontic treatment. Traditional OIERR diagnosis is limited by subjective judgement as well as cumbersome manual measurement. The research aims to develop an intelligent detection model for OIERR based on deep convolutional neural networks (CNNs) through cone-beam computed tomography (CBCT) images, thus providing auxiliary diagnosis support for orthodontists. Six pretrained CNN architectures were adopted and 1717 CBCT slices were used for training to construct OIERR detection models. The performance of the models was tested on 429 CBCT slices and the activated regions during decision-making were visualized through heatmaps. The model performance was then compared with that of two orthodontists. The EfficientNet-B1 model, trained through hold-out cross-validation, proved to be the most effective for detecting OIERR. Its accuracy, precision, sensitivity, specificity as well as F1-score were 0.97, 0.98, 0.97, 0.98 and 0.98, respectively. The metrics remarkably outperformed those of orthodontists, whose accuracy, recall and F1-score were 0.86, 0.78, and 0.87 respectively (P < 0.01). The heatmaps suggested that the OIERR detection model primarily relied on root features for decision-making. Automatic detection of OIERR through CNNs as well as CBCT images is both accurate and efficient. The method outperforms orthodontists and is anticipated to serve as a clinical tool for the rapid screening and diagnosis of OIERR.