Automatic assessment of root numbers of vertical mandibular third molar using a deep learning model based on attention mechanism
Objective To develop a deep learning network based on attention mechanism to identify the number of the vertical man-dibular third molar(MTM)roots(single or double)on panoramic radiographs in an automatic way.Methods The sample consisted of 1 045 patients with 1 642 MTMs on paired panoramic radiographs and Cone-beam computed tomography(CBCT)and were randomly grouped into the training(80%),the validation(10%),and the test(10%).The evaluation of CBCT was defined as the ground truth.A deep learning network based on attention mechanism,which was named as RN-MTMnet,was trained to judge if the MTM on pano-ramic radiographs had one or two roots.Diagnostic performance was evaluated by accuracy,sensitivity,specificity,and positive predict value(PPV),and the receiver operating characteristic(ROC)curve with the area under the ROC curve(AUC).Its diagnostic perform-ance was compared with dentists'diagnosis,Faster-RCNN,CenterNet,and SSD using evaluation metrics.Results On CBCT images,single-rooted MTM was observed on 336(20.46%)sides,while two-rooted MTM was 1 306(79.54%).The RN-MTMnet achieved an accuracy of 0.888,a sensitivity of 0.885,a specificity of 0.903,a PPV of 0.976,and the AUC value of 0.90.Conclusion RN-MTM-net is developed as a novel,robust and accurate method for detecting the numberof MTM roots on panoramic radiographs.
mandibular third molarpanoramic radiographyCBCTdeep learningattention mechanism
NETL
NSTL
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