Artificial Intelligence in the Detection of Clinically Negotiable Second Mesio‐Buccal Canals in Periapical Images of Maxillary Molars

AIM: Artificial intelligence (AI) has the potential to aid clinicians in assessing case difficulty in endodontics. The objectives of this study were to develop and validate deep learning models for the detection of clinically negotiable MB2 canals in periapical images of maxillary first and second molars, and to compare the performance of AI models with that of human clinicians. METHODOLOGY: A total of 1504 pre-operative periapical images of maxillary first and second molars that were treated by endodontic specialists were collected with clinical data as to the presence or absence of a clinically negotiable MB2 canal. Six pretrained supervised convolutional neural networks (ResNet-18, ResNet-50, ResNeXt-101, VGG-16, DenseNet-121 and MobileNetV2) and three self-supervised models (DINO, SimCLR and BYOL) were fine-tuned using fivefold cross-validation. Model performance was evaluated on a hold-out test set using accuracy, precision, sensitivity, specificity, and F1-score with 95% confidence intervals. Three independent clinicians (an endodontist, an endodontic resident, and an oral and maxillofacial radiologist) also assessed the test set. RESULTS: In cross-validation, ResNet-50 achieved the highest mean accuracy (67.6%), while DINO was the top-performing self-supervised model (62.8%). ResNet-18, ResNet-50, ResNeXt-101, DenseNet-121 and DINO significantly outperformed BYOL (p < 0.01), while no significant differences were observed among the top-performing models. ResNet-18 achieved the highest accuracy at 66.0% (95% CI, 63.0-68.9) on the test set while human expert accuracy ranged from 53.6% to 61.4%. Stratified analysis showed a general trend for improved AI model performance in maxillary first molars and in teeth without full-crown restorations. There was no significant difference in the accuracy of the top-performing AI model and human experts (p > 0.05). CONCLUSION: Deep learning models performed similarly to clinician experts in identifying clinically negotiable MB2 canals in periapical images of maxillary first and second molars. These findings support the potential role of AI in endodontic case difficulty assessment.