Background Introducing artificial intelligence (AI) into the medical field proved beneficial in automating tasks and streamlining the practitioners’ lives. Hence, this study was conducted to design and evaluate an AI tool called Make Sure Caries Detector and Classifier (MSc) for detecting pathological exposure of pulp on digital periapical radiographs and to compare its performance with dentists. Methods This study was a diagnostic, multi-centric study, with 3461 digital periapical radiographs from three countries and seven centers. MSc was built using Yolov5-x model, and it was used for exposed and unexposed pulp detection. The dataset was split into a train, validate, and test dataset; the ratio was 8–1-1 to prevent overfitting. 345 images with 752 labels were randomly allocated to test MSc. The performance metrics used to test MSc performance included mean average precision (mAP), precision, F1 score, recall, and area under receiver operating characteristic curve (AUC). The metrics used to compare the performance with that of 10 certified dentists were: right diagnosis exposed (RDE), right diagnosis not exposed (RDNE), false diagnosis exposed (FDE), false diagnosis not exposed (FDNE), missed diagnosis (MD), and over diagnosis (OD). Results MSc achieved a performance of more than 90% in all metrics examined: an average precision of 0.928, recall of 0.918, F1-score of 0.922, and AUC of 0.956 (P<.05). The results showed a higher mean of 1.94 for all right (correct) diagnosis parameters in MSc group, while a higher mean of 0.64 for all wrong diagnosis parameters in the dentists group (P<.05). Conclusions The designed MSc tool proved itself reliable in the detection and differentiating between exposed and unexposed pulp in the internally validated model. It also showed a better performance for the detection of exposed and unexposed pulp when compared to the 10 dentists’ consensus.

Background Introducing artificial intelligence into the medical field proved to be beneficial in automating tasks and streamlining the practitioners’ lives. Hence, this study was conducted to design and evaluate an Artificial Intelligence (AI) tool called Make Sure Caries Detector and Classifier (MSc) for detecting pathological exposure of pulp on digital periapical radiographs and to compare its performance with dentists. Methods This study was a retrospective multi-centric study, with over 3461 digital periapical radiographs from different countries’ and centers. MSc was built using Yolov5-x model, which was used for exposed and unexposed pulp caries detection. The dataset was split into train, validate, and test datasets; the ratio was 8-1-1 to prevent overfitting. 345 images with 752 labels were randomly allocated to test MSc. The performance metrics used to test MSc performance included mean average precision (mAP), precision, F1 score, recall, and area under receiver operating characteristic Curve (AUC). The metrics used to compare the performance with that of 10 certified dentists were: right detection exposed, right detection not exposed, false detection exposed, false detection not exposed, missed diagnosis, and over diagnosis. Results MSc achieved a performance of more than 90% in all metrics examined: an average precision of 0.928, recall of 0.918, F1-score of 0.922, and an mAP@ .5 (AUC) of 0.956 (P < .05). The results showed a higher mean for all right diagnosis parameters in MSc group while a higher mean for all wrong diagnosis parameters in the dentists group (P < .05). Conclusions The designed MSc tool proved itself reliable in the detection and differentiating between Exposed and Unexposed pulp. It also showed a better performance when compared to the 10 dentists.