Evaluation of Deep Learning Model for Tooth Number Recognition from Smartphone Photographs

Objectives Tooth numbering plays a significant role in dental care. The aim of this study was to evaluate the Mask Region-based Convolutional Neural Network (Mask R-CNN) model for tooth number detection, classification, and segmentation from smartphone photographs. Materials and Methods This retrospective study utilized the data collected in Finland during 2022. A total of 448 individuals, aged 13–78 years, participated in this study. Participants uploaded dental photographs taken by smartphone. The dataset consisted of upper and lower occlusal, right and left lateral and frontal views. Teeth were labelled using polygon annotation for the FDI World Dental Federation (FDI) notation. The final number of images and tooth polygons were 1,272 and 14,736 respectively. The Mask R-CNN model was used for tooth number recognition. Performance of trained models was measured based on the bounding box and class prediction for 28 tooth numbers, using PASCAL performance metric’s open toolset. The mean average precision (mAP) was calculated for each class with an intersection over union (IoU) threshold of 0.5. External validation was conducted using open Mendeley dataset from India, consisting of 40 preliminary selected images. Results The tooth number recognition model demonstrated an overall mAP of 88.2%, with a standard deviation of 0.0075 on 10-fold cross validation, which indicates stable performance of the model. The external validation showed excellent performance of the model, with a mAP of 84.1%. Conclusions The Mask R-CNN model developed in this study demonstrated excellent performance and good generalizability. Clinical Relevance: The developed mHealth system facilitates remote dental screening in real-world community settings.