A Forensic Odontology Application: Impact of Image Quality on CNNs for Dental Status Analysis from Orthopantomograms

Artificial Intelligence, especially Convolutional Neural Networks (CNN), is gaining importance in health sciences, including forensic odontology. This study aimed to systematically analyze elements for automated dental status registration on OPGs using CNNs, on different image segments and resolutions. A dataset of 1400 manually annotated digital OPGs was divided into train, test, and validation sets (75%–12.5%–12.5%). Pre-trained and from-scratch models were developed and evaluated on images from full OPGs to individual and segmented teeth and sizes from 256 px to 1820 px. Performance was measured by Sørensen–Dice coefficient for segmentation and mean average precision (mAP) for detection. For segmentation, the UNet Big model was the most successful, using segmented or individual images, achieving 89.14% for crown and 84.90% for fillings, and UNet with 79.09% for root canal fillings. Caries presented a significant challenge, with the UNet model achieving the highest score of 64.68%. In detection, YOLOv5x6, trained from scratch, achieved the highest mAP of 98.02% on 1820 px images. Larger image resolutions and individual tooth inputs significantly improved performance. This study confirms the success of CNN models in specific tasks on OPGs. Image quality and input (individual tooth, resolutions above 640 px) critically influenced model competence. Further research with from-scratch models, higher resolutions, and smaller image segments is recommended.