ArticleBMC oral health2026
Deep learning models for the detection of dental-findings and tooth-types using video data.
Article in BMC oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundOral diseases have an estimated incidence of 35.5% worldwide, yet access to dental care is often hindered by a shortage of dental professionals and limited diagnostic resources. While Artificial Intelligence (AI) has shown promise in dental diagnostics, most existing models rely on high-quality static images captured in controlled clinical settings. This study aimed to develop and validate Deep Learning (DL) models capable of detecting dental findings and identifying tooth types using video data from a handheld, toothbrush-mounted intraoral device.
methodsIn this validation study, 708 videos were captured using an electronic toothbrush integrated with a miniature intraoral camera. A custom python script extracted 16,552 image frames, which were filtered to 7,963 eligible frames for the dental-findings model and 3,799 frames for the tooth-type model. Two dental experts annotated findings, including decay, fillings, plaque, and staining as well as tooth types using bounding boxes. The YOLOv8s architecture was employed for both the models.
resultsThe dental-findings model achieved an overall Mean Average Precision (mAP) of 83.0% and an F1-score of 77.9%. It performed best in detecting amalgam fillings (94.5% mAP) and decay (84.2% mAP). The tooth-type model demonstrated an overall mAP of 88.6% and F-1 score of 86.2%. The highest mAP was observed for molars (97.7%) and premolars (96.7%). Despite the lack of a standardized video capture sequence, the models maintained strong performance across diverse visual variations.
conclusionThis study establishes a proof of concept for embedding AI diagnostics into everyday oral care devices. By accurately detecting clinical findings from user-captured video, this technology has the potential to facilitate remote monitoring, support early intervention, and optimize healthcare resource allocation, particularly in underserved regions.
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