Deep learning-based localization of bounded edentulous spaces in intraoral occlusal images

Objectives This study developed a deep learning pipeline to localize bounded edentulous spaces with missing teeth FDI number directly from intraoral photographs, eliminating reliance on radiographs or controlled imaging.

A total of 4,373 intraoral occlusal images were included in the study. Of these, 367 images with at least one bounded edentulous space were held out for pipeline evaluation. The remaining 4,006 images were split into training (80%), validation (10%), and test (10%) sets for model development. The pipeline included ResNet-18, YOLOv8m and ResNet-101. Bounded edentulous were inferred based on FDI discontinuities and localized via spatial interpolation.

The pipeline was evaluated on a test set of 367 occlusal images, comprising 4,964 expected teeth. This equated to a precision of 88.2%, recall of 89.1%, an F1-score of 88.6%, and MCC of 0.875. The pipeline showed stable diagnostic performance for actual use cases and made full inference in 9 seconds per image on consumer-grade hardware, indicating its viability for use with standard clinical workflows.

The pipeline showed reliable ability to detect and localize bounded edentulous spaces. Its consistent performance across a wide range of different clinical cases justifies its use as a diagnostic tool in routine dental practice. Clinical Significance Our results show that accurate detection of edentulous spaces is now possible even from unstandardized clinical images and is less dependent on radiographs and manual charting. This simplifies workflows in day-to-day practice, aids precision in orthodontic and implant planning, and makes AI tools more accessible for use in those practices with minimal image infrastructure. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study did not receive any funding Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics Committee/IRB of I.M. Sechenov First Moscow State Medical University gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability All data produced in the present study are available upon reasonable request to the authors