Estimating Periodontal Stability Using Computer Vision.
Authors
Affiliations (10)
Affiliations (10)
- Department of Oral Medicine, Infection, and Immunity, Harvard School of Dental Medicine, Boston, MA, USA.
- ITU/WHO/WIPO Global Initiative on Artificial Intelligence for Health, Geneva, Switzerland.
- Department of Oral Surgery, University Clinic of Dentistry, Medical University of Vienna, Vienna, Austria.
- Department of Oral Biology, University Clinic of Dentistry, Medical University of Vienna, Vienna, Austria.
- Core for Computational Biomedicine, Department for Biomedical Informatics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.
- Department of Oral Health Policy and Epidemiology, Harvard School of Dental Medicine, Boston, MA, USA.
- Initiative to Integrate Oral Health and Medicine, Harvard School of Dental Medicine, Boston, MA, USA.
- Department of Restorative Dentistry and Biomaterials Sciences, Harvard School of Dental Medicine, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Dental Research Division, Guarulhos University, Guarulhos, Brazil.
Abstract
Periodontitis is a severe infection affecting oral and systemic health and is traditionally diagnosed through clinical probing-a process that is time-consuming, uncomfortable for patients, and subject to variability based on the operator's skill. We hypothesized that computer vision can be used to estimate periodontal stability from radiographs alone. At the tooth level, we used intraoral radiographs to detect and categorize individual teeth according to their periodontal stability and corresponding treatment needs: healthy (prevention), stable (maintenance), and unstable (active treatment). At the patient level, we assessed full-mouth series and classified patients as stable or unstable by the presence of at least 1 unstable tooth. Our 3-way tooth classification model achieved an area under the receiver operating characteristic curve of 0.71 for healthy teeth, 0.56 for stable, and 0.67 for unstable. The model achieved an F<sub>1</sub> score of 0.45 for healthy teeth, 0.57 for stable, and 0.54 for unstable (recall, 0.70). Saliency maps generated by gradient-weighted class activation mapping primarily showed highly activated areas corresponding to clinically probed regions around teeth. Our binary patient classifier achieved an area under the receiver operating characteristic curve of 0.68 and an F<sub>1</sub> score of 0.74 (recall, 0.70). Taken together, our results suggest that it is feasible to estimate periodontal stability, which traditionally requires clinical and radiographic examination, from radiographic signal alone using computer vision. Variations in model performance across different classes at the tooth level indicate the necessity of further refinement.