A deep learning method for diagnosis of oral potentially malignant disorders

This study aimed to develop and validate a two-stage deep learning method for diagnosing oral potentially malignant disorders (OPMDs). We also compared its diagnostic performance with that of clinicians at different levels of seniority and evaluated its utility as a clinical decision support tool.

A two-stage deep learning method was developed. CLA OPMD-OOML was designed to differentiate OPMDs from other oral mucosal lesions (OOML), while CLA OPMDs was designed to classify specific OPMD subtypes. The method was trained on an internal dataset (ZJUSS, n = 3305), and its generalizability was evaluated on two external multicenter datasets (WCHS and CS-SJTU, n = 1756). In a blinded, two-step comparative study, nine clinicians (junior, intermediate, and senior groups) performed diagnoses independently and then with AI assistance.

The method significantly outperformed all clinician groups, achieving higher F1 scores in both CLA OPMD-OOML (89.9 % vs. 78.4-82.6 % for clinicians) and CLA OPMDs (92.2 % vs. 77.7-80.0 % for clinicians). It showed robust generalizability on the two external datasets, with F1 scores of 87.3 % and 86.9 % for CLA OPMD-OOML and 75.9-83.0 % for CLA OPMDs. With AI assistance, the diagnostic precision of junior and intermediate clinicians increased by 10.8 % and 5.9 %, respectively, raising the junior group's performance to the level of senior clinicians.

The developed two-stage deep learning method demonstrated diagnostic performance comparable to or exceeding that of experienced clinicians in classifying OPMDs from clinical images. It functions as a powerful assistive tool that substantially enhances the diagnostic capabilities of junior and intermediate clinicians.

This AI method has the potential to serve as a reliable tool for large-scale early screening of OPMDs, particularly in regions with limited access to specialist dental care. It can also serve as a valuable training and decision support system for junior clinicians, helping to standardize diagnostic accuracy and improve patient outcomes through timely and accurate detection. A supplemental appendix to this article is available online.