AI-Assisted Screening of Oral Potentially Malignant Disorders Using Smartphone-Based Photographic Images.

Simple Summary: The early detection of oral cancer is essential for improving patient outcomes. A conventional oral examination by specialists is the clinical standard for detecting oral lesions. However, many high-risk individuals in middle- and low-income countries lack access to specialists. Therefore, there is a need to develop an easy-to-use, non-invasive oral screening tool that enhances the existing system for detecting precancerous lesions. This study explores artificial intelligence (AI)-based techniques to identify precancerous lesions using photographic images of oral cavities in the Indian population. The high performance of deep learning models suggests that an AI-based solution can be deployed for community screening programs in low-resource settings after further improvement and validation. The prevalence of oral potentially malignant disorders (OPMDs) and oral cancer is surging in low- and middle-income countries. A lack of resources for population screening in remote locations delays the detection of these lesions in the early stages and contributes to higher mortality and a poor quality of life. Digital imaging and artificial intelligence (AI) are promising tools for cancer screening. This study aimed to evaluate the utility of AI-based techniques for detecting OPMDs in the Indian population using photographic images of oral cavities captured using a smartphone. A dataset comprising 1120 suspicious and 1058 non-suspicious oral cavity photographic images taken by trained front-line healthcare workers (FHWs) was used for evaluating the performance of different deep learning models based on convolution (DenseNets) and Transformer (Swin) architectures. The best-performing model was also tested on an additional independent test set comprising 440 photographic images taken by untrained FHWs (set I). DenseNet201 and Swin Transformer (base) models show high classification performance with an F1-score of 0.84 (CI 0.79–0.89) and 0.83 (CI 0.78–0.88) on the internal test set, respectively. However, the performance of models decreases on test set I, which has considerable variation in the image quality, with the best F1-score of 0.73 (CI 0.67–0.78) obtained using DenseNet201. The proposed AI model has the potential to identify suspicious and non-suspicious oral lesions using photographic images. This simplified image-based AI solution can assist in screening, early detection, and prompt referral for OPMDs. [ABSTRACT FROM AUTHOR]