AI Assisted Screening of Oral Potentially Malignant Disorders Using Smartphone Photographic Images - An Indian Cohort Study

Talwar Vivek Jayant

Abstract

The escalating prevalence of Oral Potentially Malignant Disorders (OPMDs) and oral cancer in lowand middle-income countries presents a critical challenge, exacerbated by limited resources that hinder population screening in remote areas. The study evaluates the efficacy of artificial intelligence (AI) and digital imaging diagnostics as tools for OPMD detection in the Indian population, utilizing smartphone-captured oral cavity images. Trained front-line healthcare workers (FHWs) contributed a dataset comprising 1,120 suspicious and 1,058 non-suspicious images. Various deep-learning models, including DenseNets and Swin Transformers, were assessed for image-classification performance. The best-performing model was then tested on an independent external set of 440 images collected by untrained FHWs. DenseNet201 and Swin Transformer (base) models exhibited high classification accuracy on the internal test set, achieving F1-scores of 0.84 (CI 0.79–0.89) and 0.83 (CI 0.78–0.88). However, performance declined on the external set—characterized by significant variation in image quality—with DenseNet201 yielding the highest F1-score of 0.73 (CI 0.67–0.78). The AI model demonstrates potential for identifying suspicious versus non-suspicious oral lesions via photographic images. This image-based solution holds promise for facilitating early screening, detection, and timely referral for OPMDs.

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