EClinicalMedicine
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The overall prognosis of oral cancer remains poor because over half of patients are diagnosed at advanced-stages. Previously reported screening and earlier detection methods for oral cancer still largely rely on health workers' clinical experience and as yet there is no established method. We aimed to develop a rapid, non-invasive, cost-effective, and easy-to-use deep learning approach for identifying oral cavity squamous cell carcinoma (OCSCC) patients using photographic images. ⋯ Automated detection of OCSCC by deep-learning-powered algorithm is a rapid, non-invasive, low-cost, and convenient method, which yielded comparable performance to that of human specialists and has the potential to be used as a clinical tool for fast screening, earlier detection, and therapeutic efficacy assessment of the cancer.