Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

SIMPLE SUMMARY: Oral cancer is characterized by high morbidity and mortality, since the disease is typically in an advanced locoregional stage at the time of diagnosis. The application of artificial intelligence (AI) techniques to oral cancer screening has recently been proposed. This scoping review analyzed the information about different machine learning tools in support of non-invasive diagnostic techniques including telemedicine, medical images, fluorescence images, exfoliative cytology and predictor variables at risk of developing oral cancer. The results suggest that such tools can make a noninvasive contribution to the early diagnosis of oral cancer and we express the gaps of the proposed questions to be improved in new investigations. ABSTRACT: The early diagnosis of cancer can facilitate subsequent clinical patient management. Artificial intelligence (AI) has been found to be promising for improving the diagnostic process. The aim of the present study is to increase the evidence on the application of AI to the early diagnosis of oral cancer through a scoping review. A search was performed in the PubMed, Web of Science, Embase and Google Scholar databases during the period from January 2000 to December 2020, referring to the early non-invasive diagnosis of oral cancer based on AI applied to screening. Only accessible full-text articles were considered. Thirty-six studies were included on the early detection of oral cancer based on images (photographs (optical imaging and enhancement technology) and cytology) with the application of AI models. These studies were characterized by their heterogeneous nature. Each publication involved a different algorithm with potential training data bias and few comparative data for AI interpretation. Artificial intelligence may play an important role in precisely predicting the development of oral cancer, though several methodological issues need to be addressed in parallel to the advances in AI techniques, in order to allow large-scale transfer of the latter to population-based detection protocols.