Oral cancer is one of the most common malignant tumors. There are 354,864 new cases and 177,384 death per year globally according to Globocan 2018 report. Most of the cases are in low- and middle-income countries that lack trained specialists and health services, of which India accounts for approximately one-third of the new cases and two-fifth deaths. Point-of-care oral screening tool to enable early diagnosis is urgently needed. We developed a dual-mode intraoral oral cancer screening platform and an automatic classification algorithm for oral dysplasia and malignancy images using deep learning.
Oral cancer is a growing health issue in low- and middle-income countries due to betel quid, tobacco, and alcohol use and in younger populations of middle- and high-income communities due to the prevalence of human papillomavirus. The described point-of-care, smartphone-based intraoral probe enables autofluorescence imaging and polarized white light imaging in a compact geometry through the use of a USB-connected camera module. The small size and flexible imaging head improves on previous intraoral probe designs and allows imaging the cheek pockets, tonsils, and base of tongue, the areas of greatest risk for both causes of oral cancer. Cloud-based remote specialist and convolutional neural network clinical diagnosis allow for both remote community and home use. The device is characterized and preliminary field-testing data are shared.
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