Histopathology-Based Diagnosis of Oral Squamous Cell Carcinoma Using Deep Learning

Oral squamous cell carcinoma (OSCC) is prevalent worldwide and is associated with a poor prognosis. OSCC is typically diagnosed from tissue biopsy sections by pathologists who rely on their empirical experience. The present study model automatically evaluated these images and arrived at a diagnosis with a sensitivity of 0.98, specificity of 0.92, the positive predictive value of 0.924, the negative predictive value of 0.978, and F1 score of 0.951. It was found that junior pathologists could delineate OSCC in these images 6.26 min faster when assisted by the model than when working alone. Furthermore, when the model helped the clinicians, their average F1 score improved from 0.9221 to 0.9566 in the case of junior pathologists and from 0.9361 to 0.9463 in the case of senior pathologists. Our findings indicate that deep learning can improve the accuracy and speed of OSCC diagnosis from histopathology images.