An analysis on efficacy of applying β-elemene intervention on chemically -induced tongue lesions using SAM algorithm

Abstract

An artificial intelligence (AI) model was designed to assist pathologists in diagnosing and quantifying structural changes in tongue lesions induced by chemical carcinogens. Using a tongue cancer model induced by 4-nitroquinoline-N-oxide and treated with β-elemene, a total of 183 digital pathology slides were processed. The Segment Anything Model (SAM) was employed for initial segmentation, followed by conventional algorithms for more detailed segmentation. The epithelial contour area was computed using OpenCV's findcontour method, and the skeletonize method was used to calculate the distance map and skeletonized representation. The AI model demonstrated high accuracy in measuring tongue epithelial thickness and the number of papilla-like protrusions. Results indicated that the model group had significantly higher epithelial thickness and fewer papillae compared with the blank group. Furthermore, the treatment group exhibited reduced epithelial thickness and fewer papilla-like protrusions compared with the model group, though these differences were less pronounced. Overall, the SAM framework algorithm proved effective in quantifying tongue epithelial thickness and the number of papilla-like protrusions, thereby assisting healthcare professionals in understanding pathological changes and assessing treatment outcomes.