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.
摘要：
设计了人工智能（AI）模型，以帮助病理学家诊断和量化化学致癌物引起的舌头病变的结构变化。使用4-硝基喹啉-N-氧化物诱导并用β-榄香烯处理的舌癌模型，共处理了183张数字病理幻灯片.分段任意模型(SAM)用于初始分段，其次是传统的算法进行更详细的分割。上皮轮廓面积使用OpenCV的findoutline方法计算，并采用骨架化方法计算距离图和骨架化表示。AI模型在测量舌上皮厚度和乳头状突起的数量方面表现出很高的准确性。结果表明，与空白组相比，模型组上皮厚度明显升高，乳头减少。此外，与模型组相比，治疗组上皮厚度减少，乳头样突起减少，尽管这些差异不太明显。总的来说,SAM框架算法在量化舌上皮厚度和乳头状突起的数量方面被证明是有效的，从而协助医疗保健专业人员了解病理变化和评估治疗结果。
