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目的 舌象中点刺所生的部位、颜色、分布的疏密可以推测邪热所在脏腑及其轻重.本研究聚焦于人工智能的图像分析方法研究中医点刺舌识别.方法 基于图像深度学习和实例分割原理,设计了舌象点刺识别与提取模型.该模型包括多尺度特征图生成模块、候选区域搜索模块和目标区域识别模块.首先使用深度卷积网络分别建立多尺度低、高抽象度的特征图谱,再在特征图上进行目标候选框生成算法和优选策略以精选出高质量目标候选区域,最后使用分类网络对目标区域分类、计算目标区域像素,最终得到舌象表面点刺的区域分割.在无辅助光源条件下手机拍摄的不同规格舌象,使用该方法进行实验.结果 实验结果表明,该点刺识别受试者工作特征曲线下的面积(AUC)值为92.40％,精确度为84.30％,灵敏度为88.20％,特异度为94.19％,召回率为88.20％,区域像素准确率指标像素精度(PA)为73.00％,均像素精度(mPA)为73.00％,交并比(IoU)为60.00％,均交并比(mIoU)为56.00％.结论 本研究结果表明该模型适用于中医舌诊系统应用.基于多尺度卷积神经网络的点刺舌识别,有助于提高点刺分类和点刺区域像素的精准提取,为中医智能舌诊提供一种切实可行的方法.“,”Objective In tongue diagnosis, the location, color, and distribution of spots can be used to speculate on the viscera and severity of the heat evil. This work focuses on the image analysis method of artificial intelligence (AI) to study the spotted tongue recognition of traditional Chinese medicine (TCM).Methods A model of spotted tongue recognition and extraction is designed, which is based on the principle of image deep learning and instance segmentation. This model includes multiscale feature map generation, region proposal searching, and target region recognition. Firstly, deep convolution network is used to build multiscale low- and high-abstraction fea-ture maps after which, target candidate box generation algorithm and selection strategy are used to select high-quality target candidate regions. Finally, classification network is used for classifying target regions and calculating target region pixels. As a result, the region segmenta-tion of spotted tongue is obtained. Under non-standard illumination conditions, various tongue images were taken by mobile phones, and experiments were conducted. Results The spotted tongue recognition achieved an area under curve (AUC) of 92.40％, an accuracy of 84.30％ with a sensitivity of 88.20％, a specificity of 94.19％, a recall of 88.20％, a regional pixel accuracy index pixel accuracy (PA) of 73.00％, a mean pixel accuracy (mPA) of 73.00％, an intersection over union (IoU) of 60.00％, and a mean intersection over union (mIoU) of 56.00％. Conclusion The results of the study verify that the model is suitable for the application of the TCM tongue diagnosis system. Spotted tongue recognition via multiscale convolutional neur-al network (CNN) would help to improve spot classification and the accurate extraction of pixels of spot area as well as provide a practical method for intelligent tongue diagnosis of TCM.