TonguExpert:一种基于深度学习的算法平台,用于细粒度提取和分类舌头表型.
Ting Li1, Ling Zuo2, Pengyu Wang1
1CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031 China.
Phenomics (Cham, Switzerland)
|July 3, 2025
概括
用于疾病检测的舌头分析现在可以自动化使用TonguExpert. 这个平台使用大量数据集的深度学习来准确地提取舌头表型,改进诊断和公开数据.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 生物医学成像技术 生物医学成像技术
背景情况:
- 舌头分析是一种有前途的疾病检测和健康监测工具,特别是在中国传统医学中.
- 主观性和缺乏大型数据集阻碍了自动化舌头诊断.
- 现有的深度学习方法与微妙的舌头细节作斗争.
研究的目的:
- 开发一种自动化的深度学习框架,用于舌头图像分析和表型提取.
- 创建一个免费的平台,TonguExpert,用于存档和分析舌头图像.
- 建立最大的公开可用的舌头图像和表型数据集.
主要方法:
- 开发了用于舌头细分和表型提取的深度学习框架.
- 利用来自中国人口的5992张舌头图像的数据集.
- 提取了773种表型,包括全球和本地特征,并预测了标签.
主要成果:
- 在分类舌头颜色 (ROC-AUC 0.89-0.99),裂 (0.97) 和牙痕迹 (0.88) 中,TonguExpert实现了高准确度.
- 该模型在外部数据集上展示了良好的概括能力,用于预测新的表型,如油性涂层.
- 发布了最大的公开可用的舌头图像和相关表型数据集.
结论:
- TonguExpert显著优于手动分类,推进了自动化舌头诊断.
- 该平台和数据集促进了更广泛的临床采用和舌头诊断的未来研究.
- 这项工作为扩大自动化舌头分析在医疗保健中的应用铺平了道路.
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