人工智能驱动的舌头图像分析用于诊断和预测冠状动脉疾病
Alireza Hekmat-Ardakani1, Hamid R Rabiee2, Armin Behnamnia3
1Department of Traditional Medicine, School of Persian Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Scientific reports
|December 29, 2025
概括
人工智能分析舌头图像用于冠状动脉疾病 (CAD) 查. 这种非侵入性方法显示出有前途,在识别左环动脉狭窄症时达到80%的准确性.
科学领域:
- 综合医学是一个整体医学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 冠状动脉疾病 (CAD) 是一个主要的全球健康问题,现有的诊断方法往往是侵入性的和昂贵的.
- 传统伊朗医学提供了关于舌头诊断的见解,表明舌头特征与全身健康之间存在联系.
- 需要对CAD进行非侵入性,准确和可访问的查工具.
研究的目的:
- 调查人工智能 (AI) 支持的舌头图像分析作为冠状动脉疾病 (CAD) 的新型非侵入性诊断工具的有效性.
- 为了将特定的舌头特征与冠状动脉狭窄的存在和严重程度相关联.
主要方法:
- 利用了12,000张来自906名接受冠状动脉血管造影的患者的高分辨率舌头图像的数据集.
- 采用人工智能模型,包括Segment-Anything模型用于细分和DeepLabv3+/DINOv2用于特征提取和分类.
- 分析了舌头的特征,如颜色,裂,涂层,水分和红斑,整合了传统伊朗医学的原则.
主要成果:
- 人工智能模型在预测左环动脉狭窄方面达到高达80%的准确性.
- 舌头的颜色和裂被确定为冠状动脉狭窄的重要预测因素.
- 展示了人工智能驱动的舌头分析的潜力,以快速和经济有效地进行CAD查.
结论:
- 人工智能驱动的舌头图像分析代表了查冠状动脉疾病的有希望的,非侵入性的辅助方法.
- 这种方法在资源有限的环境中可能特别有价值.
- 需要在不同人群中进一步验证,以确定更广泛的临床实用性.
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