首页|期刊导航|数字中医药(英文)|从脉搏到像素:人工智能增强的脉诊用于心血管疾病评估

从脉搏到像素:人工智能增强的脉诊用于心血管疾病评估OA

From pulse to pixel:artificial intelligence-enhanced pulse diagnosis for cardiovascular diseases

中文摘要英文摘要

中医脉诊是一种用于评估心血管健康的无创方法,但其解读相对主观,限制了可重复性与诊断精确性.本综述总结了利用现代传感器与人工智能(AI)对桡动脉脉诊进行数字化的研究进展,并评估了其在心血管筛查与临床决策支持中的应用.我们检索 PubMed、IEEE Xplore和 Web of Science Core Collection 从建库起至 2025年 11月 30日的文献,纳入使用电子设备采集桡动脉脉搏信号并采用定量分析或机器学习/深度学习识别脉象特征或评估心血管疾病相关结局的研究.现有文献显示,压力传感阵列、可穿戴设备常用的光电容积描记(PPG)以及混合平台可促进更标准化的脉搏采集;同时,AI模型在血压估计、高血压筛查、冠状动脉疾病识别、心力衰竭风险分层和心律失常检测等任务中报告了有前景的性能.然而,方法学异质性、样本量有限、标注标准不一致以及外部验证不足仍是临床转化的关键障碍.总体而言,AI增强的数字脉诊可提高脉诊评估的客观性,并可能与常规心血管诊断形成互补,但仍需在严格研究设计、透明报告和具有临床意义的前瞻性验证基础上推进.

Traditional Chinese medicine(TCM)pulse diagnosis is a non-invasive approach used to infer cardiovascular status,but its interpretation is relatively subjective,limiting reproducibility and diagnostic precision.This review summarizes progress in digitized radial pulse assess-ment using modern sensors and artificial intelligence(AI),and evaluates reported applica-tions in cardiovascular screening and decision support.We searched PubMed,IEEE Xplore,and Web of Science Core Collection from inception through November 30,2025,for studies that acquired wrist/radial pulse signals with electronic devices and applied quantitative anal-ysis or machine learning/deep learning to characterize pulse patterns or assess cardiovascu-lar conditions.Across the literature,pressure-sensor arrays,wearable photoplethysmography(PPG)surrogates,and hybrid platforms enabled more standardized pulse acquisition,while AI models reported promising performance for tasks such as blood pressure estimation,hy-pertension screening,coronary artery disease identification,heart failure risk stratification,and arrhythmia detection.However,methodological heterogeneity,limited sample sizes,in-consistent labeling standards,and insufficient external validation remain key barriers to clini-cal translation.Overall,AI-enhanced digital pulse diagnosis may improve the objectivity of TCM pulse assessment and complement conventional cardiovascular diagnostics,provided that future studies adopt rigorous protocols,transparent reporting,and clinically meaningful prospective validation.

Xilong Zheng

Department of Biochemistry and Molecular Biology/Department of Physiology and Pharmacology,Cumming School of Medicine,University of Calgary,Calgary,Alberta T2N 4N1,Canada

脉诊中医人工智能心血管疾病机器学习

Pulse diagnosisTraditional Chinese medicineArtificial intelligenceCardiovascular diseaseMachine learning

《数字中医药(英文)》 2026 (2)

161-172,12

Heart and Stroke Foundation of Canada(HSFCG-25-0041271),and Canadian Institutes of Health Research(CIHRPJT-165941 and PJT-178010).

10.1016/j.dcmed.2026.05.001

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