数字中医药（英文）2026，Vol.9Issue(2)：161-172,12.DOI:10.1016/j.dcmed.2026.05.001
从脉搏到像素:人工智能增强的脉诊用于心血管疾病评估
From pulse to pixel:artificial intelligence-enhanced pulse diagnosis for cardiovascular diseases
摘要
Abstract
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.关键词
脉诊/中医/人工智能/心血管疾病/机器学习Key words
Pulse diagnosis/Traditional Chinese medicine/Artificial intelligence/Cardiovascular disease/Machine learning引用本文复制引用
Xilong Zheng..从脉搏到像素:人工智能增强的脉诊用于心血管疾病评估[J].数字中医药（英文）,2026,9(2):161-172,12.基金项目
Heart and Stroke Foundation of Canada(HSFC （）
G-25-0041271),and Canadian Institutes of Health Research(CIHR （）
PJT-165941 and PJT-178010). （）
