Machine learning in diagnosing coronary artery disease via optical pumped magnetometer magnetocardiography: a prospective cohort study.

IF 2.3 4区医学 Q3 BIOPHYSICS

Physiological measurement Pub Date : 2025-07-16 DOI:10.1088/1361-6579/adf0be

Chenchen Tu, Shuwen Yang, Zhixiang Wang, Linqi Liu, Zhao Ma, Huan Zhang, Lanxin Feng, Bin Cai, Hongjia Zhang, Ming Ding, Xiantao Song

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引用次数: 0

Abstract

Objective: The potential of optical pumped magnetometer magnetocardiography (OPM-MCG) for diagnosing coronary artery disease (CAD) has been initially shown, yet lacks large-scale prospective research.

Approach: Using invasive coronary angiography (ICA) as a reference, we constructed three feature sets for the development of machine learning (ML) models: a "Heart" feature set consisting only of OPM-MCG features, a "Clinical" feature set, and a "Heart+Clinical" combined feature set. We assessed the performance of 11 ML models with 10-fold cross-validation and conducted a feature importance analysis.

Main result: Among 1513 participants (mean age 58.2 ± 12.0 years, 75.5% male), 1194 (78.92%) tested positive for ICA. Significant differences were observed in "Heart" and "Clinical" features between ICA-positive and negative groups. ML models using only "Heart" features (AUC 0.84 - 0.88) outperformed those using only "Clinical" features (AUC 0.62 - 0.75). Combining both feature types improved diagnostic accuracy (AUC 0.75 - 0.90). Feature importance analysis highlighted that "Significant changes in Ar-PN" in OPM-MCG were key for ICA diagnosis (47.8%), along with "Abnormal Sp-TT", "Significant changes in Ps-PN", and "Abnormal Mg-TT".

Significance: OPM-MCG has high performance in diagnosing CAD, and the significant change in Ar-PN is the most important feature. Cat Boost and Random Forest are more suitable for OPM-MCG to build ML diagnostic models for CAD.

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机器学习在诊断冠状动脉疾病中的应用：一项前瞻性队列研究。

目的：光泵浦磁强计心脏磁图（OPM-MCG）诊断冠状动脉疾病（CAD）的潜力已初步显示，但缺乏大规模的前瞻性研究。方法：以侵入性冠状动脉造影（ICA）为参考，我们构建了三个用于开发机器学习（ML）模型的特征集：仅由OPM-MCG特征组成的“心脏”特征集，“临床”特征集和“心脏+临床”组合特征集。我们通过10倍交叉验证评估了11个ML模型的性能，并进行了特征重要性分析。主要结果：1513名参与者（平均年龄58.2±12.0岁，男性75.5%）中，1194人（78.92%）检测出ICA阳性。ica阳性组和阴性组在“心脏”和“临床”特征上有显著差异。仅使用“心脏”特征（AUC 0.84 - 0.88）的ML模型优于仅使用“临床”特征（AUC 0.62 - 0.75）的ML模型。结合这两种特征类型提高了诊断准确性（AUC 0.75 - 0.90）。特征重要性分析显示，OPM-MCG中“Ar-PN显著变化”是ICA诊断的关键（47.8%），其次是“Sp-TT异常”、“Ps-PN显著变化”和“Mg-TT异常”。意义：OPM-MCG在CAD诊断中具有较高的效能，Ar-PN的显著变化是最重要的特征。Cat Boost和Random Forest更适合OPM-MCG构建CAD的ML诊断模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Physiological measurement 生物-工程：生物医学

CiteScore

5.50

自引率

9.40%

发文量

124

审稿时长

3 months

期刊介绍： Physiological Measurement publishes papers about the quantitative assessment and visualization of physiological function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. Papers are published on topics including: applied physiology in illness and health electrical bioimpedance, optical and acoustic measurement techniques advanced methods of time series and other data analysis biomedical and clinical engineering in-patient and ambulatory monitoring point-of-care technologies novel clinical measurements of cardiovascular, neurological, and musculoskeletal systems. measurements in molecular, cellular and organ physiology and electrophysiology physiological modeling and simulation novel biomedical sensors, instruments, devices and systems measurement standards and guidelines.