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February 6, 2026European Heart Journal

Noninvasive diagnosis of ischemia non-obstructive coronary using machine learning-assisted magnetocardiography

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Authors

XXX XuCSC Shen

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Overview

Research shows enhanced diagnosis of ischemia non-obstructive coronary in patients, indicating better clinical outcomes.

Key Points

  • The study aims to develop a rapid and accurate diagnostic model for ischemia non-obstructive coronary arteries (INOCA) using magnetocardiography.
  • 709 patients underwent coronary angiography and magnetocardiography (MCG) for analysis.
  • Feature extraction utilized statistical parameters, time-frequency analysis, and heterogeneous methods like wavelet transforms.
  • Machine learning model XGBoost modified with Focal Loss was used for diagnosis, with hyperparameters optimized via OPTUNA.
  • Model performance assessed through stratified five-fold cross-validation.
  • Achieved an average accuracy of 0.785, sensitivity of 0.770, and specificity of 0.797 during validation.
  • Area under the ROC curve (AUC) reached 0.832, indicating good discrimination capability.
  • The model effectively incorporated clinical variables, enhancing practical utility in clinical settings.

Cite This Study

Xu et al. (2025) studied this question.

synapsesocial.com/papers/698585bd8f7c464f23009559https://doi.org/10.1093/eurheartj/ehaf784.1707
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Also Consider

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  1. 1Machine learning in diagnosing coronary artery disease via optical pumped magnetometer magnetocardiography: a prospective cohort study2025 · 6 citations
  2. 2Magnetocardiography-Based Ischemic Heart Disease Detection and Localization Using Machine Learning Methods2018 · 143 citations
  3. 3Application of artificial intelligence on magnetocardiology2026
  4. 4Magnetoionography enhances diagnostic accuracy of magnetocardiography in coronary artery disease2025
  5. 5Magnetoionography enhances diagnostic accuracy of magnetocardiography in coronary artery disease2025 · 4 citations