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This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (3454) Decision Support for Health Professionals (2074) Machine Learning (2992) Coronary Heart Disease (148) Artificial Intelligence (4454) Cardiac Diagnostics (57)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/70943, first published .
Laptop screen displaying medical scans of human skeleton, brain, and heart with data

Prediction of Percutaneous Coronary Intervention Success in Patients With Moderate to Severe Coronary Artery Calcification Using Machine Learning Based on Coronary Angiography: Prospective Cohort Study

Prediction of Percutaneous Coronary Intervention Success in Patients With Moderate to Severe Coronary Artery Calcification Using Machine Learning Based on Coronary Angiography: Prospective Cohort Study

Authors of this article:

Zixiang Ye1, 2, 3 Author Orcid Image ;   Zhangyu Lin1, 2, 3 Author Orcid Image ;   Enmin Xie1, 2, 3 Author Orcid Image ;   Chenxi Song1, 2, 3 Author Orcid Image ;   Rui Zhang1, 2, 3 Author Orcid Image ;   Hao-Yu Wang1, 2, 3 Author Orcid Image ;   Shanshan Shi1, 2, 3 Author Orcid Image ;   Lei Feng1, 2, 3 Author Orcid Image ;   Kefei Dou1, 2, 3 Author Orcid Image
Article Authors Cited by (2) Tweetations (3) Metrics

Journals

  1. Zepeda-Lugo C, Sanchez-Lizarraga M, Insfran-Rivarola A, Arévalos A. Effectiveness of AI Models for Cardiovascular Disease Diagnosis: A Systematic Review and Meta-analysis. Revista de Investigación e Innovación en Ciencias de la Salud 2025;8(1):1 View
  2. Chen J. Development and validation of a machine learning model for colorectal cancer status classification using NHANES data: A cross-sectional study. Medicine 2026;105(23):e49114 View