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Machine Learning in Invasive and Noninvasive Coronary Angiography

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Abstract

Purpose of Review

The objective of this review is to shed light on the transformative potential of machine learning (ML) in coronary angiography. We aim to understand existing developments in using ML for coronary angiography and discuss broader implications for the future of coronary angiography and cardiovascular medicine.

Recent Findings

The developments in invasive and noninvasive imaging have revolutionized diagnosis and treatment of coronary artery disease (CAD). However, CAD remains underdiagnosed and undertreated. ML has emerged as a powerful tool to further improve image analysis, hemodynamic assessment, lesion detection, and predictive modeling. These advancements have enabled more accurate identification of CAD, streamlined workflows, reduced the need for invasive diagnostic procedures, and improved the diagnostic value of invasive procedures when they are needed. Further integration of ML with coronary angiography will advance the prevention, diagnosis, and treatment of CAD.

Summary

The integration of ML with coronary angiography is ushering in a new era in cardiovascular medicine. We highlight five use cases to leverage ML in coronary angiography: (1) improvement of quality and efficacy, (2) characterization of plaque, (3) hemodynamic assessment, (4) prediction of future outcomes, and (5) diagnosis of non-atherosclerotic coronary disease.

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Funding

Dr. Unlu receives funding from the National Heart Lung and Blood Institute under award number T32HL007604. Dr. Fahed receives funding from the National Heart Lung and Blood Institute under award numbers K08 HL161448 and R01 HL164629.

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Authors and Affiliations

  1. Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Ozan Unlu

  2. Clinical Informatics, Mass General Brigham, Harvard Medical School, Boston, MA, USA

    Ozan Unlu

  3. Cardiovascular Disease Initiative and ML for Health, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Ozan Unlu & Akl C. Fahed

  4. Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street CPZN 3.128, Boston, MA, 02114, USA

    Akl C. Fahed

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  1. Ozan Unlu
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O.U. and A.C.F conceptualized and wrote the main manuscript text and prepared tables and figures.

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Correspondence to Akl C. Fahed.

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Conflict of Interest

Dr. Fahed reports being co-founder of Goodpath and received a research grant from Abbott Vascular, unrelated to the subject of this manuscript. Dr. Unlu has nothing to disclose.

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Unlu, O., Fahed, A.C. Machine Learning in Invasive and Noninvasive Coronary Angiography. Curr Atheroscler Rep 25, 1025–1033 (2023). https://doi.org/10.1007/s11883-023-01178-z

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