Examination of the performance of machine learning-based automated coronary plaque characterization by near-infrared spectroscopy–intravascular ultrasound and optical coherence tomography with histology

NIRS-IVUS-ML plaque compositional analysis has a good performance in assessing plaque components while OCT-ML performs well for the FT, moderately for the Ca, and has weak performance in detecting NC tissue. This may be attributable to the limitations of standalone intravascular imaging and to the fact that the OCT-ML classifier was trained on human experts rather than histological standards.

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Publication Details

Journal
European Heart Journal - Digital Health
Published
2025-03-04
DOI
https://doi.org/10.1093/ehjdh/ztaf009
Citations
4
Primary Topic
Cardiac Imaging and Diagnostics
Type
article
Field-Weighted Citation Impact
5.44
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OCT
article

Examination of the performance of machine learning-based automated coronary plaque characterization by near-infrared spectroscopy–intravascular ultrasound and optical coherence tomography with histology

Jouke Dijkstra, Francesco Prati, Ramya Parasa, Medeni Karaduman et al.
4 citations
European Heart Journal - Digital Health
Cardiac Imaging and Diagnostics
5.44
article

Examination of the performance of machine learning-based automated coronary plaque characterization by near-infrared spectroscopy–intravascular ultrasound and optical coherence tomography with histology

Jouke Dijkstra, Francesco Prati, Ramya Parasa, Medeni Karaduman, Nathan Angelo Lecaros Yap, Ryo Torii, Stephanie Grainger, Carol A.G.R. Busk, Helle Precht, Christos V. Bourantas, Andreas Baumbach, Anthony Mathur, Mohil Garg, Retesh Bajaj, Alexander Broersen, Héctor M. García‐García, Murat Çap, Tom Johnson, Steven A. White
article en
4 citations

Abstract

NIRS-IVUS-ML plaque compositional analysis has a good performance in assessing plaque components while OCT-ML performs well for the FT, moderately for the Ca, and has weak performance in detecting NC tissue. This may be attributable to the limitations of standalone intravascular imaging and to the fact that the OCT-ML classifier was trained on human experts rather than histological standards.

European Heart Journal - Digital HealthVol. 6(3)
Van Yüzüncü Yıl Üniversitesi (TR), MedStar Washington Hospital Center (US), MedStar National Rehabilitation Hospital (US), University of Ottawa (CA), University of Southern Denmark (DK), Barts Health NHS Trust (GB), University College Lillebaelt (DK), Azienda Ospedaliera San Giovanni Addolorata (IT), Leiden University Medical Center (NL), University Hospitals Bristol NHS Foundation Trust (GB), Washington Hospital (US), Essex Cardiothoracic Centre (GB), Lega Italiana per la Lotta ai Tumori (IT), Lillebaelt Hospital (DK), The London College (GB), William Harvey Research Institute (GB), Infrared Laboratories (United States) (US), University Hospitals Bristol and Weston NHS Foundation Trust (GB), University College London (GB), Newcastle University (GB)
Openalex Percentile: Top 4%
Cardiac Imaging and Diagnostics
5.44
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