{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T16:52:20Z","timestamp":1788022340608,"version":"build-2784847793"},"reference-count":55,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T00:00:00Z","timestamp":1741046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000274","name":"British Heart Foundation","doi-asserted-by":"publisher","award":["FS\/CRA\/22\/23032"],"award-info":[{"award-number":["FS\/CRA\/22\/23032"]}],"id":[{"id":"10.13039\/501100000274","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Aims<\/jats:title>\n                  <jats:p>Near-infrared spectroscopy\u2013intravascular ultrasound (NIRS\u2013IVUS) and optical coherence tomography (OCT) can assess coronary plaque pathology but are limited by time-consuming and expertise-driven image analysis. Recently introduced machine learning (ML)-classifiers have expedited image processing, but their performance in assessing plaque pathology against histological standards remains unclear. The aim of this study is to assess the performance of NIRS\u2013IVUS\u2013ML-based and OCT\u2013ML-based plaque characterization against a histological standard.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods and results<\/jats:title>\n                  <jats:p>Matched histological and NIRS\u2013IVUS\/OCT frames from human cadaveric hearts were manually annotated and fibrotic (FT), calcific (Ca), and necrotic core (NC) regions of interest (ROIs) were identified. Near-infrared spectroscopy\u2013intravascular ultrasound and OCT frames were processed by their respective ML classifiers to segment and characterize plaque components. The histologically defined ROIs were overlaid onto corresponding NIRS\u2013IVUS\/OCT frames and the ML classifier estimations were compared with histology. In total, 131 pairs of NIRS\u2013IVUS\/histology and 184 pairs of OCT\/histology were included. The agreement of NIRS\u2013IVUS\u2013ML with histology [concordance correlation coefficient (CCC) 0.81 and 0.88] was superior to OCT\u2013ML (CCC 0.64 and 0.73) for the plaque area and burden. Plaque compositional analysis showed a substantial agreement of the NIRS\u2013IVUS\u2013ML with histology for FT, Ca, and NC ROIs (CCC: 0.73, 0.75, and 0.66, respectively) while the agreement of the OCT\u2013ML with histology was 0.42, 0.62, and 0.13, respectively. The overall accuracy of NIRS\u2013IVUS\u2013ML and OCT\u2013ML for characterizing atheroma types was 83% and 72%, respectively.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>NIRS\u2013IVUS\u2013ML plaque compositional analysis has a good performance in assessing plaque components while OCT\u2013ML 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\u2013ML classifier was trained on human experts rather than histological standards.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ehjdh\/ztaf009","type":"journal-article","created":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T00:24:45Z","timestamp":1741134285000},"page":"359-371","source":"Crossref","is-referenced-by-count":11,"title":["Examination of the performance of machine learning-based automated coronary plaque characterization by near-infrared spectroscopy\u2013intravascular ultrasound and optical coherence tomography with histology"],"prefix":"10.1093","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7424-0419","authenticated-orcid":false,"given":"Retesh","family":"Bajaj","sequence":"first","affiliation":[{"name":"Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust , West Smithfield, London EC1A 7BE ,","place":["UK"]},{"name":"Centre for Cardiovascular Medicine and Device Innovation, William Harvey Research Institute, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ ,","place":["UK"]},{"name":"Department of Cardiology, Ottawa Heart Institute , Ottawa, ON ,","place":["Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramya","family":"Parasa","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust , West Smithfield, London EC1A 7BE ,","place":["UK"]},{"name":"Centre for Cardiovascular Medicine and Device Innovation, William Harvey Research Institute, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ ,","place":["UK"]},{"name":"The Essex Cardiothoracic Centre , Basildon ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Broersen","sequence":"additional","affiliation":[{"name":"Division of Image Processing, Department of 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