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Artificial Intelligence-Led Whole Coronary Artery OCT Analysis; Validation and Identification of Drug Efficacy and Higher-Risk Plaques
,
Sophie Gu,
Yuan Huang ,
Martin Goddard,
Adam Brown,
Daniel Obaid ,
Michael Mahmoudi,
Hector M. Garcia Garcia ,
Stephen P. Hoole ,
Lorenz Räber ,
Francesco Prati ,
Carola-Bibiane Schönlieb ,
Michael Roberts ,
Martin Bennett
Circulation: Cardiovascular Imaging, Volume: 18, Issue: 11, Start page: e018133
Swansea University Author:
Daniel Obaid
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© 2025 The Authors. Circulation: Cardiovascular Imaging is published on behalf of the American Heart Association, Inc., by Wolters Kluwer Health, Inc. This is an open access article under the terms of the Creative Commons Attribution License.
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DOI (Published version): 10.1161/circimaging.125.018133
Abstract
BACKGROUND: Intracoronary optical coherence tomography (OCT) can identify changes following drug/device treatment and high-risk plaques, but analysis requires expert clinician or core laboratory interpretation, while artifacts and limited sampling markedly impair reproducibility. Assistive technolog...
| Published in: | Circulation: Cardiovascular Imaging |
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| ISSN: | 1941-9651 1942-0080 |
| Published: |
Wolters Kluwer Health, Inc.
2025
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa71376 |
| Abstract: |
BACKGROUND: Intracoronary optical coherence tomography (OCT) can identify changes following drug/device treatment and high-risk plaques, but analysis requires expert clinician or core laboratory interpretation, while artifacts and limited sampling markedly impair reproducibility. Assistive technologies such as artificial intelligence-based analysis may therefore aid both detailed OCT interpretation and patient management. We determined if artificial intelligence-based OCT analysis (AutoOCT) can rapidly process, optimize, and analyze OCT images, and identify plaque composition changes that predict drug success/failure and high-risk plaques. METHODS: AutoOCT deep learning artificial intelligence modules were designed to correct segmentation errors from poor-quality or artifact-containing OCT images, identify tissue/plaque composition, classify plaque types, measure multiple parameters including lumen area, lipid and calcium arcs, and fibrous cap thickness, and output segmented images and clinically useful parameters. Model development used 36 212 frames (127 whole pullbacks, 106 patients). Internal validation of tissue and plaque classification and measurements used ex vivo OCT pullbacks from autopsy arteries, while external validation for plaque stabilization and identifying high-risk plaques used core laboratory analysis of IBIS-4 (Integrated Biomarkers and Imaging Study-4) high-intensity statin (83 patients) and CLIMA (Relationship Between Coronary Plaque Morphology of Left Anterior Descending Artery and Long-Term Clinical Outcome Study; 62 patients) studies, respectively. RESULTS: AutoOCT recovered images containing common artifacts with measurements and tissue and plaque classification accuracy of 83% versus histology, equivalent to expert clinician readers. AutoOCT replicated core laboratory plaque composition changes after high-intensity statin, including reduced lesion lipid arc (13.3° versus 12.5°) and increased minimum fibrous cap thickness (18.9 µm versus 24.4 µm). AutoOCT also identified high-risk plaque features leading to patient events including minimal lumen area <3.5 mm2, Lipid arc >180°, and fibrous cap thickness <75 µm, similar to the CLIMA core laboratory. CONCLUSIONS: AutoOCT-based analysis of whole coronary artery OCT identifies tissue and plaque types and measures features correlating with plaque stabilization and high-risk plaques. Artificial intelligence-based OCT analysis may augment clinician or core laboratory analysis of intracoronary OCT images for trials of drug/device efficacy and identifying high-risk lesions. |
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| Keywords: |
artificial intelligence, biomarkers, deep learning, lipids, self-help devices |
| College: |
Faculty of Medicine, Health and Life Sciences |
| Funders: |
Supported by British Heart Foundation Grants PG/18/14/33562, RG13/14/30314, RE/24/130011, TA/F/20/210001 (London), Academy of Medical Sciences Starter Grants for Clinical Lecturers (REF: SGL030\1012), Innovate UK Advancing Precision Medicine 10069871, National Institutes of Health, R01 HL150608, EPSRC Cambridge Maths in Healthcare (Nr. EP/N014588/1) and Cambridge NIHR Biomedical Research Centres. |
| Issue: |
11 |
| Start Page: |
e018133 |