AI-powered OCT shows similar performance to IVUS for plaque burden quantification: Head-to-head benchmark study against histopathology of native coronary arteries

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ID: 332069
2026
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Abstract
Abstract Background Optical coherence tomography (OCT) provides excellent resolution at the cost of shallow penetration, limiting its ability for plaque burden (PB) assessment. A deep learning (DL) algorithm was therefore developed to provide fully automated assessment of PB and vessel size from OCT. Objectives We aimed to perform a head-to-head comparison of AI-powered OCT versus intravascular ultrasound (IVUS) for PB quantification, using histopathology as the reference standard. Methods Ten post-mortem human hearts were harvested. All the 29 major epicardial coronary arteries were perfused and underwent OCT and IVUS pullbacks. After imaging, the arteries were serially sectioned at 1-mm intervals for histopathology. Lumen and vessel contours were derived fully automatically from OCT using the DL algorithm and semi-automatically from IVUS. Histopathological measurements were obtained by experienced analysts. Statistical analyses accounted for within-patient clustering. Results A total of 852 paired OCT-IVUS-histopathology cross-sections were obtained. AI-powered OCT showed strong correlations with histopathology for the assessment of lumen area (r=0.89 [95% CI: 0.88–0.91]), vessel area (r=0.85 [95% CI: 0.83–0.87]), and PB (r=0.76 [95% CI: 0.73–0.79]). The corresponding correlations between IVUS and histopathology were 0.90 (95% CI: 0.89–0.92), 0.86 (95% CI: 0.84–0.88), and 0.79 (95% CI: 0.76–0.81), respectively. Comparisons of the OCT–histopathology and IVUS–histopathology correlation coefficients yielded p=0.336 for lumen area, p=0.444 for vessel area, and p=0.161 for PB. Both OCT and IVUS showed good diagnostic performance in identifying PB>65%, with the area under the receiver operating characteristic curve being 0.92 (95% CI: 0.90-0.94) and 0.92 (95% CI: 0.88-0.95), respectively. Conclusions In this histopathology-validation study of 10 post-mortem human hearts, AI-powered OCT enabled fully automated plaque burden quantification and correlated strongly with histopathology, with no statistically significant difference between the OCT–histopathology and IVUS–histopathology correlations.
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Authors Jiayue Huang, R H Dai, Yu Xu, Miao Chu, Su Zhang, Simone Fezzi, Seán O. Hynes, Yining Wang, William C. Wijns, Shengxian Tu
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Journal European Heart Journal - Digital Health
Year 2026
DOI
10.1093/ehjdh/ztag163
URL
https://doi.org/10.1093/ehjdh/ztag163
Keywords Keywords not found

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