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

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.