Plaque burden estimated from optical coherence tomography with deep learning: In vivo validation using co‐registered intravascular ultrasound

Objectives The objective of the present study was to compare plaque burden (PB) calculated from optical coherence tomography (OCT) using deep learning (DL) with PB derived from co‐registered intravascular ultrasound (IVUS). Background A DL algorithm was developed for automated plaque characterization and PB quantification from OCT images. However, the performance of this algorithm for PB quantification has not been validated. Methods Five‐year follow‐up OCT and IVUS images from 15 patients implanted with bioresorbable vascular scaffold (BVS) at baseline were analyzed. Precise co‐registration for 72 anatomical slices was achieved utilizing unique BVS radiopaque markers. PB derived from OCT DL and IVUS were compared. OCT cross‐sections were divided into four subgroups with different media visibility level. The impact of media visibility on the numerical difference between OCT‐derived and IVUS‐derived PB was investigated. The stent sizes selected by OCT DL and IVUS were compared. Results Sixty‐four paired OCT and IVUS cross‐sections were compared. OCT DL showed good concordance with IVUS for PB assessment (ICC = 0.81, difference = −3.53 ± 6.17%, p < 0.001). The numerical difference between OCT DL‐derived PB and IVUS‐derived PB was not substantially impacted by missing segments of media visualization ( p = 0.21). OCT DL showed a diagnostic accuracy of 92% in identifying PB > 65%. The stent sizes selected by OCT DL were smaller compared to the ones selected by IVUS (difference = 0.30 ± 0.34 mm, p < 0.001). Conclusions The DL algorithm provides a feasible and reliable method for automated PB estimation from OCT, irrespective of media visibility. OCT DL showed good diagnostic accuracy in identifying PB > 65%, revealing its potential to complement conventional OCT imaging.