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Review
. 2025 May 15;6(4):529-539.
doi: 10.1093/ehjdh/ztaf053. eCollection 2025 Jul.

Review and recommendations for using artificial intelligence in intracoronary optical coherence tomography analysis

Xu Chen  1 , Yuan Huang  1   2 , Benn Jessney  1 , Jason Sangha  1 , Sophie Gu  1 , Carola-Bibiane Schönlieb  2 , Martin Bennett  1 , Michael Roberts  1   2
Affiliations
Review

Review and recommendations for using artificial intelligence in intracoronary optical coherence tomography analysis

Xu Chen et al. Eur Heart J Digit Health. .

Abstract

Artificial intelligence (AI) tools hold great promise for the rapid and accurate diagnosis of coronary artery disease (CAD) from intravascular optical coherent tomography (IVOCT) images. Numerous papers have been published describing AI-based models for different diagnostic tasks, yet it remains unclear, which models have potential clinical utility and have been properly validated. This systematic review considered published literature between January 2015 and December 2024 describing AI-based diagnosis of CAD using IVOCT. Our search identified 8600 studies, with 629 included after initial screening and 39 studies included in the final systematic review after quality screening. Our findings indicate that most of the identified models are not currently suitable for clinical use, primarily due to methodological flaws and underlying biases. To address these issues, we provide recommendations to improve model quality and research practices to enhance the development of clinically useful AI products.

Keywords: Artificial intelligence; Deep learning; Intravascular imaging; Machine learning; Optical coherance tomography.

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Conflict of interest statement

Conflict of interest: M.B. and M.R. are founders of Octiocor Ltd., a company developing software for analysing IVOCT imaging.

Figures

Graphical Abstract
Graphical Abstract
Figure 1
Figure 1
An overview of coronary artery and plaque anatomy, corresponding Cartesian intravascular optical coherent tomography (IVOCT) image, IVOCT detection and segmentation, and use to predict pathology underlying events, measure plaque components, and classify plaques in the polar view.
Figure 2
Figure 2
(Top) PRISMA diagram indicating the inclusion/exclusion of papers at each stage of the review. (Bottom) Detail of the quality screening stage.
Figure 3
Figure 3
Overview of data sources and modalities in the literature.
Figure 4
Figure 4
Overview of model development strategies in the literature.
See this image and copyright information in PMC

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