Key result
AI identifies calcified coronary plaques on OCT with ~75% accuracy compared to manual scoring.
Why the study?
Accurate and rapid interpretation of intravascular OCT images for coronary artery calcification assessment is essential to improve clinical outcomes during PCI.
Does an automated AI software algorithm accurately identify and quantify coronary plaque calcification compared to manual OCT image analysis?
Observational
Does an automated AI software algorithm accurately identify and quantify coronary plaque calcification compared to manual OCT image analysis?
Effect estimate: ρ 0.84 (95% CI 0.81-0.87)
p-value: p=≤0.001
An automated AI algorithm can rapidly and accurately map coronary calcium in intravascular OCT images, showing good agreement with expert manual analysis.
May support faster OCT calcium mapping; hypothesis-generating and requires prospective validation before clinical use.
Background/Aims: Intravascular imaging during percutaneous coronary intervention (PCI) improves clinical outcomes; however, is dependent on accurate and rapid interpretation of the images generated. This study aimed to compare coronary artery calcification assessment using a novel automated artificial intelligence (AI) software algorithm with manual optical coherence tomography (OCT) image analysis. Methods and Results: A deep neural network based on a UNet-like architecture was developed and trained to identify calcified atherosclerotic plaque from an independent dataset of expert-annotated clinical intravascular OCT pullbacks. The AI network was subsequently validated on previously unseen clinical OCT pullbacks that were manually annotated for plaque calcium and used to quantify clinically relevant calcified plaque characteristics. Correlation and agreement between the expert-annotated images and the model predictions were evaluated. In total, 8259 cross-sectional images comprised the training and internal validation dataset. Pixel-based classification by the AI model performed best to identify calcified plaque (AUC = 0.96), with an overall diagnostic accuracy of 73.3%. During independent external validation, the model correctly identified 934 of the 1248 calcified plaques, corresponding to a diagnostic accuracy of 74.8%. The AI model performed well in assessing the calculated OCT-calcium score (ρ = 0.84; 95% confidence interval [CI], 0.81–0.87, p ≤ 0.001). Conclusions: Implementation of an automated AI software algorithm provides a rapid and efficient method to comprehensively map coronary calcium in intravascular OCT images. With further training and refinement, it is anticipated that the AI machine learning software will continue to improve, enabling new robust tools for clinical OCT calcium detection to better guide PCI procedures.
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McCarthy et al. (2025) conducted an observational in Coronary artery calcification. Automated artificial intelligence (AI) software algorithm vs. Manual optical coherence tomography (OCT) image analysis was evaluated on Identification of calcified plaque and OCT-calcium score assessment (ρ 0.84, 95% CI 0.81-0.87, p=≤0.001). An automated AI software algorithm identified calcified coronary plaques on OCT with 74.8% diagnostic accuracy and correlated well with manual calcium scoring (ρ=0.84; 95% CI 0.81-0.87; p≤0.001).
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