1. Diagnostic accuracy in coronary CT angiography analysis: artificial intelligence versus human assessment.
- Author
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Bernardo R, Nurmohamed NS, Bom MJ, Jukema R, de Winter RW, Sprengers R, Stroes ESG, Min JK, Earls J, Danad I, Choi AD, and Knaapen P
- Subjects
- Humans, Female, Male, Middle Aged, Reproducibility of Results, Aged, Predictive Value of Tests, Severity of Illness Index, Observer Variation, Algorithms, Retrospective Studies, Radiographic Image Interpretation, Computer-Assisted methods, Computed Tomography Angiography methods, Coronary Angiography methods, Artificial Intelligence, Coronary Stenosis diagnostic imaging, Coronary Artery Disease diagnostic imaging, Coronary Artery Disease diagnosis, Coronary Vessels diagnostic imaging
- Abstract
Background: Visual assessment of coronary CT angiography (CCTA) is time-consuming, influenced by reader experience and prone to interobserver variability. This study evaluated a novel algorithm for coronary stenosis quantification (atherosclerosis imaging quantitative CT, AI-QCT)., Methods: The study included 208 patients with suspected coronary artery disease (CAD) undergoing CCTA in Perfusion Imaging and CT Coronary Angiography With Invasive Coronary Angiography-1. AI-QCT and blinded readers assessed coronary artery stenosis following the Coronary Artery Disease Reporting and Data System consensus. Accuracy of AI-QCT was compared with a level 3 and two level 2 clinical readers against an invasive quantitative coronary angiography (QCA) reference standard (≥50% stenosis) in an area under the curve (AUC) analysis, evaluated per-patient and per-vessel and stratified by plaque volume., Results: Among 208 patients with a mean age of 58±9 years and 37% women, AI-QCT demonstrated superior concordance with QCA compared with clinical CCTA assessments. For the detection of obstructive stenosis (≥50%), AI-QCT achieved an AUC of 0.91 on a per-patient level, outperforming level 3 (AUC 0.77; p<0.002) and level 2 readers (AUC 0.79; p<0.001 and AUC 0.76; p<0.001). The advantage of AI-QCT was most prominent in those with above median plaque volume. At the per-vessel level, AI-QCT achieved an AUC of 0.86, similar to level 3 (AUC 0.82; p=0.098) stenosis, but superior to level 2 readers (both AUC 0.69; p<0.001)., Conclusions: AI-QCT demonstrated superior agreement with invasive QCA compared to clinical CCTA assessments, particularly compared to level 2 readers in those with extensive CAD. Integrating AI-QCT into routine clinical practice holds promise for improving the accuracy of stenosis quantification through CCTA., Competing Interests: Competing interests: NSN reports grants from the Dutch Heart Foundation (Dekker 03-007-2023-0068), European Atherosclerosis Society (2023), research funding/speaker fees from Cleerly, Daiichi Sankyo and Novartis and is co-founder of Lipid Tools. JKM and JE are employees of Cleerly. ADC reports grant support from GW Heart and Vascular Institute, equity in Cleerly and consulting with Siemens Healthineers, Amgen and Cleerly. PK has received research grants from Cleerly and HeartFlow. The other authors report no conflicts of interest., (© Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.)
- Published
- 2025
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