Background: The commercial coronary computed tomographic angiography artificial intelligence (CCTA-AI) platform has made great progress in clinical application. However, research is needed to elucidate the current stage of commercial AI platforms and the role of radiologists. This study compared the diagnostic performance of the commercial CCTA-AI platform with that of a reader based on a multicenter and multidevice sample.
Methods: A total of 318 patients with suspected coronary artery disease (CAD) who underwent both CCTA and invasive coronary angiography (ICA) were included in a multicenter and multidevice validation cohort between 2017 and 2021. The commercial CCTA-AI platform was used to automatically assess coronary artery stenosis by using ICA findings as the gold standard. The CCTA reader was completed by radiologists. The diagnostic performance of the commercial CCTA-AI platform and CCTA reader was evaluated at the patient and segment levels. The cutoff values of models 1 and 2 were 50% and 70% stenosis, respectively.
Results: It took 20.4 seconds to accomplish post-processing per patient when using the CCTA-AI platform, which was significantly shorter than the time taken to complete this task with the CCTA reader (1,112.1 s). In the patient-based analysis, the area under the curve (AUC) was 0.85 using the CCTA-AI platform and 0.61 using the CCTA reader in model 1 (stenosis ratio: 50%). In contrast, the AUC was 0.78 using the CCTA-AI platform and 0.64 using the CCTA reader in model 2 (stenosis ratio: 70%). In the segment-based analysis, the AUCs of CCTA-AI were slightly better than those of the readers. The negative predictive value (NPV) increased from model 1 to model 2. Furthermore, the diagnostic performance was better for larger-diameter arteries.
Conclusions: The commercial CCTA-AI platform may provide a feasible solution for the diagnosis of coronary artery stenosis, and it has a diagnostic performance that is slightly better than that of a radiologist with a moderate level of experience (5-10 years of experience).
Keywords: Coronary artery computed tomographic angiography; artificial intelligence (AI); coronary artery disease (CAD); coronary artery stenosis.
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