Diagnostic accuracy in coronary CT angiography analysis: artificial intelligence versus human assessment

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.

Keywords: Atherosclerosis; CORONARY ARTERY DISEASE; Computed Tomography Angiography; Coronary Stenosis.

Figure 1. Receiver operating characteristic (ROC) curves for the prediction of coronary artery stenosis on a per-patient level. (A) The ROC curves for predicting ≥50% stenosis defined by quantitative coronary angiography per patient. (B) The ROC curves for predicting ≥70% stenosis defined by QCA per patient. ***p<0.001. AI-QCT, atherosclerosis imaging-quantitative CT; AUC, area under the curve.

Figure 2. Receiver operating characteristic (ROC) curves for the prediction of coronary artery stenosis on a per-vessel level. (A) The ROC curves for predicting ≥50% stenosis defined by quantitative coronary angiography per patient. (B) The ROC curves for predicting ≥70% stenosis defined by QCA per patient. ***p<0.001. AI-QCT, atherosclerosis imaging-quantitative CT; AUC, area under the curve.

Figure 3. Distribution of per-patient CAD-RADS scores for AI-QCT and clinical readers. The histograms display the frequency of CAD-RADS scores assigned by QCA, AI-QCT and level 3 (L3) and level 2 readers (L2). Each panel represents the CAD-RADS score distribution for the particular reader or modality (0: no stenosis/no plaque, 1: 0 reader 24% stenosis, 2: 25%–49% stenosis, 3: 50%–69% stenosis, 4: 70%–99% stenosis, 5: 100% stenosis). AI-QCT, artificial intelligence-QCT; CAD-RADS, Coronary Artery Disease-Reporting and Data System; QCA, quantitative coronary angiography.

Figure 4. Interobserver correlation heatmaps comparing CAD-RADS score assessments. (A) The level of agreement between quantitative coronary angiography (QCA) as the gold standard versus intelligence-guided algorithm for assessment of coronary stenosis (AI-QCT). (B) The level of agreement between QCA as the gold standard versus the level 3 reader (L3). (C) The level of agreement between QCA as the gold standard versus the level 2 reader 1 (L2–1). (D) The level of agreement between QCA as the gold standard versus the level 2 reader 2 (L2–2). In all panels, each cell represents the frequency of agreement counts, shaded according to the legend, across the range of possible CAD-RADS scores (0–5) for each rater against the gold standard. AI-QCT, artificial intelligence quantitative CT; CAD-RADS, Coronary Artery Disease Reporting and Data System; QCA, quantitative coronary angiography.

Figure 5. Receiver operating characteristic (ROC) curves for the prediction of coronary artery stenosis for patients with plaque values above and below the median. (A) The ROC curves for predicting ≥50% stenosis defined by quantitative coronary angiography on a per-patient basis for patients with total plaque volume above the median (218.1 mm³; A) and below the median (B). *p<0.05; **p<0.01. AI-QCT, atherosclerosis imaging-quantitative CT; AUC, area under the curve.