A novel algorithm for automated analysis of coronary CTA-derived FFR in identifying ischemia-specific CAD: A multicenter study.

Background: Coronary artery fractional flow reserve derived from coronary computed tomography angiography (CTA) is increasingly favored due to its non-invasive nature.

Purpose: We aim to validate the ability of a novel on-site analysis model for computed tomography derived fractional flow reserve (CT FFR) using deep learning and level set algorithms to identify lesion-specific ischemic coronary artery disease (CAD).

Methods: A retrospective analysis was conducted on 198 vessels from 171 patients from four medical centers who underwent CTA and invasive fractional flow reserve (FFR) examinations. Using invasive FFR and invasive coronary angiography (ICA) as reference standards, a new model based on deep learning and level set algorithm, as well as an artificial intelligence (AI) platform based on deep learning, were used to compare CT FFR values and stenosis rates.

Results: Compared with the ai platform, the new model has a single-vessel accuracy of 85.9% [95% confidence interval (95% CI) 80-90), higher than the AI platform's 66.7% (95% CI: 59.6-73.1). The sensitivity is 82.8% (95% CI: 72.8-89.7), specificity is 88.3% (95% CI: 80.5-93.4), and the area under the curve (AUC) is 0.9 (95% CI: 0.85-0.94). The stenosis rate measured by model was much higher than ICA (r = 0.84, p < 0.0001). Using the standard FFR threshold of 0.8, the new model accurately identified 24 vessels with FFR values between 0.75 and 0.8. The AI platform exhibits significant differences in accuracy within different stenosis ranges (p = 0.022).

Conclusion: The novel CT FFR algorithm based on a combination of deep learning and level set algorithms to optimize coronary artery 3D reconstruction may have a potential value in fully automatic on-site analysis of specific coronary ischemia.