Real-world approach to comprehensive artificial intelligence-aided CT evaluation of coronary artery disease in 530 patients: A retrospective study

Himanshu Gupta; Basil Spanopoulous; Edward Lubat; Glenn Krinsky; John Rutledge; Jacqueline H Fortier; Juan Grau; Rajiv Tayal

doi:10.1016/j.heliyon.2023.e19974

Real-world approach to comprehensive artificial intelligence-aided CT evaluation of coronary artery disease in 530 patients: A retrospective study

Heliyon. 2023 Sep 9;9(9):e19974. doi: 10.1016/j.heliyon.2023.e19974. eCollection 2023 Sep.

Authors

Himanshu Gupta¹, Basil Spanopoulous¹, Edward Lubat¹, Glenn Krinsky¹, John Rutledge¹, Jacqueline H Fortier¹, Juan Grau^{1

2}, Rajiv Tayal¹

Affiliations

¹ The Valley Hospital, Ridgewood, NJ, USA.
² The University of Ottawa Heart Institute, Ottawa, Ontario, Canada.

Abstract

Purpose: Recent guidelines provide broader support for the use of less invasive imaging modalities for the evaluation of patients with stable chest pain. Coronary CT angiography (CCTA) uses increasingly sophisticated techniques to improve evaluation of coronary lesions. The purpose of this study is to describe one center's experience implementing AI-assisted advanced imaging techniques to diagnose coronary artery disease.

Materials & methods: Retrospective study of patients who had AI-assisted CCTA interpretation, including a subgroup who underwent fractional flow reserve CT (FFR-CT) and invasive coronary angiography. Descriptive statistics summarized baseline characteristics and univariate statistics compared findings between groups of patients with and without anatomically and hemodynamically significant lesions based on FFR-CT. For patients who underwent invasive coronary angiography, concordance between CCTA and angiography was evaluated.

Results: Of 532 included patients, AI-assisted CCTA identified statistically significant difference in calcification scores, plaque types and total plaque volume between lesions <50% and ≥50% stenosis. CCTA results were mostly concordant with invasive coronary angiography. Importantly, we identified a subset of patients with less than 50% anatomical stenosis that demonstrated physiologically significant stenosis on FFR-CT and invasive coronary angiography.

Conclusions: AI-assisted CCTA and other advanced techniques are a tool to support high quality diagnostic assessment of coronary lesions in a clinical environment. Combined CCTA with FFRCT in mild to moderate coronary stenosis identifies patients with hemodynamically significant stenosis even when quantitative stenosis is <50%. Implementation of AI-assisted coronary CT angiography is feasible in a community hospital setting, but these technologies do not replace the need for expert review and clinical correlation.

Keywords: Artificial intelligence; Computed tomography; Coronary artery disease; Fractional flow reserve.