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

被引:3
作者
Gupta, Himanshu [1 ,3 ]
Spanopoulous, Basil [1 ]
Lubat, Edward [1 ]
Krinsky, Glenn [1 ]
Rutledge, John [1 ]
Fortier, Jacqueline H. [1 ]
Grau, Juan [1 ,2 ]
Tayal, Rajiv [1 ]
机构
[1] Valley Hosp, Ridgewood, NJ USA
[2] Univ Ottawa, Heart Inst, Ottawa, ON, Canada
[3] Radiol Adm, 223 North Dien Ave,Cheel 2, Ridgewood, NJ 07450 USA
关键词
Coronary artery disease; Computed tomography; Artificial intelligence; Fractional flow reserve; FRACTIONAL FLOW RESERVE; COMPUTED-TOMOGRAPHY; CHEST-PAIN; ANGIOGRAPHY;
D O I
10.1016/j.heliyon.2023.e19974
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
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