Opportunistic screening of coronary artery calcification on non-gated conventional CT scans using artificial intelligence

Abstract

Abstract Background Recent advancements in artificial intelligence (AI) led to the development of automatic Coronary artery calcification (CAC) analysis based on chest CT scans. The objective of this study is to assess the impact of AI-based CAC evaluation on improving the allocation of add-on therapies and further evaluation. Methods We used a novel propriety AI software to estimate CAC from non-gated, non-contrast chest CT scans. Patients were categorized into groups: low CAC 0-99 Agatston unit (AU), moderate CAC 100-399AU, and high CAC ≥ 400 AU. Exclusion criteria included prior myocardial infarction, percutaneous coronary intervention (PCI), coronary artery bypass graft (CABG), and life expectancy < 2 years. Patients classified as high CAC were invited to a dedicated clinic to perform a risk factor and clinical assessment, initiate appropriate medication, and refer to further testing as needed. For low-moderate CAC, referring physicians were informed of CAC status, encouraging guideline-based optimal preventive management. Results 1272 eligible patients between January 1th, 2023 to February 29th, 2024 were evaluated for inclusion. 641/1272 (50.3%) were excluded. Out of 631 enrolled patients, 84 (13%) patients were classified as high CAC, 163 (26%) as moderate and 348 (61%) as low CAC. At the dedicated clinic, appropriate low-density lipoprotein (LDL) goals were assigned and statins were initiated or dose adjusted where necessary. Twenty patients were referred to myocardial perfusion imaging, of which 2 were referred for invasive coronary angiography, one of them underwent PCI (Figure), and the other was referred for CABG. Conclusion This ongoing study indicates routine CAC quantification using AI software on chest CT scans can identify patients who may benefit from preventive cardiology services and lead them to appropriate cardiac treatment pathway. Further follow-up is required to assess its clinical impact.