AI-Assisted Characterization of Coronary High-Risk Plaques in Troponin-Negative Patients Presenting with Atypical Chest Pain: A 256-Slice CCTA Analysis

Abstract

Background: While coronary computed tomography angiography (CCTA) is an established modality for evaluating chest pain, there is limited up-to-date data on the utility of advanced artificial intelligence (AI)-assisted cardiac software utilizing 256-slice computed tomography (CT) scans to systematically detect and characterize specific high-risk plaque features (HRPF) in the clinical subset of patients with atypical chest pain and troponin-negative (TNEG). Objective: To analyze the characteristics of coronary artery HRPF on CCTA using AI-assisted software and a 256-slice CT scanner in patients presenting with atypical chest pain and TNEG. Materials and Methods: A retrospective analysis was conducted on 118 eligible TNEG patients with documented coronary plaques who underwent CCTA between January 2020 and April 2024. Data were retrieved via the Picture Archiving and Communication System (PACS) and Hospital Information System (HIS). Coronary artery plaque analysis was performed using AI-assisted cardiac software (Cardiac Suite) on a 256-slice CT scanner. The presence and types of HRPF were evaluated, including: 1) low-attenuation plaque (<30 Hounsfield units), 2) positive remodeling, 3) spotty calcification, and 4) the napkin-ring sign. Statistical analyses were performed using the unpaired t-test, chi-square test, and Cramer’s V to determine associations between variables. Results: Of the 118 cases evaluated, obstructive coronary artery disease (CAD) was identified in 66 cases (56.0%) and non-obstructive CAD in 52 cases (44.0%). HRPF were significantly more prevalent in the obstructive CAD group (43/66 cases, 65.2%) compared to the non-obstructive CAD group (5/52 cases, 9.6%). The AI-assisted software efficiently identified and characterized plaque morphology. Low-attenuation plaque was the most common HRPF characteristic, observed in 38 cases (32.2%), predominantly within obstructive lesions (stenosis >50%). A high coronary artery calcium score (CACS >300, percentiles P3 and P4) demonstrated a strong, statistically significant association with HRPF, present in 51 cases (43.2%). Based on these data, it is hypothesized that the transition from P2 (CACS 101-300) to P3/P4 (CACS >300) corresponds to a biological shift from non-HRPF to HRPF. Notably, a 36-year-old male smoker presented with distinct obstructive HRPF despite a CACS of 0. Conclusion: AI-assisted cardiac software provides rapid, precise identification and characterization of coronary plaques, particularly vulnerable HRPF. Upgrading CT infrastructure with integrated AI-assisted analysis tools significantly enhances diagnostic accuracy. This software acts as a crucial clinical decision-making aid to accurately rule out or confirm coronary etiologies in patients presenting with atypical chest pain and TNEG.