Abstract
BACKGROUND: Clinical adoption of cardiovascular magnetic resonance (CMR) is hampered by procedural complexity and magnetic resonance imaging access. OBJECTIVES: The objective of the study was to investigate the benefits of artificial intelligence (AI)-assisted CMR in clinical practice. METHODS: Patients at a tertiary-care center underwent CMR via conventional, operator-directed (ODS) or AI-assisted (AIA) scanning over 3 years. Scan time, scan time variation, image quality using a validated 5-point scale, scan failure, and survival were compared between groups, stratified across CMR indications and technologists' experience. RESULTS: From 2020 to 2024, 6,080 consecutive patients (age 57 ± 17 years, 55% males) underwent 6,545 studies (1,635, 25% AIA, 4,910, 75% ODS) for 5 primary indications. Average scan time and its coefficient of variation were reduced by 23% and 31%, respectively, with AIA compared to ODS (37.9 ± 6.9 vs 49.2 ± 13.0 min; P < 0.001). Scan time reduction by AIA was consistent across indications and range of technologists' experience, and remained significant after adjusting for key demographic and clinical differences (P < 0.0001) or propensity score. Scan quality score was higher in AIA for cine images (4.09 ± 0.8 vs 4.03 ± 0.8; P < 0.001), late gadolinium enhancement (4.08 ± 0.8 vs 3.98 ± 0.7; P < 0.001), and perfusion (4.28 ± 0.6 vs 4.14 ± 0.6; P < 0.001). Scan failure leading to 90-day repeat CMR was lower in AIA compared to ODS (0.6% vs 1.4%, P < 0.001). After its introduction in 2021, AIA use progressively increased to 78% by 2023. At a median follow-up of 29 months, patient survival did not differ between groups (log-rank P = 0.500). CONCLUSIONS: In this nonrandomized comparison, AIA improved scan time, scan time consistency, image quality, and 90-day scan failure across top CMR indications, compared to ODS.