Use of artificial intelligence-assisted measurement of aortic diameter in clinical decision making about abdominal aortic aneurysm repair

OBJECTIVES: Decisions to perform abdominal aortic aneurysm (AAA) repair are dependent on aneurysm size, but variation in size measurement leads to inconsistency in management. AI-assisted systems have potential to improve repeatability of measuring aortic dimensions. This study compared the repeatability and agreement in clinical decision-making between using artificial intelligence (AI)-automated and traditional semi-automated methods for measuring abdominal aortic aneurysm (AAA) size. MATERIALS AND METHODS: Computed tomography angiogram scans from 142 patients who had scans at baseline (n = 142), 1 year (n = 100), 2 years (n = 56) and 3 years (n = 4) were analysed using semi-automated and AI-assisted automated systems. Three observers measured maximal AAA diameter and volume twice with each method. Intra- and inter-observer repeatability were assessed using reproducibility coefficients (RC). Measurements were used to decide if AAA repair was required according to clinical guidelines, and agreement was evaluated using Kappa coefficients (K). The ability of AI-assisted measurements to predict actual requirement for AAA repair was assessed using Cox proportional hazard analysis. RESULTS: AI-assisted measurements had perfect intra- and inter-observer repeatability (RC = 0) which were significantly superior to traditional measurements (RC for diameter: 1.9-5.6 mm; volume: 7.8-22.6 cm³, p < 0.001). Agreement about AAA repair was superior using AI-assisted (K = 1) than traditional (K = 0.55-0.70) measurements. Baseline AI-assisted measurements predicted actual requirement for AAA repair (Hazard ratio, HR, per mm diameter increase 1.12, 95% confidence intervals, CI, 1.03-1.22, HR per cm³ volume increase 1.02, 95% CI 1.01-1.02, p < 0.001). CONCLUSION: The findings suggest AI-assisted measurement of AAA size would enhance the consistency of decisions about AAA repair.