Fully automated assessment of post-TEVAR follow-up CT scan using Deep Learning-Based Segmentation.

OBJECTIVE: The objective of this study was to apply an artificial intelligence (AI) pipeline for the automatic analysis of follow-up Computed Tomography Angiography (CTA) after Thoracic Endovascular Aortic Repair (TEVAR). MATERIALS AND METHODS: A deep-learning network was developed to automatically measure the mean diameters of the proximal (D_LZP) and distal (D_LZD) landing zones, stent length (L), maximum aneurysm diameter (D_MAX), and aneurysm volume. Segmentation accuracy was assessed with the Dice Similarity Coefficient (DSC), and agreement with manual measurements using the Intraclass Correlation Coefficient (ICC). Manual measurements were obtained by an experienced vascular surgeon using dedicated software (EndoSize), based on standardized centerline landmarks. RESULTS: The study included 45 TEVAR patients; 3 Computed Tomography (CT) scans (6.6%) were excluded due to segmentation failure, leaving 84 CT scans for analysis (42 preoperative and 42 follow-up). At 1 month and 1 year, D_LZP was 32 ±4.7 mm (ICC 0.86) and 33.41 ±5.8 mm (ICC 0.78), D_LZD 30.15 ±4.54 mm (ICC 0.97) and 31.35 ±4.85 mm (ICC 0.83), while stent length remained stable (∼212 mm, ICC >0.98). D_MAX decreased from 57.4 ±14.8 mm to 55.5 ±13.2 mm (p<0.0001), and aneurysm volume from 68.6 ±86.1 mm3 to 54.3 ±103.7 mm3 (p<0.0001), with a strong correlation between changes in D_MAX and volume (r=0.77, p<0.0001). CONCLUSION: The proposed AI-based pipeline enables reliable and reproducible quantification of stent-graft landing zones after TEVAR. The strong agreement with manual measurements and the detection of significant morphological changes over time support its potential for standardized, objective, and clinically relevant follow-up.