Accuracy of segment anything model for classification of vascular stenosis in digital subtraction angiography

TL;DR

This study shows that Dr-SAM, a modified Segment Anything Model, accurately detects and grades vascular stenosis in DSA images, matching radiologists' assessments.

Contribution

Dr-SAM is a novel multi-stage framework optimized for vascular stenosis classification in DSA images.

Findings

Dr-SAM showed high agreement with radiologists for common iliac arteries and moderate agreement for external iliac arteries.

The model demonstrated excellent specificity and robust sensitivity across all vascular regions.

Dr-SAM's performance had no significant differences compared to radiologist grading.

Abstract

This retrospective study evaluates the diagnostic performance of an optimized comprehensive multi-stage framework based on the Segment Anything Model (SAM), which we named Dr-SAM, for detecting and grading vascular stenosis in the abdominal aorta and iliac arteries using digital subtraction angiography (DSA).

A total of 100 DSA examinations were conducted on 100 patients. The infrarenal abdominal aorta (AAI), common iliac arteries (CIA), and external iliac arteries (EIA) were independently evaluated by two experienced radiologists using a standardized 5-point grading scale. Dr-SAM analyzed the same DSA images, and its assessments were compared with the average stenosis grading provided by the radiologists. Diagnostic accuracy was evaluated using Cohen's kappa, specificity, sensitivity, and Wilcoxon signed-rank tests.

Interobserver agreement between radiologists, which established the reference standard, was strong (Cohen's kappa: CIA right = 0.95, CIA left = 0.94, EIA right = 0.98, EIA left = 0.98, AAI = 0.79). Dr-SAM showed high agreement with radiologist consensus for CIA (κ = 0.93 right, 0.91 left), moderate agreement for EIA (κ = 0.79 right, 0.76 left), and fair agreement for AAI (κ = 0.70). Dr-SAM demonstrated excellent specificity (up to 1.0) and robust sensitivity (0.67–0.83). Wilcoxon tests revealed no significant differences between Dr-SAM and radiologist grading (p > 0.05).

Dr-SAM proved to be an accurate and efficient tool for vascular assessment, with the potential to streamline diagnostic workflows and reduce variability in stenosis grading. Its ability to deliver rapid and consistent evaluations may contribute to earlier detection of disease and the optimization of treatment strategies. Further studies are needed to confirm these findings in prospective settings and to enhance its capabilities, particularly in the detection of occlusions.

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