ArticleClinical neuroradiology2025
AI-Based Automated Quantification of Arterial Stenosis in Head and Neck CT Angiography: A Comparison with Manual Measurements from Digital Subtraction Angiography and CT Angiography.
Article in Clinical neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTo evaluate the performance of an artificial intelligence (AI) algorithm for automated quantification of arterial stenosis in head and neck CT angiography (CTA).
methodsPatients who received head and neck CTA and DSA between January 2019 and December 2021 in two centers were included. The quantitative performance of CerebralDoc per-lesion was evaluated through intraclass correlation coefficients (ICCs) and Bland-Altman analysis, comparing automated stenosis measurements and manual measurements across 0-100%, < 50%, ≥ 50% and ≥ 70% thresholds. Sensitivity analysis included linear and logistic regression, and subgroups analysis was performed to identify influencing factors.
results287 patients with 1765 lesions were analyzed. ICCs between CerebralDoc and DSA for ≥ 50% and ≥ 70% stenosis were excellent (0.955, 0.922, respectively), for 0-100% stenosis was good (0.735), and for < 50% stenosis was poor (0.056). For ≥ 50% and ≥ 70% stenosis of CerebralDoc and CTA manual measurements versus DSA, ICCs were close (0.955 vs 0.994; 0.922 vs 0.986), and differences were small (0.258% vs -0.362%; 0.369% vs -0.199%). The sensitivity analysis revealed that specific locations (V1, V2, V3, V4) and slender vessels have large or remarkable differences ranging from 15.551% to 44.238%.
conclusionCerebralDoc exhibited excellent performance in automatically quantifying arterial stenosis of ≥ 50% and ≥ 70% in head and neck CTA. However, further research was needed to improve its performance for < 50% stenosis and to address differences in specific locations and slender vessels.
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