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.

Purpose: To evaluate the performance of an artificial intelligence (AI) algorithm for automated quantification of arterial stenosis in head and neck CT angiography (CTA).

Methods: Patients 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.

A Comparative Study of AI-Based Automated and Manual Postprocessing of Head and Neck CT Angiography: An Independent External Validation with Multi-Vendor and Multi-Center Data.

Purpose: To externally validate the performance of automated postprocessing (AP) on head and neck CT Angiography (CTA) and compare it with manual postprocessing (MP).

Methods: This retrospective study included head and neck CTA-exams of patients from three tertiary hospitals acquired on CT scanners from five manufacturers. AP was performed by CerebralDoc.

Performance of deep learning-based autodetection of arterial stenosis on head and neck CT angiography: an independent external validation study.

Purpose: To externally validate the performance of automated stenosis detection on head and neck CT angiography (CTA) and investigate the impact factors using an independent bi-center dataset with digital subtraction angiography (DSA) as the ground truth.

Material And Methods: Patients who underwent head and neck CTA and DSA between January 2019 and December 2021 were retrospectively included. The degree of stenosis was automatically evaluated using CerebralDoc based on CTA.