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

作者全名："Yang, Yongwei; Huan, Xinyue; Guo, Dajing; Wang, Xiaolin; Niu, Shengwen; Li, Kunhua"

作者地址："[Yang, Yongwei; Huan, Xinyue; Guo, Dajing; Wang, Xiaolin; Niu, Shengwen; Li, Kunhua] Chongqing Med Univ, Affiliated Hosp 2, Dept Radiol, 74 Linjiang Rd, Chongqing 400010, Peoples R China; [Yang, Yongwei] Fifth Peoples Hosp Chongqing, Dept Radiol, Chongqing, Peoples R China"

通信作者："Li, KH (通讯作者)，Chongqing Med Univ, Affiliated Hosp 2, Dept Radiol, 74 Linjiang Rd, Chongqing 400010, Peoples R China."

来源：RADIOLOGIA MEDICA

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001031580400002

JCR分区：Q1

影响因子：8.9

年份：2023

卷号：　

期号：　

开始页：　

结束页：　

文献类型：Article; Early Access

关键词：Computed tomography angiography; Artificial intelligence; Arteries; Stroke

摘要："PurposeTo 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 methodsPatients 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. The performance of CerebralDoc across levels (per-patient, per-region, per-vessel, and per-segment) and thresholds (& GE; 50%, & GE; 70%, and = 100%) was evaluated. Logistic regression was performed to identify independent factors associated with false negative results.Results296 patients were analyzed. Specificity across levels and thresholds was high, exceeding 92%. The area under the curve ranged from poor (0.615, 95% CI: 0.544, 0.686; at the region-based analysis for stenosis & GE; 70%) to excellent (0.945, 95% CI: 0.905, 0.985; at the patient-based analysis for stenosis & GE; 50%). Sensitivity ranged from 0.714 (95% CI: 0.675, 0.750) at the segment-based analysis for stenosis & GE; 70% to 0.895 (95% CI: 0.849, 0.919) at the patient-based analysis for stenosis & GE; 50%. The multiple logistic regression analysis revealed that false negative results were primarily more likely to specific stenosis locations (particularly the M2 segment and skull base segment of the internal carotid artery) and occlusion.ConclusionsCerebralDoc has the potential to automated stenosis detection on head and neck CTA, but further efforts are needed to optimize its performance."

基金机构：Chongqing medical scientific research project (Joint project of Chongqing Health Commission and Science and Technology Bureau) [2023MSXM014]; Chongqing medical scientific research project (Joint project of Chongqing Health Commission and Science and Technology Bureau) [2023MSXM014]

基金资助正文：This work was supported by the Chongqing medical scientific research project (Joint project of Chongqing Health Commission and Science and Technology Bureau) under Grant No.2023MSXM014.