PDF(Pubmed)
Abstract：
BACKGROUND: This multicenter, double-blinded, randomized controlled trial (RCT) aims to assess the impact of an artificial intelligence (AI)-based model on the efficacy of intracranial aneurysm detection in CT angiography (CTA) and its influence on patients\' short-term and long-term outcomes.
METHODS: Study design: Prospective, multicenter, double-blinded RCT.
METHODS: The model was designed for the automatic detection of intracranial aneurysms from original CTA images.
METHODS: Adult inpatients and outpatients who are scheduled for head CTA scanning. Randomization groups: (1) Experimental Group: Head CTA interpreted by radiologists with the assistance of the True-AI-integrated intracranial aneurysm diagnosis strategy (True-AI arm). (2) Control Group: Head CTA interpreted by radiologists with the assistance of the Sham-AI-integrated intracranial aneurysm diagnosis strategy (Sham-AI arm).
METHODS: Block randomization, stratified by center, gender, and age group.
METHODS: Coprimary outcomes of superiority in patient-level sensitivity and noninferiority in specificity for the True-AI arm to the Sham-AI arm in intracranial aneurysms.
RESULTS: Diagnostic performance for other intracranial lesions, detection rates, workload of CTA interpretation, resource utilization, treatment-related clinical events, aneurysm-related events, quality of life, and cost-effectiveness analysis.
METHODS: Study participants and participating radiologists will be blinded to the intervention.
METHODS: Based on our pilot study, the patient-level sensitivity is assumed to be 0.65 for the Sham-AI arm and 0.75 for the True-AI arm, with specificities of 0.90 and 0.88, respectively. The prevalence of intracranial aneurysms for patients undergoing head CTA in the hospital is approximately 12%. To establish superiority in sensitivity and noninferiority in specificity with a margin of 5% using a one-sided α = 0.025 to ensure that the power of coprimary endpoint testing reached 0.80 and a 5% attrition rate, the sample size was determined to be 6450 in a 1:1 allocation to True-AI or Sham-AI arm.
CONCLUSIONS: The study will determine the precise impact of the AI system on the detection performance for intracranial aneurysms in a double-blinded design and following the real-world effects on patients\' short-term and long-term outcomes.
BACKGROUND: This trial has been registered with the NIH, U.S. National Library of Medicine at ClinicalTrials.gov, ID: NCT06118840 . Registered 11 November 2023.
METHODS: Study design: Prospective, multicenter, double-blinded RCT.
METHODS: The model was designed for the automatic detection of intracranial aneurysms from original CTA images.
METHODS: Adult inpatients and outpatients who are scheduled for head CTA scanning. Randomization groups: (1) Experimental Group: Head CTA interpreted by radiologists with the assistance of the True-AI-integrated intracranial aneurysm diagnosis strategy (True-AI arm). (2) Control Group: Head CTA interpreted by radiologists with the assistance of the Sham-AI-integrated intracranial aneurysm diagnosis strategy (Sham-AI arm).
METHODS: Block randomization, stratified by center, gender, and age group.
METHODS: Coprimary outcomes of superiority in patient-level sensitivity and noninferiority in specificity for the True-AI arm to the Sham-AI arm in intracranial aneurysms.
RESULTS: Diagnostic performance for other intracranial lesions, detection rates, workload of CTA interpretation, resource utilization, treatment-related clinical events, aneurysm-related events, quality of life, and cost-effectiveness analysis.
METHODS: Study participants and participating radiologists will be blinded to the intervention.
METHODS: Based on our pilot study, the patient-level sensitivity is assumed to be 0.65 for the Sham-AI arm and 0.75 for the True-AI arm, with specificities of 0.90 and 0.88, respectively. The prevalence of intracranial aneurysms for patients undergoing head CTA in the hospital is approximately 12%. To establish superiority in sensitivity and noninferiority in specificity with a margin of 5% using a one-sided α = 0.025 to ensure that the power of coprimary endpoint testing reached 0.80 and a 5% attrition rate, the sample size was determined to be 6450 in a 1:1 allocation to True-AI or Sham-AI arm.
CONCLUSIONS: The study will determine the precise impact of the AI system on the detection performance for intracranial aneurysms in a double-blinded design and following the real-world effects on patients\' short-term and long-term outcomes.
BACKGROUND: This trial has been registered with the NIH, U.S. National Library of Medicine at ClinicalTrials.gov, ID: NCT06118840 . Registered 11 November 2023.
摘要：
背景：这个多中心，双盲,随机对照试验(RCT)旨在评估基于人工智能(AI)的模型对CT血管造影(CTA)中颅内动脉瘤检测效果的影响及其对患者短期和长期结局的影响.
方法：研究设计：前瞻性，多中心，双盲RCT。
方法：该模型设计用于从原始CTA图像自动检测颅内动脉瘤。
方法：安排头部CTA扫描的成人住院患者和门诊患者。随机分组：（1）实验组：放射科医师在True-AI整合颅内动脉瘤诊断策略（True-AIarm）的协助下解释头部CTA。（2）对照组：由放射科医师在Sham-AI整合的颅内动脉瘤诊断策略（Sham-AIarm）的协助下解释的头部CTA。
方法：区组随机化，按中心分层，性别,和年龄组。
方法：在颅内动脉瘤中，True-AI臂在患者水平的敏感性和特异性方面的非劣效性优于Sham-AI臂。
结果：其他颅内病变的诊断表现，检测率，CTA解释的工作量，资源利用率，治疗相关临床事件，动脉瘤相关事件,生活质量,和成本效益分析。
方法：研究参与者和参与的放射科医生将对干预措施视而不见。
方法：根据我们的试点研究，假定Sham-AI臂的患者水平灵敏度为0.65，True-AI臂的患者水平灵敏度为0.75，特异性分别为0.90和0.88。在医院接受头部CTA的患者颅内动脉瘤的患病率约为12％。使用单侧α=0.025来确定敏感性和特异性的非劣效性的优势，以5％的边缘，以确保联合主要终点测试的功效达到0.80和5％的流失率，在True-AI或Sham-AI组的1:1分配中,样本量确定为6450.
结论：该研究将确定AI系统对双盲设计中颅内动脉瘤检测性能的确切影响，并遵循对患者短期和长期结果的实际影响。
背景：该试验已在NIH注册，美国国家医学图书馆ClinicalTrials.gov，ID:NCT06118840。2023年11月11日注册。
方法：研究设计：前瞻性，多中心，双盲RCT。
方法：该模型设计用于从原始CTA图像自动检测颅内动脉瘤。
方法：安排头部CTA扫描的成人住院患者和门诊患者。随机分组：（1）实验组：放射科医师在True-AI整合颅内动脉瘤诊断策略（True-AIarm）的协助下解释头部CTA。（2）对照组：由放射科医师在Sham-AI整合的颅内动脉瘤诊断策略（Sham-AIarm）的协助下解释的头部CTA。
方法：区组随机化，按中心分层，性别,和年龄组。
方法：在颅内动脉瘤中，True-AI臂在患者水平的敏感性和特异性方面的非劣效性优于Sham-AI臂。
结果：其他颅内病变的诊断表现，检测率，CTA解释的工作量，资源利用率，治疗相关临床事件，动脉瘤相关事件,生活质量,和成本效益分析。
方法：研究参与者和参与的放射科医生将对干预措施视而不见。
方法：根据我们的试点研究，假定Sham-AI臂的患者水平灵敏度为0.65，True-AI臂的患者水平灵敏度为0.75，特异性分别为0.90和0.88。在医院接受头部CTA的患者颅内动脉瘤的患病率约为12％。使用单侧α=0.025来确定敏感性和特异性的非劣效性的优势，以5％的边缘，以确保联合主要终点测试的功效达到0.80和5％的流失率，在True-AI或Sham-AI组的1:1分配中,样本量确定为6450.
结论：该研究将确定AI系统对双盲设计中颅内动脉瘤检测性能的确切影响，并遵循对患者短期和长期结果的实际影响。
背景：该试验已在NIH注册，美国国家医学图书馆ClinicalTrials.gov，ID:NCT06118840。2023年11月11日注册。
