NCT07124624

Stepped-Wedge Cluster Randomized Trial of AI-Assisted CTA Detection for Intracranial Aneurysms in Regional Hospitals

Impact of an AI-Driven CT Angiography Model on Intracranial Aneurysm Detection and Clinical Outcomes in Regional Hospitals (IDEAL2): A Nationwide Stepped-Wedge Cluster-Randomized Trial

• Status: Not Yet Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 14,400 (estimated)
• Sponsor: Jinling Hospital, China · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

Summary

This study (IDEAL 2) is a nationwide stepped-wedge cluster-randomized trial designed to prospectively enroll over 14,400 patients undergoing outpatient head CT angiography (CTA). The trial will be conducted across more than 72 regional hospitals in China. Clusters were randomly assigned to nine randomization groups. In accordance with the stepped-wedge design, clusters will sequentially transition from the control condition (standard human diagnosis) to the intervention condition (AI-assisted diagnosis) at regular intervals over a 10-month period, until all clusters receive the intervention. The primary outcome is the detection rate of intracranial aneurysms. Secondary outcomes include patient prognosis and clinical outcomes.

Detailed description

A multicenter, stepped-wedge cluster-randomized trial will be conducted in regional hospitals, specifically prefecture-level and county-level institutions across China. Each cluster (i.e., hospital) will enroll approximately 200 patients undergoing head computed tomography angiography (CTA), yielding a total sample size of at least 14,400 participants. The trial consists of nine steps, each lasting one month. Clusters will transition sequentially from the control condition to the intervention condition based on stratified randomization, until all clusters have received the intervention. In the control group, diagnoses and treatments will follow local standard clinical protocols. In the intervention group, diagnostic procedures will be supported by an artificial intelligence (AI)-assisted system. The primary outcome is the detection rate of intracranial aneurysms, as determined from radiology reports at the patient level. Secondary outcomes include additional diagnostic performance metrics on CTA, such as the detection of intracranial arterial stenosis, occlusion, and tumors. Follow-up evaluations at 3 and 12 months will assess treatment-related indicators-including repeat head CTA or magnetic resonance angiography (MRA), hospitalization rates, and digital subtraction angiography (DSA) utilization-as well as clinical outcomes related to aneurysm events. These measures aim to evaluate both the short- and long-term impacts of AI-assisted diagnosis on routine clinical practice and patient prognosis.

Conditions

• Intracranial Aneurysm
• CT Angiography
• AI (Artificial Intelligence)
• Cluster Randomized Trial

Interventions

Type	Name	Description
DEVICE	AI-Assisted CTA Interpretation	A locked, independently validated deep learning model was used to assist radiologists in interpreting head CTA scans. The model was trained on 16,546 CTA cases and externally validated on an independent set of 900 DSA-verified CTA cases, achieving a patient-level sensitivity of 0.943 and an average of 0.187 false positives per case.
DIAGNOSTIC_TEST	Standard CTA Interpretation	Head CTA interpretation performed by radiologists using local routine diagnostic workflows without AI support.

Timeline

• Start date: 2025-10-09
• Primary completion: 2026-08-31
• Completion: 2029-08-31
• First posted: 2025-08-15
• Last updated: 2025-08-20