IDEAL Study: Blinded RCT for the Impact of AI Model for Cerebral Aneurysms Detection on Patients' Diagnosis and Outcomes
Assessing the Impact of an Artificial Intelligence-Based Model for Intracranial Aneurysm Detection in CT Angiography on Patients' Diagnosis and Outcomes: The IDEAL Study - A Web-Based Multicenter, Double-Blinded Randomized Controlled Trial
Jinling Hospital, China
6,450 participants
May 20, 2024
INTERVENTIONAL
Conditions
Summary
This study (IEDAL study) intends to prospectively enroll more than 6450 patients who will undergo head CT angiography (CTA) scanning in the outpatient clinic. It will be carried out in 21 hospitals in more than 10 provinces in China. The patient's head CTA images will be randomly assigned to the True-AI and Sham-AI group with a ratio of 1:1, and the patients and radiologists are unaware of the allocation. The primary outcomes are sensitivity and specificity of detecting intracranial aneurysms. The secondary outcomes focus on the prognosis and outcomes of the patients.
Eligibility
Inclusion Criteria1
- Adult inpatients and outpatients who are scheduled for head CTA scanning.
Exclusion Criteria6
- Age under 18 years.
- Patients with contraindications to CTA.
- Modified Rankin Scale (mRS) score \> 3.
- Refuse to sign informed consent.
- Participation in other clinical studies of intracranial aneurysms.
- Patients with failed head CTA scanning or incomplete image data, or poor image quality.
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Interventions
The True-AI deep-learning based model for intracranial aneurysms detection had a patient-wise sensitivity, lesion-wise sensitivity and specificity of 0.96, 0.87, and 0.80 in the internal validation dataset.
The Sham-AI deep-learning based model for intracranial aneurysms detection is designed to have a sensitivity close to 0% and a similar specificity to the True-AI. In the internal validation dataset, the Sham-AI had a patient-wise sensitivity, lesion-wise sensitivity, specificity of 0.02, 0.01, and 0.80, respectively.
Locations(21)
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NCT06118840