IDEAL Study: Blinded RCT for the Impact of AI Model for Cerebral Aneurysms Detection on Patients' Diagnosis and Outcomes
NCT ID: NCT06118840
Last Updated: 2025-10-07
Study Results
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Basic Information
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RECRUITING
NA
6450 participants
INTERVENTIONAL
2024-05-20
2026-12-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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RANDOMIZED
PARALLEL
DIAGNOSTIC
QUADRUPLE
A Sham-AI for the automatic detection of intracranial aneurysms has been delicately designed to mimic the True-AI model with a sensitivity close to zero and a similar specificity to True-AI. Thereforem, the participating radiologists interpreting head computered tomograhy angiography exams will also be blinded to the allocation of the AI models.
Study Groups
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True-AI-integrated intracranial aneurysms diagnosis strategy
For patients who underwent head CTA and assigned to True-AI group, they will be diagnosed by a radiologist who are aided by the True-AI-integrated intracranial aneurysms diagnosis strategy.
True-AI-integrated intracranial aneurysms diagnosis strategy
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.
Sham-AI-integrated intracranial aneurysms diagnosis strategy
For patients who underwent head CTA and assigned to Sham-AI group, they will be diagnosed by a radiologist who are aided by the Sham-AI-integrated intracranial aneurysms diagnosis strategy. To mimic the True-AI, the Sham-AI had a sensitivity close to 0% and a similar specificity to the True-AI.
Sham-AI-integrated intracranial aneurysms diagnosis strategy
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.
Interventions
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True-AI-integrated intracranial aneurysms diagnosis strategy
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.
Sham-AI-integrated intracranial aneurysms diagnosis strategy
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.
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
* 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.
18 Years
ALL
Yes
Sponsors
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Jinling Hospital, China
OTHER
Responsible Party
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Zhang longjiang,MD
Head of Radiology
Principal Investigators
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Trial Manager
Role: PRINCIPAL_INVESTIGATOR
Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University
Locations
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The First Affiliated Hospital of University of Science and Technology of China
Hefei, Anhui, China
The First Affiliated Hospital of Wannan Medical College
Wuhu, Anhui, China
Guizhou Provincial People's Hospital
Guiyang, Guizhou, China
First Affiliated Hospital of Zhengzhou University
Zhengzhou, Henan, China
Shiyan People's Hospital
Shiyan, Hubei, China
Research Institute Of Medical Imaging Jinling Hospital
Nanjing, Jiangsu, China
Affiliated Hospital of Xuzhou Medical University
Xuzhou, Jiangsu, China
The First Hospital of Jilin University
Changchun, Jilin, China
Shandong Provincial Hospital Affiliated to Shandong First Medical University
Jinan, Shandong, China
Yidu Central Hospital Affiliated to Shandong Second Medical University
Weifang, Shandong, China
The First Affiliated Hospital of Kunming Medical University
Kunming, Yunnan, China
Hainan General Hospital
Haikou, , China
The First People's Hospital of Kashgar Region
Kashgar, , China
University Second Hospital
Lanzhou, , China
First People's Hospital of Lianyungang
Lianyungang, , China
Ma'anshan People's Hospital
Ma’anshan, , China
BenQ Medical Center, Affiliated BenQ Hospital of Medical School, Nanjing Medical University
Nanjing, , China
Long Gang Central Hospital of Shenzhen
Shenzhen, , China
The Affiliated Suqian First Hospital of Nanjing Medical University
Suqian, , China
Tianjin Medical University General Hospital
Tianjin, , China
General Hospital of Ningxia Medical University
Yinchuan, , China
Countries
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Central Contacts
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Facility Contacts
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References
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Shi Z, Hu B, Lu M, Chen Z, Zhang M, Yu Y, Zhou C, Zhong J, Wu B, Zhang X, Wei Y, Zhang LJ; China Aneurysm AI Project Group. Assessing the Impact of an Artificial Intelligence-Based Model for Intracranial Aneurysm Detection in CT Angiography on Patient Diagnosis and Outcomes (IDEAL Study)-a protocol for a multicenter, double-blinded randomized controlled trial. Trials. 2024 Jun 4;25(1):358. doi: 10.1186/s13063-024-08184-9.
Other Identifiers
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2022NZKY-015-05
Identifier Type: -
Identifier Source: org_study_id
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