RecruitingNot ApplicableNCT06118840

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


Sponsor

Jinling Hospital, China

Enrollment

6,450 participants

Start Date

May 20, 2024

Study Type

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

Min Age: 18 Years

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

DEVICETrue-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.

DEVICESham-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.


Locations(21)

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

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