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AI Models for Cerebral Aneurysms Segmentation, Detection and Stability Prediction

Artificial Intelligence Applications for Cerebral Aneurysms Segmentation, Detection and Stability Prediction: a Stepwise, Multicenter Study

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT06766422
Acronym
AI-CARE
Enrollment
10000
Registered
2025-01-09
Start date
2025-01-10
Completion date
2027-06-30
Last updated
2025-06-03

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Artificial Intelligence (AI), Magnetic Resonance Angiography, Subarachnoid Hemorrhage, Aneurysmal, Unruptured Cerebral Aneurysm

Keywords

Artificial Intelligence, Cerebral Aneurysms, Magnetic Resonance Angiography, Subarachnoid Hemorrhage

Brief summary

Aneurysmal subarachnoid hemorrhage (SAH) is one of the critical diseases that severely threaten human health, with a clinical mortality rate reaching as high as 30%. Early diagnosis and intervention before rupture are considered key to improving the prognosis of aneurysmal SAH. With the widespread clinical application of non-invasive cerebrovascular imaging techniques, such as CTA and MRA, the detection rate of unruptured intracranial aneurysms (UIAs) has significantly increased. However, addressing the growing demand for clinical cerebrovascular imaging diagnostics raises the challenge of improving diagnostic accuracy while alleviating the workload of diagnostic physicians. Furthermore, considering that not all detected UIAs will rupture, it is crucial to accurately identify high-risk aneurysms prone to rupture to avoid unnecessary overtreatment, which could lead to significant socioeconomic burdens and iatrogenic harm to patients.To meet this clinical need, researchers have developed an artificial intelligence (AI) algorithm to create software capable of automatically identifying intracranial aneurysms based on non-invasive vascular imaging data, enabling accurate diagnosis of aneurysms. To evaluate the clinical utility of this AI algorithm, a prospective, multicenter, registry study was proposed. Through long-term standardized and uniform non-invasive imaging follow-up, individualized imaging analysis profiles will be established. By correlating these profiles with aneurysm outcome events (growth or rupture), imaging features capable of accurately predicting aneurysm growth and rupture will be identified and analyzed. This approach is expected to enhance the accuracy of UIA diagnosis and enable risk stratification for unruptured intracranial aneurysms through the utilization of relevant data.

Interventions

None listed

Sponsors

Shanghai Jiao Tong University Affiliated Sixth People's Hospital
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

Inclusion criteria

1. Age ≥ 18 years; 2. Preliminary diagnosis or symptoms indicating the presence or potential presence of a cerebral aneurysm; 3. Undergoing a non-contrast head MRA or contrast-enhanced head/neck CTA; 4. The patient or their legal representative is able and willing to sign an informed consent form.

Exclusion criteria

1. Other intracranial vascular diseases: moyamoya disease, arteriovenous malformations, arteriovenous fistulas, arterial occlusions, and arterial dissections; 2. History of intracranial arterial interventions: stent placement, partial aneurysm coil treatment, etc.; 3. Severe allergy to contrast agents or absolute contraindications to iodine-based contrast agents; 4. Renal insufficiency with elevated serum creatinine (greater than twice the upper normal limit); 5. MRI contraindications: pacemakers, claustrophobia, etc.; 6. Diseases or conditions that affect the quality of CTA/MRA images; 7. Inability to complete the study due to psychiatric disorders, cognitive, or emotional disturbances. Note: The CTA sub-study does not include exclusion criterion 5; the MRA sub-study does not include

Design outcomes

Primary

MeasureTime frameDescription
Consistency between the artificial intelligence model and the manually annotated gold standard.1 monthConsistency between the artificial intelligence model and the manually annotated gold standard.Intracranial aneurysms appear as saccular (most common), fusiform, or irregular localized vascular dilations on MRA 3D TOF(Time-of-Flight).Aneurysms are classified into four main categories based on their diameter: small aneurysms, generally less than five millimeters; small aneurysms, with diameters ranging from 0.5 centimeters to 1.5 centimeters; large aneurysms, typically between 1.5 and 2.5 centimeters; and giant aneurysms, which exceed 2.5 centimeters.
Consistency between the artificial intelligence model and radiologists' image interpretations.1 monthConsistency between the artificial intelligence model and radiologists' image interpretations.Intracranial aneurysms appear as saccular (most common), fusiform, or irregular localized vascular dilations on MRA 3D TOF(Time-of-Flight).Aneurysms are classified into four main categories based on their diameter: small aneurysms, generally less than five millimeters; small aneurysms, with diameters ranging from 0.5 centimeters to 1.5 centimeters; large aneurysms, typically between 1.5 and 2.5 centimeters; and giant aneurysms, which exceed 2.5 centimeters.

Countries

China

Contacts

Primary ContactYueqi Zhu
zhuyueqi@hotmail.com+86-21-66301136

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026