AI Models for Cerebral Aneurysms Segmentation, Detection and Stability Prediction
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Unruptured Cerebral Aneurysm, Artificial Intelligence (AI), Subarachnoid Hemorrhage, Aneurysmal, Magnetic Resonance Angiography. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
Artificial Intelligence Applications for Cerebral Aneurysms Segmentation, Detection and Stability Prediction: a Stepwise, Multicenter Study
Overview
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.
Primary outcome measures
- Consistency between the artificial intelligence model and the manually annotated gold standard. [Time frame: 1 month]
- Consistency between the artificial intelligence model and radiologists' image interpretations. [Time frame: 1 month]
Eligibility criteria
Inclusion criteria
- Age ≥ 18 years;
- Preliminary diagnosis or symptoms indicating the presence or potential presence of a cerebral aneurysm;
- Undergoing a non-contrast head MRA or contrast-enhanced head/neck CTA;
- The patient or their legal representative is able and willing to sign an informed consent form.
Exclusion criteria
- Other intracranial vascular diseases: moyamoya disease, arteriovenous malformations, arteriovenous fistulas, arterial occlusions, and arterial dissections;
- History of intracranial arterial interventions: stent placement, partial aneurysm coil treatment, etc.;
- Severe allergy to contrast agents or absolute contraindications to iodine-based contrast agents;
- Renal insufficiency with elevated serum creatinine (greater than twice the upper normal limit);
- MRI contraindications: pacemakers, claustrophobia, etc.;
- Diseases or conditions that affect the quality of CTA/MRA images;
- 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 exclusion criteria 3 and 4.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- Shanghai Sixth People's Hospital, Shanghai, 200023 — Shanghai
Identifiers
NCT: NCT06766422 · AI-CARE