Clinical trial NCT06766422: AI Models for Cerebral Aneurysms Segmentation, Detection and Stability Prediction
Official title: Artificial Intelligence Applications for Cerebral Aneurysms Segmentation, Detection and Stability Prediction: a Stepwise, Multicenter Study
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Unruptured Cerebral Aneurysm
Artificial Intelligence (AI)
Subarachnoid Hemorrhage, Aneurysmal
Magnetic Resonance Angiography
Keywords: Artificial Intelligence
Cerebral Aneurysms
Magnetic Resonance Angiography
Subarachnoid Hemorrhage
Lead sponsor: Shanghai Jiao Tong University Affiliated Sixth People's Hospital
Sponsor class: OTHER
First posted: 2025-01-09
Enrollment: 10000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: There is a preliminary diagnosis or symptoms indicating the presence or potential of a cerebral aneurysm.

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.


Eligibility criteria: 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 exclusion criteria 3 and 4.

Primary outcomes:
measure: Consistency between the artificial intelligence model and the manually annotated gold standard.
timeFrame: 1 month
measure: Consistency between the artificial intelligence model and radiologists' image interpretations.
timeFrame: 1 month
Secondary outcomes:
Other outcomes:
Locations:
city: Shanghai
country: China
facility: Shanghai Sixth People's Hospital, Shanghai, 200023
Study officials:
References: