ANEURYSM@RISK: Automatic Intracranial Aneurysm Quantification and Feature Learning Modelling to Optimize Intracranial Aneurysm Rupture Prediction

Active, not recruiting

• Conditions: Intracranial Aneurysms; Unruptured Intracranial Aneurysm; Subarachnoid Hemorrhage (SAH) From Ruptured Aneurysm

• Registration Number: NCT07111975
• Lead Sponsor: UMC Utrecht

Brief Summary

ANEURYSM@RISK is an observational study aiming to develop and validate an artificial intelligence (AI)-based prediction model for the growth and rupture of intracranial aneurysms (IAs). By applying automated 3D segmentation and morphological quantification of IAs from MR angiography (MRA) scans, the model is intended to provide clinicians with objective and reproducible risk estimates of aneurysm instability.

The study utilizes retrospective imaging data from multiple European centers, including UMC Utrecht, AP-HP Paris, and University Medical Center Hamburg-Eppendorf (UKE). A clinical vignette study will evaluate the model's clinical utility and user experience among interventional radiologists.

This study is exempt from medical ethics review (non-WMO in the Netherlands), as it involves only existing, anonymized data and imposes no additional burden on patients.

Detailed Description

The ANEURYSM@RISK study is part of the SHERPA project (Smart Human-centred Effortless support for Professional clinical Applications). The study aims to develop and validate a multivariable artificial intelligence (AI)-based prediction model to identify unstable unruptured intracranial aneurysms (UIAs), using morphological and clinical features.

Retrospective MR angiography (MRA) data will be collected from three clinical sites: UMC Utrecht (The Netherlands), AP-HP Paris (France), and University Medical Center Hamburg-Eppendorf (Germany). The study workflow includes:

* Development of AI algorithms for 3D shape feature extraction after automated aneurysm segmentation

* Training of predictive models for aneurysm growth and rupture based on morphological and clinical parameters

* Validation of model performance using a longitudinal dataset of \~1,000 patients (target C-statistic ≥ 0.80)

* A clinical vignette study in real-life settings to evaluate usability, decision-making impact, and inter-clinician variability

Key Performance Indicators (KPIs):

* Discriminative performance for aneurysm instability prediction; C-statistic ≥ 0.80

* Sensitivity ≥ 80% and specificity ≥ 50% (based on optimal cut-off values)

* ≥ 25% reduction in time to clinical decision-making

* ≥ 80% adherence to AI-generated suggestions by interventional radiologists

* ≥ 20% improvement in user experience using 3D visualization compared to 2D displays (survey-based)

* ≥ 50% reduction in inter- and intra-observer variability in aneurysm assessment

Ethical Considerations:

This is a non-interventional, retrospective study using previously acquired and anonymized imaging data. No additional procedures or data collection will be performed. The study poses no added burden or risk to patients. According to Dutch regulations, it is not subject to the Medical Research Involving Human Subjects Act (non-WMO).

Study Timeline

• Study Start: 2025-01-01
• Study First Submitted: 2025-07-28
• Status Verified: 2025-08
• Study First Submitted QC: 2025-08-05
• Last Update Submitted: 2025-08-05
• Study First Posted: 2025-08-08
• Last Update Posted: 2025-08-08
• Primary Completion: 2028-06
• Study Completion: 2028-12-01

Recruitment & Eligibility

• Status: ACTIVE_NOT_RECRUITING
• Sex: All
• Target Recruitment: 3800

Inclusion Criteria

• Available MR angiography (MRA) scans of the Circle of Willis
• Presence of at least one intracranial aneurysm (IA)
• Availability of follow-up imaging or clinical records indicating stability, growth, or rupture

Exclusion Criteria

• Imaging of insufficient quality for segmentation or analysis
• Lack of follow-up data

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Discriminative performance of AI model for aneurysm instability prediction	At time of model validation; Up to 36 months from study start	Model performance measured by C-statistic (AUC) with a target threshold of ≥ 0.80. Instability is defined as aneurysm growth and/or rupture during follow-up.

Secondary Outcome Measures

Name	Time	Method
Sensitivity and specificity of AI model	At time of model validation; Up to 36 months from study start	Targets: Sensitivity ≥ 80% and specificity ≥ 50%, based on optimal cut-off derived from ROC analysis.
Reduction in inter- and intra-observer variability	During vignette study in clinical setting; During final 6 months of study	Measured as reduction in variation in risk classification between and within clinicians. Target: ≥ 50% reduction.
User satisfaction with AI tool	Immediately after using the AI tool in vignette study; During final 6 months of study	Self-reported satisfaction measured using a 5-point Likert scale (1 = very dissatisfied, 5 = very satisfied). Higher scores indicate greater satisfaction. Target: average score ≥ 4.
Clinician adherence to AI suggestions	During vignette study; During final 6 months of study	Proportion of cases where interventional radiologists follow AI risk estimates in their decision-making. Target adherence: ≥ 80%.
Reduction in clinical decision-making time	During vignette study; During final 6 months of study	Time required for clinical decision-making with AI compared to without AI. Target reduction: ≥ 25%.

Trial Locations

Locations (1)

University Medical Center (UMC) Utrecht; 🇳🇱 Utrecht, Netherlands