Intracranial AneurysmsNCT07111975
ANEURYSM@RISK: Automatic Intracranial Aneurysm Quantification and Feature Learning Modelling to Optimize Intracranial Aneurysm Rupture Prediction
An observational study of Intracranial Aneurysms and Unruptured Intracranial Aneurysm, run by UMC Utrecht at 1 site in Netherlands.
Trial 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). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Intracranial Aneurysms, Unruptured Intracranial Aneurysm, Subarachnoid Hemorrhage (SAH) From Ruptured Aneurysm
- How many people
- 3,800 participants
- Where
- 1 siteNetherlands
- Study dates
- 1 Jan 2025to Dec 2028
- Phase
- Observational
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adults with diagnosed unruptured intracranial aneurysms (UIAs) identified through MR angiography imaging, with follow-up records, from three European academic hospitals. All data are retrospective and anonymized.
Who may be able to join
- 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
Who cannot join
- Imaging of insufficient quality for segmentation or analysis
- Lack of follow-up data
Trial locations and contacts
1 site in 1 country.
Netherlands1 site
University Medical Center (UMC) Utrecht
Utrecht, 3584 CX
About this study
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.
Read the 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).
- Conditions
- Intracranial Aneurysms, Unruptured Intracranial Aneurysm, Subarachnoid Hemorrhage (SAH) From Ruptured Aneurysm
- Keywords
- Artificial Intelligence, Intracranial Aneurysm, Brain Aneurysm, Unruptured Intracranial Aneurysm, MR Angiography, 3D Segmentation, Rupture/Growth Risk Prediction, Subarachnoid Hemorrhage, Clinical Decision Support, SHERPA, UMC Utrecht, Morphology, Shape
- Medical subject headings
- Intracranial Aneurysm, Subarachnoid Hemorrhage, Rupture
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Stable Intracranial Aneurysm Group | Participants with unruptured intracranial aneurysms (UIAs) that remained stable over time, showing no morphological growth or rupture during follow-up. Data are sourced from UMC Utrecht, UKE Hamburg, and AP-HP Paris. | |
| Unstable Intracranial Aneurysm Group | Participants with intracranial aneurysms (IAs) that demonstrated instability over time, defined as morphological growth and/or rupture during follow-up. Data are sourced from UMC Utrecht, UKE Hamburg, and AP-HP Paris. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Discriminative performance of AI model for aneurysm instability prediction
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.
Measured: At time of model validation; Up to 36 months from study start
Secondary outcomes (5)
Sensitivity and specificity of AI model
Targets: Sensitivity ≥ 80% and specificity ≥ 50%, based on optimal cut-off derived from ROC analysis.
Measured: At time of model validation; Up to 36 months from study start
Reduction in inter- and intra-observer variability
Measured as reduction in variation in risk classification between and within clinicians. Target: ≥ 50% reduction.
Measured: During vignette study in clinical setting; During final 6 months of study
Clinician adherence to AI suggestions
Proportion of cases where interventional radiologists follow AI risk estimates in their decision-making. Target adherence: ≥ 80%.
Measured: During vignette study; During final 6 months of study
Reduction in clinical decision-making time
Time required for clinical decision-making with AI compared to without AI. Target reduction: ≥ 25%.
Measured: During vignette study; During final 6 months of study
User satisfaction with AI tool
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.
Measured: Immediately after using the AI tool in vignette study; During final 6 months of study
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Aug 2025
- Study start1 Jan 2025
- Primary completionJun 2028 (estimated)
- Study completionDec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Intracranial AneurysmsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 3,800 participants (estimated)
Sponsor and investigators
- Lead sponsor
- UMC Utrecht
- Sponsor type
- University, hospital or other
- Collaborators
- Bichat Hospital, Universitätsklinikum Hamburg-Eppendorf, Philips Medical Systems
- Responsible party
- Principal investigator, Phebe J Groenheide, MSc, UMC Utrecht
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07111975
- Sponsor's protocol ID
- 23U-0036_AWARE
- Status last verified
- Aug 2025
- First submitted
- 28 Jul 2025
- Last updated
- 8 Aug 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: De-identified participant-level data derived from imaging, including intracranial aneurysm morphological measurements and risk predictions, will be shared upon reasonable request from qualified investigators.