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NCT Number: NCT07111975

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

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.

Active, Not Recruiting

This study is active but is not currently recruiting participants.

Key information

Conditions

Intracranial Aneurysms Aneurysm Brain Diseases Cardiovascular Diseases Central Nervous System Diseases Cerebrovascular Disorders Hemorrhage Intracranial Aneurysm Intracranial Arterial Diseases Intracranial Hemorrhages Nervous System Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms Rupture Subarachnoid Hemorrhage Subarachnoid Hemorrhage (SAH) From Ruptured Aneurysm Unruptured Intracranial Aneurysm Vascular Diseases Wounds and Injuries

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

University Medical Center (UMC) Utrecht

Utrecht, 3584 CX, Netherlands

About this study

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).

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

  1. Discriminative performance of AI model for aneurysm instability prediction

    Time frame: 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 outcomes

  1. Sensitivity and specificity of AI model

    Time frame: 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.

  2. Reduction in inter- and intra-observer variability

    Time frame: 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.

  3. Clinician adherence to AI suggestions

    Time frame: 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%.

  4. Reduction in clinical decision-making time

    Time frame: During vignette study; During final 6 months of study

    Time required for clinical decision-making with AI compared to without AI. Target reduction: ≥ 25%.

  5. User satisfaction with AI tool

    Time frame: 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.

Sponsors and collaborators

Lead sponsor

UMC Utrecht

Other

Collaborators

  • Bichat Hospital
  • Philips Medical Systems
  • Universitätsklinikum Hamburg-Eppendorf

Registry information

Important dates

Study start
2025
Primary completion
2028
Study completion
2028
First posted
Aug 8, 2025
Registry last updated
Aug 8, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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