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

NCT07111975 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 3800

Last updated 2025-08-08

No results posted yet for this study

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.

Conditions

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

Sponsors & Collaborators

  • AP-HP (Assistance Publique - Hôpitaux de Paris), FRANCE : Hôpital Pitié Salpêtrière, Hôpital Bichat

    collaborator UNKNOWN

  • University Medical Center Hamburg-Eppendorf (UKE)

    collaborator UNKNOWN

  • Philips Medical Systems

    collaborator INDUSTRY

  • UMC Utrecht

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-01-01
Primary Completion
2028-06-30
Completion
2028-12-31

Countries

  • Netherlands

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07111975 on ClinicalTrials.gov