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