Mortality-SAH is an AI-powered clinical tool designed to estimate the risk of mortality within the first 90 days following aneurysmal subarachnoid hemorrhage (aSAH). Built on state-of-the-art deep learning techniques, it relies solely on non-contrast computed tomography (CT) scans acquired at the time of admission—providing fast, fully automated prognostic insight when decisions are most critical.
This tool is the result of a collaborative project between GEIBAC and the University of Augsburg (Germany), combining clinical expertise with cutting-edge artificial intelligence research.
It is currently undergoing multicenter validation across several Spanish hospitals.
Key Features
Automated Data Processing
Converts DICOM images into NIfTI format for streamlined analysis.
User-Friendly Input Structure
Simply organize your DICOM images into patient-specific folders, and the system will handle preprocessing and inference.
Output Files
Processed NIfTI images & Prediction report (predictions.csv) summarizing risk probabilities
How it Works
Unlike traditional prognostic scores that require manual input and clinical variables, Mortality-SAH leverages convolutional neural networks trained on large datasets of de-identified CT scans. It provides a mortality probability score directly from imaging, eliminating user bias and reducing evaluation time.
Publications
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December 22, 2023 Brain Sciences
García-García S, Cepeda S, Müller D, et al.
Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan
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September 26, 2023 Brain and Spine
García-García S, Cepeda S, Müller D, et al.
Neurological Outcome Prediction in Patients with Subarachnoid Hemorrhage Using a Model Based on Initial CT Scan, Clinical Data and Neural Networks
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August 5, 2024 World Neurosurgery
García-García S, Cepeda S, Arrese I, and Sarabia R
A Fully Automated Pipeline Using Swin Transformers for Deep Learning-Based Blood Segmentation on Head Computed Tomography Scans After Aneurysmal Subarachnoid Hemorrhage
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June 20, 2023 Neurocirugía
García-García S, Cepeda S, Müller D, et al.
Mortality prediction in patients with subarachnoid hemorrhage using an artificial intelligence model based on initial CT scan and neural networks
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April 23, 2026 Neurosurgery
Cepeda S, Rizoudis A, Müller D, et al.
Development and external validation of a deep learning model to predict mortality in aneurysmal subarachnoid hemorrhage using admission CT
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March 12, 2026 Bildverarbeitung für die Medizin 2026
Rizoudis A, Cepeda S, Kramer F, and Müller D.
Comparative Analysis of Machine Learning Models for 3-month Survival Prediction in Aneurysmal Subarachnoid Hemorrhage
License
This software is distributed under the Creative Commons Attribution- NonCommercial (CC BY-NC) license. It is free to use, modify, and share for educational, personal, or non-profit purposes. Commercial use is strictly prohibited without prior written permission.