Development and External Validation of a Deep Learning Model to Predict Mortality in Aneurysmal Subarachnoid Hemorrhage Using Admission Computed Tomography

Santiago Cepeda; Alexia Rizoudis; Dominik Müller; Frank Kramer; Olga Esteban-Sinovas; Ignacio Arrese; Rosario Sarabia; on behalf of the Mortality-SAH Collaborative Group

doi:10.1227/neu.0000000000004038

April 23, 2026Neurosurgery

Development and External Validation of a Deep Learning Model to Predict Mortality in Aneurysmal Subarachnoid Hemorrhage Using Admission Computed Tomography

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Key Points

This research aims to develop a deep learning model to predict 90-day mortality in patients with aneurysmal subarachnoid hemorrhage using admission computed tomography scans.
Development of a fully automated deep learning model based on admission non-contrast computed tomography (NCCT) scans.
Validation of the model's predictive accuracy against conventional clinical models.
Objective assessment without additional data collection beyond standard imaging.
The deep learning model demonstrates comparable discrimination and calibration for predicting mortality as established clinical models.
The model provides objective, reproducible risk stratification at the point of care.
It serves as a complementary decision-support tool in clinical settings.

Implication

Develops a model predicting 90-day mortality in aneurysmal subarachnoid hemorrhage, suggesting a new tool for risk assessment.

Abstract

A fully automated DL model based solely on admission NCCT predicts 90-day mortality after aneurysmal subarachnoid hemorrhage with discrimination and calibration comparable with clinical models requiring multiple variables. Rather than replacing conventional prognostication, this approach offers a complementary decision-support tool that requires no data collection beyond routine imaging, enabling objective, reproducible risk stratification at the point of care.

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Cite This Study

Cepeda et al. (2026) studied this question.

synapsesocial.com/papers/69ec5a4488ba6daa22dabd8d https://doi.org/10.1227/neu.0000000000004038

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Development and External Validation of a Deep Learning Model to Predict Mortality in Aneurysmal Subarachnoid Hemorrhage Using Admission Computed Tomography

Key Points

Implication

Abstract

Discussion

Cite This Study

Development and External Validation of a Deep Learning Model to Predict Mortality in Aneurysmal Subarachnoid Hemorrhage Using Admission Computed Tomography

Key Points

Implication

Abstract

Discussion

Cite This Study

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