Development and External Validation of a Deep Learning Model to Predict Mortality in Aneurysmal Subarachnoid...

Traditional prognostication after aneurysmal subarachnoid hemorrhage depends on subjective clinical grading and radiological scoring systems that exhibit inter-rater variability and require multiple variables, hampering clinical adoption. We aimed to develop and externally validate a fully automated deep learning (DL) model predicting 90-day mortality exclusively from admission noncontrast computed tomography (NCCT), requiring no manual input and providing objective, reproducible, image-only risk stratification.

This multicenter retrospective study included 9 tertiary hospitals for model development and 2 independent centers for external validation. A DL model was trained using 3-dimensional DenseNet-121 architecture with transfer learning, using admission NCCT scans as the sole input. Three comparator logistic regression models were constructed: Core (age, World Federation of Neurosurgical Societies grade), Imaging (adding modified Fisher grade, aneurysm size, and location), and Full Clinical (further including treatment modality). Performance metrics included discrimination, classification, calibration, and decision-curve analysis.

The study included 863 patients: 586 for training, 147 for internal testing, and 130 for external validation. In internal testing, area under the curves (95% CI) were: Core 0.856 (0.790-0.913), Imaging 0.853 (0.780-0.916), Full 0.844 (0.766-0.909), and DL 0.855 (0.786-0.917). In external validation, area under the curves were: Core 0.823 (0.738-0.895), Imaging 0.793 (0.705-0.871), Full 0.798 (0.707-0.873), and DL 0.806 (0.724-0.876). All models demonstrated good calibration. Decision-curve analysis showed comparable net benefit across clinically relevant thresholds, with no significant performance differences between the DL model and conventional approaches (DeLong P > .05).

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.