ArticleNPJ digital medicine2025
Prediction of functional outcomes in aneurysmal subarachnoid hemorrhage using pre-/postoperative noncontrast CT within 3 days of admission.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Artificial Intelligence for Cerebral Aneurysm Management: Integrating Imaging, Hemodynamics, and Clinical Decision Support.Journal of imaging informatics in medicine · 2026Review
- Non-contrast computed tomography at the end of the acute care phase as an additional prognostic tool after aneurysmal subarachnoid hemorrhage.Neurosurgical review · 2026Article
- Machine learning versus conventional grading systems for prognostication in aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis.Neuroradiology · 2026Article
- A Causal and interpretable machine learning framework for postcranioplasty risk prediction and surgical decision support.NPJ digital medicine · 2026Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening condition, and accurate prediction of functional outcomes is critical for optimizing patient management within the initial 3 days of presentation. However, existing clinical scoring systems and imaging assessments do not fully capture clinical variability in predicting outcomes. We developed a deep learning model integrating pre- and postoperative noncontrast CT (NCCT) imaging with clinical data to predict 3-month modified Rankin Scale (mRS) scores in aSAH patients. Using data from 1850 patients across four hospitals, we constructed and validated five models: preoperative, postoperative, stacking imaging, clinical, and fusion models. The fusion model significantly outperformed the others (all p<0.001), achieving a mean absolute error of 0.79 and an area under the curve of 0.92 in the external test. These findings demonstrate that this integrated deep learning model enables accurate prediction of 3-month outcomes and may serve as a prognostic support tool early in aSAH care.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.