ArticleiScience2025
Explainable CT-based deep learning model for predicting hematoma expansion including intraventricular hemorrhage growth.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Artificial intelligence in neurovascular surgery: advancing diagnosis, treatment, and outcomes.Frontiers in surgery · 2026Review
- Explainable Artificial Intelligence in Non-Contrast Brain Computed Tomography Scan for Intracerebral Hemorrhage: A Scoping Review.Archives of academic emergency medicine · 2026Article
- An end-to-end deep learning pipeline for hematoma expansion prediction in spontaneous intracerebral hemorrhage based on non-contrast computed tomography.NPJ digital medicine · 2025Article
- Noncontrast CT-based deep learning for predicting intracerebral hemorrhage expansion incorporating growth of intraventricular hemorrhage.Scientific reports · 2025Article
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Authors and funding
12 authors.
Funding
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Abstract
Hematoma expansion (HE), including intraventricular hemorrhage (IVH) growth, significantly affects outcomes in patients with intracerebral hemorrhage (ICH). This study aimed to develop, validate, and interpret a deep learning model, HENet, for predicting three definitions of HE. Using CT scans and clinical data from 718 ICH patients across three hospitals, the multicenter retrospective study focused on revised hematoma expansion (RHE) definitions 1 and 2, and conventional HE (CHE). HENet's performance was compared with 2D models and physician predictions using two external validation sets. Results showed that HENet achieved high AUC values for RHE1, RHE2, and CHE predictions, surpassing physicians' predictions and 2D models in net reclassification index and integrated discrimination index for RHE1 and RHE2 outcomes. The Grad-CAM technique provided visual insights into the model's decision-making process. These findings suggest that integrating HENet into clinical practice could improve prediction accuracy and patient outcomes in ICH cases.
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