ArticleNPJ digital medicine2024
Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06602115 (Noncontrast CT-Based Deep Learning for Predicting Hematoma Expansion Risk in Patients with Spontaneous Intracerebral Hemorrhage), which is not on this map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Noncontrast CT-Based Deep Learning for Predicting Hematoma Expansion Risk in Patients with Spontaneous Intracerebral Hemorrhage
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Application of Deep Learning for Predicting Hematoma Expansion in Intracerebral Hemorrhage Using Computed Tomography Scans: A Systematic Review and Meta-Analysis of Diagnostic Accuracy.La Radiologia medica · 2025Pooled it
- A multimodal prediction model for brain hematoma expansion based on deep neural network with transformer-driven collaborative attention fusion.Medical & biological engineering & computing · 2026Article
- Review
- Proactive concussion prediction using symmetry-aware multiscale explainable hybrid deep learning and multimodal data fusion.Scientific reports · 2026Article
- Prediction of intracerebral hemorrhage hematoma expansion: value of a novel deep learning system score.European radiology · 2026Article
- Deep learning-based imaging model to predict early hematoma enlargement and hospital mortality in spontaneous intracerebral hemorrhage.World journal of radiology · 2026Article
- Hematoma expansion in intracerebral hemorrhage: neuroimaging biomarkers, radiomics-based prediction models, and clinical translation.Frontiers in human neuroscience · 2026Review
- Minimally Invasive Evacuation for Spontaneous Supratentorial Intracerebral Hemorrhage After Contemporary Randomized Trials: Patient Selection, Timing, and Platform Choice.Therapeutics and clinical risk management · 2026Review
- 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
- Quantitative Shape Irregularity and Density Heterogeneity Predict Hematoma Expansion in Patients With Intracerebral Hemorrhage.Annals of clinical and translational neurology · 2025Article
- Multimodal AI-driven Biomarker for Early Detection of Cancer Cachexia.medRxiv : the preprint server for health sciences · 2025Article
- Noncontrast CT-based deep learning for predicting intracerebral hemorrhage expansion incorporating growth of intraventricular hemorrhage.Scientific reports · 2025Article
- Explainable CT-based deep learning model for predicting hematoma expansion including intraventricular hemorrhage growth.iScience · 2025Article
- Improving the Robustness of Deep Learning Models in Predicting Hematoma Expansion from Admission Head CT.AJNR. American journal of neuroradiology · 2025Article
- Article
- Explainable deep learning algorithm for identifying cerebral venous sinus thrombosis-related hemorrhage (CVST-ICH) from spontaneous intracerebral hemorrhage using computed tomography.EClinicalMedicine · 2025Article
- Enhancing AI reliability: A foundation model with uncertainty estimation for optical coherence tomography-based retinal disease diagnosis.Cell reports. Medicine · 2025Article
- Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography.Applied sciences (Basel, Switzerland) · 2025Article
- Review
Corrections and comments
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Authors and funding
15 authors at 4 institutions in 3 countries.
Funding
Abstract
Hematoma expansion (HE) is a modifiable risk factor and a potential treatment target in patients with intracerebral hemorrhage (ICH). We aimed to train and validate deep-learning models for high-confidence prediction of supratentorial ICH expansion, based on admission non-contrast head Computed Tomography (CT). Applying Monte Carlo dropout and entropy of deep-learning model predictions, we estimated the model uncertainty and identified patients at high risk of HE with high confidence. Using the receiver operating characteristics area under the curve (AUC), we compared the deep-learning model prediction performance with multivariable models based on visual markers of HE determined by expert reviewers. We randomly split a multicentric dataset of patients (4-to-1) into training/cross-validation (n = 634) versus test (n = 159) cohorts. We trained and tested separate models for prediction of ≥6 mL and ≥3 mL ICH expansion. The deep-learning models achieved an AUC = 0.81 for high-confidence prediction of HE
Identifiers
What OpenQuestion holds
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.