ArticleScientific reports2024
Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using a multimodal neural network.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The performance of artificial intelligence in image-based prediction of hematoma enlargement: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- 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
- Multimodal model predicts screw loosening at user-specified postoperative time points after single-level lumbar interbody fusion.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Current evidence and future directions for prophylactic anticoagulation therapy after severe spontaneous intracerebral hemorrhage: a narrative review.Journal of intensive medicine · 2026Review
- Risk factors and development of a prediction model for hematoma expansion in elderly patients with spontaneous intracerebral hemorrhage.Frontiers in aging neuroscience · 2026Article
- RELICT-NI: Replica Detection in Synthetic Neuroimaging-A Study on Noncontrast CT and Time-of-Flight MRA.Neuroinformatics · 2025Article
- Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography.Applied sciences (Basel, Switzerland) · 2025Article
- Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrounding oedema.Frontiers in neurology · 2025Article
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12 authors.
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Abstract
Hematoma expansion occasionally occurs in patients with intracerebral hemorrhage (ICH), associating with poor outcome. Multimodal neural networks incorporating convolutional neural network (CNN) analysis of images and neural network analysis of tabular data are known to show promising results in prediction and classification tasks. We aimed to develop a reliable multimodal neural network model that comprehensively analyzes CT images and clinical variables to predict hematoma expansion. We retrospectively enrolled ICH patients at four hospitals between 2017 and 2021, assigning patients from three hospitals to the training and validation dataset and patients from one hospital to the test dataset. Admission CT images and clinical variables were collected. CT findings were evaluated by experts. Three types of models were developed and trained: (1) a CNN model analyzing CT images, (2) a multimodal CNN model analyzing CT images and clinical variables, and (3) a non-CNN model analyzing CT findings and clinical variables with machine learning. The models were evaluated on the test dataset, focusing first on sensitivity and second on area under the receiver operating curve (AUC). Two hundred seventy-three patients (median age, 71 years [59-79]; 159 men) in the training and validation dataset and 106 patients (median age, 70 years [62-82]; 63 men) in the test dataset were included. Sensitivity and AUC of a CNN model were 1.000 (95% confidence interval [CI] 0.768-1.000) and 0.755 (95% CI 0.704-0.807); those of a multimodal CNN model were 1.000 (95% CI 0.768-1.000) and 0.799 (95% CI 0.749-0.849); and those of a non-CNN model were 0.857 (95% CI 0.572-0.982) and 0.733 (95% CI 0.625-0.840). We developed a multimodal neural network model incorporating CNN analysis of CT images and neural network analysis of clinical variables to predict hematoma expansion in ICH. The model was externally validated and showed the best performance of all the models.
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