ArticleScientific reports2022
Machine learning prediction of hematoma expansion in acute intracerebral hemorrhage.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 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
- Machine learning for predicting hematoma expansion in spontaneous intracerebral hemorrhage: a systematic review and meta-analysis.Neuroradiology · 2024Pooled 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
- Artificial Intelligence in Intracerebral Hemorrhage: Current Applications and Future Perspectives.Journal of clinical medicine · 2026Review
- Clinically Applicable Machine Learning Approach to Predict Intracerebral Hematoma Expansion.Journal of the American Heart Association · 2026Article
- Establishment of a machine learning prediction model for Wallerian degeneration after ischemic stroke.Frontiers in medicine · 2026Article
- Hematoma expansion in intracerebral hemorrhage: neuroimaging biomarkers, radiomics-based prediction models, and clinical translation.Frontiers in human neuroscience · 2026Review
- Risk factors and development of a prediction model for hematoma expansion in elderly patients with spontaneous intracerebral hemorrhage.Frontiers in aging neuroscience · 2026Article
- Article
- Predictive modeling of hematoma expansion from non-contrast computed tomography in spontaneous intracerebral hemorrhage patients.medRxiv : the preprint server for health sciences · 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
- Prediction of prognosis in patients with cerebral contusions based on machine learning.Scientific reports · 2024Article
- Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using a multimodal neural network.Scientific reports · 2024Article
- Hematoma expansion prediction based on SMOTE and XGBoost algorithm.BMC medical informatics and decision making · 2024Article
- Predicting hematoma expansion in acute spontaneous intracerebral hemorrhage: integrating clinical factors with a multitask deep learning model for non-contrast head CT.Neuroradiology · 2024Article
- Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan.NPJ digital medicine · 2024Article
- Contrast-enhanced CT radiomics combined with multiple machine learning algorithms for preoperative identification of lymph node metastasis in pancreatic ductal adenocarcinoma.Frontiers in oncology · 2024Article
- Review
- Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease.Frontiers in human neuroscience · 2023Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To examine whether machine learning (ML) approach can be used to predict hematoma expansion in acute intracerebral hemorrhage (ICH) with accuracy and widespread applicability, we applied ML algorithms to multicenter clinical data and CT findings on admission. Patients with acute ICH from three hospitals (n = 351) and those from another hospital (n = 71) were retrospectively assigned to the development and validation cohorts, respectively. To develop ML predictive models, the k-nearest neighbors (k-NN) algorithm, logistic regression, support vector machines (SVMs), random forests, and XGBoost were applied to the patient data in the development cohort. The models were evaluated for their performance on the patient data in the validation cohort, which was compared with previous scoring methods, the BAT, BRAIN, and 9-point scores. The k-NN algorithm achieved the highest area under the receiver operating characteristic curve (AUC) of 0.790 among all ML models, and the sensitivity, specificity, and accuracy were 0.846, 0.733, and 0.775, respectively. The BRAIN score achieved the highest AUC of 0.676 among all previous scoring methods, which was lower than the k-NN algorithm (p = 0.016). We developed and validated ML predictive models of hematoma expansion in acute ICH. The models demonstrated good predictive ability, showing better performance than the previous scoring methods.
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