ArticleScientific reports2023
Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
- Radiomic Features Extracted from Magnetic Resonance Imaging to Identify Arteriopathy in a Humanized Mouse Model of Sickle Cell Anemia and Sickle Cell Trait.Annals of biomedical engineering · 2026Article
- Radiomics-Based AI Model to Assist Clinicians in Intracranial Hemorrhage Diagnosis: External Validation Study.JMIR formative research · 2025Article
- A deep learning based radiomics model for differentiating intraparenchymal hematoma induced by cerebral venous thrombosis.Scientific reports · 2025Article
- Machine learning based classification of spontaneous intracranial hemorrhages using radiomics features.Neuroradiology · 2025Article
- Improved differentiation of cavernous malformation and acute intraparenchymal hemorrhage on CT using an AI algorithm.Scientific reports · 2024Article
- The clinical potential of radiomics to predict hematoma expansion in spontaneous intracerebral hemorrhage: a narrative review.Frontiers in neurology · 2024Review
- Hemorrhage Evaluation and Detector System for Underserved Populations: HEADS-UP.Mayo Clinic proceedings. Digital health · 2023Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is challenging to distinguish between primary and secondary intracranial hemorrhage (ICH) purely by imaging data, and the two forms of ICHs are treated differently. This study aims to evaluate the potential of CT-based machine learning to identify the etiology of ICHs and compare the effectiveness of two regions of interest (ROI) sketching methods. A total of 1702 radiomic features were extracted from the CT brain images of 238 patients with acute ICH. We used the Select K Best method, least absolute shrinkage, and selection operator logistic regression to select the most discriminable features with a support vector machine to build a classifier model. Then, a ten-fold cross-validation strategy was employed to evaluate the performance of the classifier. From all quantitative CT-based imaging features obtained by two sketch methods, eighteen features were selected respectively. The radiomics model outperformed radiologists in distinguishing between primary and secondary ICH in both the volume of interest and the three-layer ROI sketches. As a result, a machine learning-based CT radiomics model can improve the accuracy of identifying primary and secondary ICH. A three-layer ROI sketch can identify primary versus secondary ICH based on the CT radiomics method.
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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 Socratic graph.