SynthesisLa Radiologia medica2025
Application of Deep Learning for Predicting Hematoma Expansion in Intracerebral Hemorrhage Using Computed Tomography Scans: A Systematic Review and Meta-Analysis of Diagnostic Accuracy.
Synthesis in La Radiologia medica, 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.
- Why Radiomics Rarely Reaches the Clinic: Reproducibility, Validation, and Evidence Gap-A Critical Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Assessing performance, calibration, and explainability of machine learning versus traditional models for early outcome prediction after spontaneous intracerebral hemorrhage: a systematic review and meta-analysis protocol.Systematic reviews · 2026Article
- Artificial intelligence in neurovascular surgery: advancing diagnosis, treatment, and outcomes.Frontiers in surgery · 2026Review
- A decision tree model for hematoma expansion prediction in women after spontaneous intracerebral hemorrhage.Frontiers in neurology · 2026Article
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Authors and funding
8 authors.
Funding
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Abstract
purposeWe aimed to systematically review the studies that utilized deep learning (DL)-based networks to predict hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH) using computed tomography (CT) images.
methodsWe carried out a comprehensive literature search across four major databases to identify relevant studies. To evaluate the quality of the included studies, we used both the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and the METhodological RadiomICs Score (METRICS) checklists. We then calculated pooled diagnostic estimates and assessed heterogeneity using the I
resultsTwenty-two studies were included in the qualitative synthesis, of which 11 and 6 were utilized for exclusive DL and combined DL meta-analyses, respectively. We found pooled sensitivity of 0.81 and 0.84, specificity of 0.79 and 0.91, positive diagnostic likelihood ratio (DLR) of 3.96 and 9.40, negative DLR of 0.23 and 0.18, diagnostic odds ratio of 16.97 and 53.51, and area under the curve of 0.87 and 0.89 for exclusive DL-based and combined DL-based models, respectively. Subgroup analysis revealed significant inter-group differences according to the segmentation technique and study quality.
conclusionDL-based networks showed strong potential in accurately identifying HE in ICH patients. These models may guide earlier targeted interventions such as intensive blood pressure control or administration of hemostatic drugs, potentially leading to improved patient outcomes.
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