ArticleJournal of clinical medicine2023
External Validation and Retraining of DeepBleed: The First Open-Source 3D Deep Learning Network for the Segmentation of Spontaneous Intracerebral and Intraventricular Hemorrhage.
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 3 syntheses or guidelines pooled it, 10 citations in OpenAlex.
- AI Models for Surgical Decision Support in Spontaneous Intracerebral Hemorrhage: A Systematic Review in Relation to Trials and Guidelines.Neurocritical care · 2026Pooled it
- Deep learning-based automated segmentation of intracerebral haemorrhage, intraventricular haemorrhage and perihaematomal oedema on non-contrast CT.European stroke journal · 2026Pooled it
- Systematic review of commercial artificial intelligence tools for the detection and volume quantification in intracerebral hemorrhage.European radiology · 2026Pooled it
- Comparing manual and automated methods for extracting perihematomal features in predicting hemorrhage expansion in deep intracerebral hemorrhage.Scientific reports · 2026Article
- Adapting foundation models for rapid clinical response: intracerebral hemorrhage segmentation in emergency settings.Scientific reports · 2025Article
- Diagnostic Accuracy of Deep Learning for Intracranial Hemorrhage Detection in Non-Contrast Brain CT Scans: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2025Review
- Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography.Applied sciences (Basel, Switzerland) · 2025Article
- A Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve Confidence.Bioengineering (Basel, Switzerland) · 2024Article
- Do Deep Learning Algorithms Accurately Segment Intracerebral Hemorrhages on Noncontrast Computed Tomography? A Systematic Review and Meta-Analysis.Stroke (Hoboken, N.J.) · 2024Article
- New Advances in Diagnostic Radiology for Ischemic Stroke.Journal of clinical medicine · 2023Article
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe objective of this study was to assess the performance of the first publicly available automated 3D segmentation for spontaneous intracerebral hemorrhage (ICH) based on a 3D neural network before and after retraining.
methodsWe performed an independent validation of this model using a multicenter retrospective cohort. Performance metrics were evaluated using the dice score (DSC), sensitivity, and positive predictive values (PPV). We retrained the original model (OM) and assessed the performance via an external validation design. A multivariate linear regression model was used to identify independent variables associated with the model's performance. Agreements in volumetric measurements and segmentation were evaluated using Pearson's correlation coefficients (r) and intraclass correlation coefficients (ICC), respectively. With 1040 patients, the OM had a median DSC, sensitivity, and PPV of 0.84, 0.79, and 0.93, compared to thoseo f 0.83, 0.80, and 0.91 in the retrained model (RM). However, the median DSC for infratentorial ICH was relatively low and improved significantly after retraining, at
conclusionThe model demonstrated good generalization in an external validation cohort. Location-specific variances improved significantly after retraining. External validation and retraining are important steps to consider before applying deep learning models in new clinical settings.
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