aceThe use of machine learning models for subdural hematoma detection: a single-arm meta-analysis
Manual evaluation of non-contrast CT scans (NCTS) for detecting subdural hematoma (SDH) is time consuming, potentially inaccurate, and subjective to the expert analyzing them. In recent years, two deep learning (DL) algorithms have been popularly studied in this respect, namely convolutional neural networks (CNN) and U-Net architectures, the latter being a specialized type of CNN. We performed the first meta-analysis comparing various DL models for SDH detection. MEDLINE, Cochrane, Scopus, and Embase databases w…
Pooled testing of deep learning models for subdural hematoma detection on non-contrast CT achieved high diagnostic performance, with U-Net models showing significantly higher sensitivity and precision than other architectures.
Comparison is imbalanced with only 4 pooled U-Net datasets versus 22 pooled CNN datasets, requiring future well-powered studies before definitive conclusions about superiority.
Evidence
- Peer-reviewedNeurosurgical Review2026-08-06
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Truvace Impact Record TRV-2026-0676, v1: “The use of machine learning models for subdural hematoma detection: a single-arm meta-analysis.” Truvace, 2026-08-07. /record/TRV-2026-0676 (accessed at citation time). sha256 eb6f3b55c223c4f8…
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