ArticleDiagnostics (Basel, Switzerland)2022
A Robust, Fully Automatic Detection Method and Calculation Technique of Midline Shift in Intracranial Hemorrhage and Its Clinical Application.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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8 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?Medicine · 2022Pooled it
- Review
- Efficient automated quantification of midline shift in intracerebral hemorrhage using a binarized deep learning model on non-contrast head CT.Neuroradiology · 2026Article
- Towards Robust Brain Midline Shift Detection: A YOLO-Based 3D Slicer Extension with a Novel Dataset.Neuroinformatics · 2025Article
- Brain Midline Approximation to Improve Symmetry Analysis of Brain CT Scans.Journal of imaging informatics in medicine · 2025Article
- Automated Midline Shift Detection in Head CT Using Localization and Symmetry Techniques Based on User-Selected Slice.AJNR. American journal of neuroradiology · 2025Article
- Development and validation of a keypoint region-based convolutional neural network to automate thoracic Cobb angle measurements using whole-spine standing radiographs.Acta neurochirurgica · 2025Article
- Three dimensional convolutional neural network-based automated detection of midline shift in traumatic brain injury cases from head computed tomography scans.Journal of neurosciences in rural practiceArticle
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
A midline shift (MLS) is an important clinical indicator for intracranial hemorrhage. In this study, we proposed a robust, fully automatic neural network-based model for the detection of MLS and compared it with MLSs drawn by clinicians; we also evaluated the clinical applications of the fully automatic model. We recruited 300 consecutive non-contrast CT scans consisting of 7269 slices in this study. Six different types of hemorrhage were included. The automatic detection of MLS was based on modified Keypoint R-CNN with keypoint detection followed by training on the ResNet-FPN-50 backbone. The results were further compared with manually drawn outcomes and manually defined keypoint calculations. Clinical parameters, including Glasgow coma scale (GCS), Glasgow outcome scale (GOS), and 30-day mortality, were also analyzed. The mean absolute error for the automatic detection of an MLS was 0.936 mm compared with the ground truth. The interclass correlation was 0.9899 between the automatic method and MLS drawn by different clinicians. There was high sensitivity and specificity in the detection of MLS at 2 mm (91.7%, 80%) and 5 mm (87.5%, 96.7%) and MLSs greater than 10 mm (85.7%, 97.7%). MLS showed a significant association with initial poor GCS and GCS on day 7 and was inversely correlated with poor 30-day GOS (p < 0.001). In conclusion, automatic detection and calculation of MLS can provide an accurate, robust method for MLS measurement that is clinically comparable to the manually drawn method.
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