Research map: The use of machine learning models for subdural hematoma detection: a single-arm meta-analysis

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  17. An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets · Hyunkwang Lee · 2018 · 455 citations · Cited by this paper
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  19. Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network · Hai Ye · 2019 · 252 citations · Cited by this paper
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  21. A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans · Xiyue Wang · 2021 · 196 citations · Cited by this paper
  22. Chronic Subdural Hematoma (cSDH): A review of the current state of the art · Aria Nouri · 2021 · 179 citations · Cited by this paper
  23. Utilization of Artificial Intelligence–based Intracranial Hemorrhage Detection on Emergent Noncontrast CT Images in Clinical Workflow · Muhannad Seyam · 2022 · 113 citations · Cited by this paper
  24. Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation · Arman Avesta · 2023 · 110 citations · Cited by this paper
  25. Increases in Subdural Hematoma with an Aging Population—the Future of American Cerebrovascular Disease · Sean N. Neifert · 2020 · 90 citations · Cited by this paper
  26. A real-world demonstration of machine learning generalizability in the detection of intracranial hemorrhage on head computerized tomography · Hojjat Salehinejad · 2021 · 65 citations · Cited by this paper
  27. Transfer Learning of the ResNet-18 and DenseNet-121 Model Used to Diagnose Intracranial Hemorrhage in CT Scanning · Qi Zhou · 2021 · 64 citations · Cited by this paper
  28. CT and MR imaging of chronic subdural hematomas: a comparative study · Şenol Şentürk · 2010 · 39 citations · Cited by this paper
  29. Automated Segmentation and Severity Analysis of Subdural Hematoma for Patients with Traumatic Brain Injuries · Negar Farzaneh · 2020 · 39 citations · Cited by this paper
  30. Artificial Intelligence with Statistical Confidence Scores for Detection of Acute or Subacute Hemorrhage on Noncontrast CT Head Scans · Eli Gibson · 2022 · 29 citations · Cited by this paper
  31. Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study · Masoud Maghami · 2023 · 28 citations · Cited by this paper
  32. The occurrence of acute subdural haematoma and diffuse axonal injury as two typical acceleration injuries · Natasha Davceva · 2012 · 27 citations · Cited by this paper
  33. Accuracy and time efficiency of a novel deep learning algorithm for Intracranial Hemorrhage detection in CT Scans · Tommaso D’Angelo · 2024 · 20 citations · Cited by this paper
  34. Motor Vehicle Crash-Related Subdural Hematoma from Real-World Head Impact Data · Jillian E. Urban · 2012 · 19 citations · Cited by this paper
  35. Differential CT features of acute lentiform subdural hematoma and epidural hematoma · I‐Chang Su · 2010 · 19 citations · Cited by this paper
  36. Strengthening deep-learning models for intracranial hemorrhage detection: strongly annotated computed tomography images and model ensembles · Dong‐Wan Kang · 2023 · 15 citations · Cited by this paper
  37. Towards Reliable Healthcare Imaging: A Multifaceted Approach in Class Imbalance Handling for Medical Image Segmentation · Lijuan Cui · 2025 · 11 citations · Cited by this paper
  38. Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis · Saeed Abdollahifard · 2022 · 10 citations · Cited by this paper
  39. Impact of Dataset Size on 3D CNN Performance in Intracranial Hemorrhage Classification · Chun‐Chao Huang · 2025 · 10 citations · Cited by this paper

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