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Diagnostic Performance of Neural Network Algorithms in Skull Fracture Detection on Ct Scans: A Systematic Review and Meta-Analysis Publisher Pubmed



Sharifi G1 ; Hajibeygi R2 ; Zamani SAM3 ; Easa AM4 ; Bahrami A5 ; Eshraghi R5 ; Moafi M10 ; Ebrahimi MJ10 ; Fathi M1 ; Mirjafari A6, 7 ; Chan JS11 ; Dixe De Oliveira Santo I8 ; Anar MA9 ; Rezaei O1 Show All Authors
Authors
  1. Sharifi G1
  2. Hajibeygi R2
  3. Zamani SAM3
  4. Easa AM4
  5. Bahrami A5
  6. Eshraghi R5
  7. Moafi M10
  8. Ebrahimi MJ10
  9. Fathi M1
  10. Mirjafari A6, 7
  11. Chan JS11
  12. Dixe De Oliveira Santo I8
  13. Anar MA9
  14. Rezaei O1
  15. Tu LH8
Show Affiliations
Authors Affiliations
  1. 1. Skull base Research Center, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  2. 2. Tehran University of Medical Sciences, School of Medicine, Tehran, Iran
  3. 3. Department of Neurosurgery, Iranian Hospital, Dubai, United Arab Emirates
  4. 4. Department of Radiology Technology, Collage of Health and Medical Technology, Al-Ayen Iraqi University, Thi-Qar, 64001, Iraq
  5. 5. Kashan University of Medical Sciences, Kashan, Iran
  6. 6. Department of Radiological Sciences, University of California, Los Angeles, CA, United States
  7. 7. College of Osteopathic Medicine of The Pacific, Western University of Health Sciences, Pomona, CA, United States
  8. 8. Department of Radiology and Biomedical Imaging, Yale School of Medicine, CT, United States
  9. 9. College of Medicine, University of Arizona, Tucson, AZ, United States
  10. 10. Cell Biology and Anatomical Sciences, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  11. 11. Keck School of Medicine of USC, Los Angeles, CA, United States

Source: Emergency Radiology Published:2025


Abstract

Background and aim: The potential intricacy of skull fractures as well as the complexity of underlying anatomy poses diagnostic hurdles for radiologists evaluating computed tomography (CT) scans. The necessity for automated diagnostic tools has been brought to light by the shortage of radiologists and the growing demand for rapid and accurate fracture diagnosis. Convolutional Neural Networks (CNNs) are a potential new class of medical imaging technologies that use deep learning (DL) to improve diagnosis accuracy. The objective of this systematic review and meta-analysis is to assess how well CNN models diagnose skull fractures on CT images. Methods: PubMed, Scopus, and Web of Science were searched for studies published before February 2024 that used CNN models to detect skull fractures on CT scans. Meta-analyses were conducted for area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Egger's and Begg's tests were used to assess publication bias. Results: Meta-analysis was performed for 11 studies with 20,798 patients. Pooled average AUC for implementing pre-training for transfer learning in CNN models within their training model’s architecture was 0.96 ± 0.02. The pooled averages of the studies' sensitivity and specificity were 1.0 and 0.93, respectively. The accuracy was obtained 0.92 ± 0.04. Studies showed heterogeneity, which was explained by differences in model topologies, training models, and validation techniques. There was no significant publication bias detected. Conclusion: CNN models perform well in identifying skull fractures on CT scans. Although there is considerable heterogeneity and possibly publication bias, the results suggest that CNNs have the potential to improve diagnostic accuracy in the imaging of acute skull trauma. To further enhance these models' practical applicability, future studies could concentrate on the utility of DL models in prospective clinical trials. © The Author(s), under exclusive licence to American Society of Emergency Radiology (ASER) 2024.
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