Diagnostic performance of neural network algorithms in skull fracture detection on CT scans: a systematic review and meta-analysis

• Published in: Emergency radiology Vol. 32; no. 1; pp. 97 - 111
• Main Authors: Sharifi, Guive, Hajibeygi, Ramtin, Zamani, Seyed Ali Modares, Easa, Ahmed Mohamedbaqer, Bahrami, Ashkan, Eshraghi, Reza, Moafi, Maral, Ebrahimi, Mohammad Javad, Fathi, Mobina, Mirjafari, Arshia, Chan, Janine S., Dixe de Oliveira Santo, Irene, Anar, Mahsa Asadi, Rezaei, Omidvar, Tu, Long H.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.02.2025; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial neural networks; Bias; Computed tomography; Deep learning; Demand analysis; Diagnosis; Emergency Medicine; Fractures; Heterogeneity; Humans; Imaging; Machine learning; Medical imaging; Medicine; Medicine & Public Health; Meta-analysis; Neural networks; Neural Networks, Computer; Radiology; Review; Sensitivity analysis; Sensitivity and Specificity; Skull Fractures - diagnostic imaging; Systematic review; Tomography, X-Ray Computed - methods; Topology; Skull; Fracture; Convolutional Neural Networks; Deep Learning
• ISSN: 1438-1435; 1070-3004; 1438-1435
• DOI: 10.1007/s10140-024-02300-7

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.