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
Skull base Research Center, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Tehran University of Medical Sciences, School of Medicine, Tehran, Iran.
Emerg Radiol. 2025 Feb;32(1):97-111. doi: 10.1007/s10140-024-02300-7. Epub 2024 Dec 16.
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.
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.
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.
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.
颅骨骨折潜在的复杂性以及其下方解剖结构的复杂性，给评估计算机断层扫描（CT）的放射科医生带来了诊断障碍。放射科医生短缺以及对快速准确骨折诊断的需求不断增加，凸显了自动化诊断工具的必要性。卷积神经网络（CNN）是一类潜在的新型医学成像技术，利用深度学习（DL）提高诊断准确性。本系统评价和荟萃分析的目的是评估CNN模型在CT图像上诊断颅骨骨折的效果。
在PubMed、Scopus和Web of Science数据库中检索2024年2月之前发表的使用CNN模型检测CT扫描颅骨骨折的研究。对受试者工作特征曲线下面积（AUC）、敏感性、特异性和准确性进行荟萃分析。使用Egger检验和Begg检验评估发表偏倚。
对11项研究共20798例患者进行了荟萃分析。在其训练模型架构内对CNN模型进行迁移学习预训练的合并平均AUC为0.96±0.02。这些研究的敏感性和特异性合并平均值分别为1.0和0.93。准确性为0.92±0.04。研究显示存在异质性，这可以通过模型拓扑结构、训练模型和验证技术的差异来解释。未检测到显著的发表偏倚。
CNN模型在CT扫描中识别颅骨骨折方面表现良好。尽管存在相当大的异质性以及可能的发表偏倚，但结果表明CNN有潜力提高急性颅骨创伤成像的诊断准确性。为了进一步提高这些模型的实际适用性，未来的研究可以集中在DL模型在前瞻性临床试验中的效用上。
