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人工智能用于X射线骨骨折检测:一个系统的审查和诊断测试准确性的元分析

Matan Kraus^1,2, Roi Anteby^3,4, Eli Konen^5,3

¹Department of Diagnostic Imaging, Sheba Medical Center, 2 Sheba Road, 5262000, Ramat Gan, Israel. Matan.Kraus@gmail.com.

European radiology

|December 14, 2023

概括

人工智能 (AI) 在X射线上诊断骨折方面表现有前途,提供高灵敏度和特异性. 需要进一步的研究来比较AI.

科学领域:

放射学和医学成像学医学成像学
医疗保健中的人工智能
骨科诊断骨科诊断骨科诊断

背景情况:

骨骨折经常使用X射线进行诊断,X射线是一种低灵敏度的方法.
隐秘骨折的高率可能导致错过诊断.
人工智能 (AI) 为改进骨骨折检测提供了一个潜在的解决方案.

研究的目的:

在X射线上系统地审查和元分析AI用于骨骨折的诊断准确性.
评估AI在检测骨骨折中的有用性.
在本应用程序中评估卷积神经网络 (CNN) 的性能.

主要方法:

按照PRISMA和PRISMA-诊断测试准确性指南进行系统审查和元分析.
在PubMed数据库的文献搜索到2023年7月.
使用QUADAS-2工具评估偏差风险和适用性;双变随机效应元分析和SROC曲线分析.

主要成果:

包括十项回顾性研究;AI诊断性能从AUC0.77到0.96.9不等.
七项研究的元分析 (3373张图像) 给出了0.80的综合灵敏度和0.89.8的特异性.
总的来说,元分析的AUC为0.88,但10项研究中有9项显示出偏差的高风险和对适用性的担忧.

结论:

人工智能在骨骨折方面表现出有希望的诊断性能,具有高的整体灵敏度,特异性和SROC结果.
人工智能系统可以帮助临床医生和放射科医生进行早期诊断,并可能防止错过的伤害.
需要进一步的临床研究来比较人工智能与人类诊断人员的性能.

关键词:

深度学习是一种深度学习.机器学习机器学习骨骨头骨头的骨头手腕骨折手腕骨折的情况.

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