一个开源的卷积神经网络,用于检测和定位远半径骨折在平面放射图
Koen D Oude Nijhuis1,2, Britt Barvelink3, Jasper Prijs4,5,6
1Department of Orthopaedic Surgery, University Medical Centre Groningen and Groningen University, Hanzeplein 1, 9713PZ, Groningen, the Netherlands. k.d.oude.nijhuis@umcg.nl.
概括
这项研究验证了一种开源算法,用于使用卷积神经网络 (CNN) 检测远半径骨折 (DRF). 该算法在X射线图上识别DRF的准确性很高,有助于初级医生进行诊断.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 整形外科手术 整形外科手术
背景情况:
- 距离半径骨折 (DRFs) 是常见的伤害,通常最初由初级医生在时间压力下进行评估.
- 错过或延迟对DRF的诊断可能会导致显著的不良结果.
- 卷积神经网络 (CNN) 提供了提高骨折检测准确性的潜力.
研究的目的:
- 在内部和外部验证一个开源算法来检测和定位DRF.
- 评估算法在放射图上识别DRF的性能.
- 评估算法的实用性,帮助临床医生进行DRF诊断.
主要方法:
- 来自澳大利亚1级创伤中心 (2016-2020) 的手腕创伤放射图的回顾性分析.
- 从两个荷兰1级创伤中心 (188名患者) 开发内部验证数据集 (190名患者) 和外部验证数据集.
- 对DRF进行了放射检查,并注释了半径,肘部和骨折位置. 算法性能由三个外科医生进行评估.
主要成果:
- 该算法是在659张X线图上训练的.
- 内部验证显示,DRF检测的准确率为87%,AUC为0.93.
- 外部验证证明了82%的准确性和0.88AUC,中度的骨折定位准确性 (AP50 of 25).
结论:
- 开源算法有效地以高精度检测DRF.
- 该算法在骨折定位方面提供了中度的准确性.
- 这是第一个基于X射线的CNN,在多家医院进行了外部验证,用于DRF检测.
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