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开源卷积神经网络,根据普通X光照的AO/OTA分类对远距离辐射骨折进行分类
Koen D Oude Nijhuis1,2, Jasper Prijs3,4,5, Britt Barvelink6
1Department of Orthopedic Surgery, University Medical Centre Groningen and Groningen University, Groningen, The Netherlands. k.d.oude.nijhuis@umcg.nl.
一个开源的卷积神经网络 (CNN) 算法在分类某些远半径骨折 (DRF) 方面表现出极好的准确性,但在其他方面遇到了困难. 这凸显了当前骨折分类系统对AI和外科医生的挑战.
科学领域:
- 整形外科手术 整形外科手术
- 放射学 放射学是一门学科.
- 医学中的人工智能.
背景情况:
- 卷积神经网络 (CNN) 在骨折检测方面显示出潜力.
- 外科医生在分类远半径骨折 (DRF) 时表现出不一致.
- AO/OTA分类系统被广泛使用,但可能存在局限性.
研究的目的:
- 为DRF分类创建和外部验证一个开源的CNN算法.
- 根据AO/OTA分类系统评估CNN的表现.
- 为了比较CNN分类准确度与外科医生观察者间协议.
主要方法:
- 在使用AO/OTA系统分类的659张DRF放射图上训练了一种CNN算法.
- 该算法在两个1级创伤中心进行了内部验证 (190名患者) 和外部验证 (188名患者).
- 绩效是根据三个独立的外科医生的分类来评估的.
主要成果:
- 美国有线电视新闻网在内部实现了62%的准确性,在外部达到61%.
- 内部验证的曲线下的面积 (AUC) 从0.63到0.93.9不等.
- 外部验证的AUC在0.56到0.88之间,对于2R3A型的DRF和没有DRF的高精度,但对于2R3B和2R3C型的中度精度.
结论:
- 该CNN算法在分类2R3A型DRF和识别没有骨折的病例方面表现出卓越的准确性.
- 在对2R3B和2R3CDRF进行分类时,表现不佳至中等,反映了外科医生观察者间协议的挑战.
- 由于当前分类系统固有的局限性,CNN可能会在骨折分类方面扎.
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