基于人工智能的儿童尾骨折检测:常见骨折类型和位置的性能和局限性
Irmhild Altmann-Schneider1,2, Christian J Kellenberger3,4, Sarah-Maria Pistorius3,4
1Department of Diagnostic Imaging, University Children's Hospital Zurich, Steinwiesstrasse 75, 8032, Zurich, Switzerland. i.altmann-schneider@hotmail.com.
Pediatric radiology
|December 15, 2023
概括
人工智能在检测前臂和下腿的儿科骨折方面表现有前途,但需要改进才能可靠地检测儿童的所有骨折类型和位.
科学领域:
- 儿科放射学 儿科放射学
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
背景情况:
- 关于用于儿科骨折检测的人工智能的研究有限,往往忽视间接的迹象和位移.
- 现有的人工智能工具尚未全面评估儿科骨折诊断的准确性.
研究的目的:
- 评估人工智能软件BoneView的诊断准确性,用于检测儿科患者的骨折,间接骨折迹象和脱位.
- 评估人工智能工具在不同儿科骨部位的性能,包括前臂,下腿和肘部.
主要方法:
- 追溯分析了来自单一急诊室的每一个身体部位的1000张X射线图,重点是0-18岁的儿童.
- 人工智能软件BoneView被用于检测骨折,间接迹象和位移,共识诊断作为参考标准.
- 纳入标准规定了前臂,下腿或肘部疑似骨折,并至少在两个投射处进行了X线图.
主要成果:
- 对于腿部骨折 (87.5%, 87.5%, 98.3%, 98.3%) 和前臂骨折 (92.9%, 98.1%, 98.4%, 91.7%) 观察到高灵敏度,特异性,PPV和NPV.
- 特定骨折类型的检测率较低,包括幼儿骨折,床骨折和某些肘部骨折/脱.
- 对于肘部骨折 (灵敏度: 80.5%,NPV: 84.7%) 和关节输液 (灵敏度: 85.1%,特异性: 85.7%) 发现了中等的性能.
结论:
- 人工智能工具BoneView展示了前臂和下腿儿科骨折的有希望的诊断性能.
- 为了使AI软件可靠地用于临床医生单独依赖儿科骨折检测,需要进一步改进.
- 该研究强调,需要继续开发人工智能,以解决儿童成像中检测微妙骨折和位移的局限性.
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