股骨支骨受损的诊断可以在骨盆放射图上使用深度学习方法进行
Ebru Atalar1, Kemal Üreten, Ulunay Kanatlı
1Ankara Şehir Hastanesi, İç Hastalıkları Kliniği, Romatoloji Bölümü, 06800 Çankaya, Ankara, Türkiye. atalarebrudr@yahoo.com.
Joint diseases and related surgery
|July 18, 2023
概括
深度学习模型,特别是VGG-16,使用部X射线图显示出强大的足骨支骨阻塞 (FAI) 诊断能力. 这种人工智能方法可以帮助临床医生诊断FAI,改善患者护理.
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 整形外科 整形外科
背景情况:
- 腿筋关节阻塞 (FAI) 是一种影响关节的疾病.
- 盆腔放射是怀疑FAI的初始成像方式.
研究的目的:
- 评估深度学习模型的诊断性能,特别是卷积神经网络 (CNN),用于使用部放射图检测FAI.
- 为了比较不同预训练的CNN模型对FAI诊断的有效性.
主要方法:
- 从516名患者 (2010-2020年) 的888张部放射图的回顾性分析.
- 在转移学习中使用深度学习模型 (VGG-16,ResNet-101,MobileNetV2,Inceptionv3).
- 使用准确度,灵敏度,特异性,精度,F-1分数和AUC来评估性能.
主要成果:
- 与其他模型相比,VGG-16模型在诊断FAI方面表现优越.
- VGG-16实现了86.6%的精度,82.5%的灵敏度,89.6%的特异性,85.5%的精度,83.9%的F1得分和0.92的AUC.
- 这些结果突出了深度学习在FAI诊断中的潜力.
结论:
- 深度学习方法在帮助诊断股骨支柱入方面表现有前途.
- 使用人工智能对盆腔放射图的分析可以增强FAI的诊断能力.
- 这项技术可以在疑似FAI病例中支持临床决策.
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