从使用深度学习模型的X射线图中检测月经患者的血管缩
Krista Wernér1, Turkka Anttila2, Sina Hulkkonen2
1Musculoskeletal and Plastic Surgery, Department of Hand Surgery, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 4, 00290, Helsinki, Finland. krista.laivuori@helsinki.fi.
Journal of imaging informatics in medicine
|February 12, 2024
概括
一个深度学习模型有效地使用手腕X射线扫描来选月经的无血管亡 (AVN). 这种人工智能工具显示出高准确度,有可能改善早期诊断和患者手部功能保护.
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:
- 晚期诊断的无血管亡 (AVN) 的月经会损害手的功能.
- 深度学习 (DL) 在医学图像分析和诊断方面表现有前途.
研究的目的:
- 开发和验证基于细分的DL模型,用于从手腕放射图中诊断月经者的AVN.
- 与临床专家相比,评估DL模型的诊断性能.
主要方法:
- 一个DL模型在319个病人和1228个控制手腕放射图上接受了训练.
- 模型性能在单独的测试组 (10%的数据) 上进行了评估.
- 诊断验证由手术医生进行,MRI证实没有疾病.
主要成果:
- DL模型实现了93.33%的灵敏度和93.28%的特异性.
- 精度为93.28%,曲线下面积 (AUC) 为0.94.28%.
- DL模型的表现与临床专家的表现相当,在AUC方面表现优于一名专家.
结论:
- 开发的DL模型展示了强大的诊断性能为月亮的AVN.
- 这种人工智能工具有可能作为一种有益的选方法.
- 通过DL早期检测可以改善患者的治疗结果和手部功能.
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