深度学习将类风湿性关节炎与骨关节炎区分在手部放射学上
Yuntong Ma1, Ian Pan2, Stanley Y Kim2
1Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA. ym2271@gmail.com.
Skeletal radiology
|August 2, 2023
概括
一个深度学习模型准确地区分了类风湿性关节炎 (RA) 和骨关节炎 (OA) 从手部X射线. 这种人工智能工具在识别不同形式的关节炎以改善诊断方面表现出很高的性能.
科学领域:
- 医学成像AI 医疗成像AI
- 放射学中的深度学习.
- 风湿病学诊断 风湿病学诊断
背景情况:
- 类风湿性关节炎 (RA) 和骨关节炎 (OA) 是常见的关节疾病.
- 准确的区分对于有效的治疗和患者管理至关重要.
- 手部放射图经常用于诊断,但解释可能具有挑战性.
研究的目的:
- 开发和评估一个深度学习模型来区分RA和OA,使用手部X射线图.
- 评估预训练和训练参数对模型诊断性能的影响.
主要方法:
- 一个卷积神经网络被训练在8387名患者的9714张手部X射线图上.
- 该模型的性能在一个由250项考试组成的独立测试组中进行了评估.
- 评估了对二元 (关节炎与非关节炎;RA与非RA) 和三元 (无关节炎与OA与RA) 分类的诊断能力.
- 研究了图像分辨率,额外的预训练和视图选择的影响.
主要成果:
- 该模型在区分关节炎类型方面取得了高性能,接收器运行特征曲线 (AUC) 下的面积为0.975对于关节炎与没有关节炎,0.955对于RA与没有RA.
- 对于三向分类,该模型实现了0.806的科恩卡帕和87.2%的准确性.
- 将图像分辨率提高到1024 × 1024像素提高了性能.
- 额外的预训练和使用所有放射视图并没有显著影响表现.
结论:
- 一个深度学习模型在手部X射线图上对没有关节炎,OA和RA进行分类时表现出高性能.
- 这种人工智能工具有可能有助于准确诊断关节炎疾病.
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