在使用深度神经网络的放射图像中综合检测手关节化和下
Keisuke Izumi1,2,3, Kanata Suzuki4,5, Masahiro Hashimoto4,6
1Division of Rheumatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan. izz@keio.jp.
Scientific reports
|April 2, 2024
概括
这项研究使用深度神经网络 (DNN) 来准确检测X射线在类风湿性关节炎 (RA) 中的关节破坏. 人工智能模型在识别化和脱方面表现出高精度,提高了诊断能力.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 类风湿病学 类风湿病学
背景情况:
- 类风湿性关节炎 (RA) 导致关节破坏,通常使用修改的夏普总分数 (mTSS) 进行评估.
- 精确检测特定的发现,如化,脱和脱,对于评估RA进展至关重要.
研究的目的:
- 开发和评估基于深度神经网络 (DNN) 的整体模型,用于检测类风湿性关节炎的特殊发现 (瘤,脱,脱).
- 为了估计修改的Sharp总分数 (mTSS) 的有效性,使用DNNs进行增强的关节破坏评估.
主要方法:
- 使用集成多个DNN检测模型 (单拍多盒检测器) 的合并机制.
- 该模型在260张手部X射线图像上接受了训练,其中有注释的近端间关节 (PIP) 和下关节 (MP) 关节.
- 使用五倍交叉验证来评估模型的性能.
主要成果:
- 拟议的整体DNN模型在MP和PIP联合区域中实现了超过99.8%的检测率.
- 该模型表现出较高的检测准确度,回忆,精度,特异性,F值和交叉点与单个检测模型相比,对化和亚位突变的检测准确度,回忆,精度,特异性,F值和交叉点.
结论:
- 深度神经网络,特别是集合模型,可以有效地检测类风湿性关节炎中关节破坏的特定迹象.
- 开发的模型有望提高mTSS评估的准确性和效率,并可能成为自动诊断系统的基础.
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