基于多式超声波图像的基于深度学习的风湿性关节炎疾病活动的自动评分模型
Xuelei He1,2,3, Ming Wang1, Chenyang Zhao1
1Department of Ultrasound, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.
Rheumatology (Oxford, England)
|July 20, 2023
概括
使用多式超声波图像的深度学习模型可以客观地量化类风湿关节炎 (RA) 活动. 动态深度学习模型在协助放射科医生提高RA超声波分级准确度方面表现有前途.
科学领域:
- 类风湿病学 类风湿病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 风湿性关节炎 (RA) 活动评估传统上依赖于临床评估和成像.
- 需要使用定量和客观方法来提高RA活动分级的准确性.
研究的目的:
- 调查深度学习 (DL) 模型的价值,使用多式超声波 (US) 图像来量化RA活动.
- 将DL模型的性能与经验丰富的放射科医生进行比较.
主要方法:
- 使用静态灰度 (SGS),动态灰度 (DGS),静态功率多普勒 (SPD) 和动态功率多普勒 (DPD) 的美国图像开发了四种DL模型 (ResNet类型).
- 模型在1244张图像上接受训练,并在两个队列 (152张和354张图像) 上进行测试.
- 绩效与EULAR-OMERACT Synovitis评分系统进行了评估,并与12名放射科医生进行了比较.
主要成果:
- 动态DL模型,特别是DPD和DGS,在量化RA活性方面表现强 (AUC高达0.95).
- DL模型取得的结果与经验丰富的放射科医生相美 (ICC:0.239-0.756).
- 动态DL模型通常优于静态模型和大多数高级放射科医生,特别是DPD模型.
结论:
- 使用多式联网图像的深度学习模型为评估RA活动提供了定量和客观的方法.
- 动态DL模型具有很大的潜力,可以帮助放射科医生提高美国RA分级的准确性.
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