可靠的深度学习框架用于检测X射线肩部图像中的异常
Laith Alzubaidi1,2,3,4, Asma Salhi2,4, Mohammed A Fadhel4
1School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia.
PloS one
|March 11, 2024
概括
一个新的深度学习框架在X射线上准确地检测肩部异常,优于以前的方法和外科医生. 这种可靠的框架可以提高肌肉骨疾病的诊断准确度.
科学领域:
- 医疗成像医学成像
- 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:
- 肌肉骨疾病影响全球17亿人,导致疼痛和残疾.
- 在紧急情况下,诊断这些情况,特别是肩部异常,是具有挑战性的.
- 现有的X射线分析深度学习方法表现不佳,缺乏透明度.
研究的目的:
- 开发可靠的深度学习 (DL) 框架,用于在X射线图像上检测肩部异常.
- 解决以前DL方法的局限性,包括过度拟合,糟糕的概括和潜在的偏差.
- 为了提高诊断准确度和对人工智能驱动的医学图像分析的信任.
主要方法:
- 提出了一个新的DL框架,包括同域转移学习 (TL) 和特征融合.
- 通过对各种X射线数据集进行预训练和对肩部X射线进行微调,TL减轻了ImageNet的不匹配.
- 功能融合结合了七个DL模型的功能,以训练机器学习分类器.
主要成果:
- 在检测肩部异常方面获得了高精度 (99.2%),F1-Score (99.2%) 和Cohen's kappa (98.5%).
- 使用像Grad CAM,激活可视化和LIME等可视化工具验证结果.
- 超越了先前的DL方法,并且比骨科外科医生 (79.1%) 取得了明显更高的准确性.
结论:
- 拟议的DL框架是有效和强大的,用于用X射线检测肩部异常.
- 该框架显示了更好的概括性和对诊断决策的信任度增加.
- 这一进步为改善肌肉骨疾病的诊断提供了一个有希望的工具.
相关概念视频
01:24X-ray Imaging
5.5K
German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
5.5K
01:17Ultrasonography
4.5K
Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...
During an ultrasonography procedure, a handheld device called...
4.5K