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基于卷积神经网络与X射线图像的宫脊椎病诊断

Yang Xie¹, Yali Nie², Jan Lundgren²

¹Department of Medical Imaging, China Rehabilitation Research Center and Capital Medical University School of Rehabilitation Medicine, Beijing 100068, China.

Sensors (Basel, Switzerland)

|June 19, 2024

概括

一个新的深度学习模型显著改善了从X射线的宫脊椎病诊断. 与手工方法相比,这种人工智能工具提高了准确性,有助于在不同患者群体中更早地检测.

科学领域:

医疗成像医学成像
医疗保健中的人工智能
脊柱诊断脊柱诊断脊柱诊断脊柱诊断

背景情况:

宫脊椎病例的增加,影响年轻人群,增加了对精确X射线查的需求.
图像技术,设备和临床医生经验的变化挑战了宫脊椎病的诊断准确性.

研究的目的:

开发和验证一个深度学习模型,以从X射线图像中改进宫脊椎病的诊断.
为解决由各种成像参数和临床医生的专业知识引起的诊断不一致问题.

主要方法:

一个ResNet-34卷积神经网络被训练在1235个椎X射线图像上,这些图像具有不同的投影角度.
该模型的性能在136张X射线图像的独立数据集上得到验证,这些图像也具有不同的投影角度.

主要成果:

深度学习模型实现了89.7%的分类准确度,用于检测宫脊椎病.
这种准确性明显超过了传统的手动诊断方法,其准确性为68.3%.

结论:

像开发的ResNet-34这样的深度学习模型可以有效地提高宫脊椎病的诊断准确性和效率.
这种人工智能驱动的方法提供了一种可行的方法,可以补充临床专业知识来识别宫脊椎病.

关键词:

X射线分类X射线分类X射线分类宫脊椎病 - - 宫脊椎病深度学习是一种深度学习.多个标签的多个标签.

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X-ray Imaging

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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...