在胸部X射线中检测投影类型和左右反转的可通用AI方法
Yukino Ohta1, Yutaka Katayama2, Takao Ichida2
1Department of Clinical Radiology, Faculty of Health Sciences, Hiroshima International University, 555-36 Kurosegakuendai, Higashi-Hiroshima City, Hiroshima, 739-2695, Japan. y-ohta@hirokoku-u.ac.jp.
Radiological physics and technology
|May 23, 2025
概括
这项研究开发了一个人工智能 (AI) 系统,使用深 convolutional 神经网络 (DCNN) 来自动验证胸部X射线成像方向. 人工智能系统显示出自动化验证的潜力,但需要对本地数据进行微调以获得最佳性能.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 深度学习用于医学诊断
背景情况:
- 手动验证胸部X射线图像的定向和反转是具有挑战性和耗时的.
- 确保正确的成像方向 (例如,前后,前后) 对于精确的放射学解释至关重要.
- 现有的图像验证方法缺乏自动化和效率.
研究的目的:
- 开发和评估基于人工智能的系统,用于自动验证胸部X射线成像方向和与检查命令的一致性.
- 将胸部X射线图像分为四类:前后背 (AP),前后背 (PA),翻转的AP和翻转的PA.
- 评估不同培训数据集对AI系统分类准确性的影响.
主要方法:
- 开发一个深层卷积神经网络 (DCNN) 用于图像分类.
- 在多个公开可用的胸部X射线数据集上训练DCNN.
- 在内部和外部数据集上测试DCNN,以评估概括性.
- 使用Grad-CAM可视化网络的决策过程.
主要成果:
- 该DCNN准确地分类成像方向,并检测到图像反转.
- 分类准确度受到培训数据集的显著影响,在外部数据上性能下降.
- 在混合数据集上的训练提高了准确性,但在COVID-CXNet数据集 (76.0%) 上测试时仍然显示下降.
- 可视化突出显示了关键区域,如心脏轮和影响分类的手臂位置.
结论:
- 人工智能,特别是DCNN,在胸部X射线中自动验证成像方向和定位方面具有重大潜力.
- 人工智能模型的性能高度依赖于培训数据的特征.
- 微调AI模型以局部数据特征为基础,对于在临床环境中实现最佳和可靠的性能至关重要.
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