使用卷积神经网络来分类低最佳胸部X射线图
Emily Huanke Liu1, Daniel Carrion2, Mohamed Khaldoun Badawy1,2
1Department of Medical Imaging and Radiation Sciences, Monash University, Clayton, Victoria, Australia.
Journal of medical radiation sciences
|July 18, 2025
概括
人工智能模型DenseNet121和YOLOv8在检测低于最佳的胸部X射线 (CXR) 中显示出高精度. 这项技术可以帮助改善图像质量,减少辐射暴露,加快患者诊断.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 放射学质量保证 质量保证
背景情况:
- 胸部X射线 (CXR) 是常见的,但由于质量差,通常需要重复成像.
- 低于最佳的CXR导致辐射暴露增加和诊断延迟.
- 自动检测低质量的CXR对于改善患者护理至关重要.
研究的目的:
- 评估DenseNet121和YOLOv8神经网络在识别非最佳胸部X射线中的有效性.
- 将这些人工智能模型的性能与放射科医生分类进行比较.
- 探索AI在改善CXR质量和患者治疗结果方面的潜力.
主要方法:
- 利用了10,000个CXR的数据集,分为训练,验证和测试子集.
- 使用DenseNet121和YOLOv8神经网络进行图像分类任务.
- 使用接收器操作曲线下的面积 (AUROC) 评估模型性能,并与放射科医生的评估进行比较.
主要成果:
- 丹森网121实现了0.97的AUROC,YOLOv8实现了0.95,展示了强大的分类能力.
- 这两种模型都有效地区分了最佳和次优CXR.
- 观察到与放射科医生对齐的变化,受临床指示等因素的影响.
结论:
- 这两种AI模型都在对胸部X射线质量进行分类方面表现出了很高的有效性.
- 这些人工智能工具有可能为放射科医生提供有价值的反,以改善图像质量.
- 这项研究是首次评估YOLOv8用于CXR质量评估,包括各种CXR类型和儿科病例.
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