在使用机器学习的前后胸部X射线图中评估灵感和技术质量
L Sorace1, N Raju1, J O'Shaughnessy1
1Department of Radiology, Austin Hospital, Heidelberg, Australia.
Radiography (London, England : 1995)
|November 2, 2023
概括
一个深度学习模型有效地评估胸部X射线图的技术和启发性充分性. 这种人工智能工具对改善放射学质量和教育充满希望.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 胸部X射线图是最常见的X射线程序.
- 低于最佳的技术因素可能会阻碍准确的临床解释.
- 评估技术和启发性充分性对于诊断质量至关重要.
研究的目的:
- 开发和评估一个深度学习模型,以评估前后 (AP) 胸部放射图的技术和启发性充分性.
- 为了确定模型在不同类别的图像质量的性能.
主要方法:
- 一个卷积神经网络被训练在2375个成年AP胸部X射线图上,标记为技术和灵感充足.
- 用回忆,精度,F1和微型F1分数来评估模型性能.
- 用梯度加权类激活映射 (Grad-CAM) 来进行分析,并评估了的影响.
主要成果:
- 该模型在技术上合适的图像上表现最好,在不合适的图像上表现最差.
- 具有足够的技术和灵感的X光照的回忆率最高 (89%).
- 每个级别的F1分数从70%到81%不等,加权和微型F1分数为78%.
结论:
- 开发的机器学习算法在评估AP胸部X射线图的技术和启发性充分性方面表现出有希望的表现.
- 对这种人工智能工具的进一步改进可以为放射学教育和质量改善计划做出重大贡献.
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