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使用热图作为可解释的人工智能来检测手腕和肘部放射图上的异常

S Lysdahlgaard¹

¹Department of Radiology and Nuclear Medicine, Hospital of South West Jutland, University Hospital of Southern Denmark, Esbjerg, Denmark; Department of Regional Health Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark; Imaging Research Initiative Southwest (IRIS), Hospital of South West Jutland, University Hospital of Southern Denmark, Esbjerg, Denmark.

Radiography (London, England : 1995)

|October 8, 2023

概括

人工智能模型在通过X光照来诊断手腕和肘部疾病方面取得了很高的准确性. 像Grad-CAM这样的可解释的AI方法为临床采用提供了透明度.

科学领域:

放射学放射学是一门学科.
医疗成像医学成像
人工智能的人工智能

背景情况:

手腕和肘部的X光照对于诊断骨折和退行性疾病至关重要.
由于复杂的解剖学和微妙的标志,解释这些图像可能具有挑战性.
人工智能 (AI) 和深度学习在提高诊断准确度方面表现有前途.

研究的目的:

评估转移学习模型在诊断手腕和肘部放射图的疾病中的表现.
使用梯度加权类激活映射 (Grad-CAM) 评估AI模型的可解释性.

主要方法:

使用了MURA数据集,其中包含了手腕和肘部的肌肉骨放射.
应用了一组20个转移学习模型 (例如VGG16,ResNet,DenseNet).
实现了Grad-CAM用于可视化AI决策过程,并计算了子相似系数 (DSC).

主要成果:

平均测试精度为手腕的0.81和肘部的0.60.
在手腕方面,VGG16获得了最高的精度 (0.84),而在肘部方面,DenseNet169 (0.73) 获得了最高的精度.
对于带有金属植入物的X射线图,模型一致性高于带有骨折的X射线图.

结论:

转移学习模型显示了在手腕和肘部放射学中提高诊断准确性的潜力.
Grad-CAM提供了对模型性能和改进领域的宝贵见解.
由人工智能驱动的诊断,使用可解释的方法,可以提高临床信任和采用.

关键词:

肘部肘部肘部肘部肘部可解释的人工智能放射学放射学放射学放射学转移学习模式的模型.他们的手腕.

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