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通过修改架构的ResNet和组合常规磁共振成像来诊断结性脊柱炎

Riel Castro-Zunti¹, Eun Hae Park^2,3, Hae Ni Park^2,3

¹Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada.

Journal of imaging informatics in medicine

|March 3, 2025

概括

深度学习模型可以使用联合常规MRI扫描来诊断结性脊髓炎 (AS),实现高精度和特异性. 这种方法可能会减少在AS诊断中需要CT扫描的需要.

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:

化脊柱炎 (AS) 是一种慢性炎症性疾病,影响脊椎和关节 (SIJs).
传统的MRI是主要的诊断工具,但CT为骨损伤提供了更高的特异性.
优化MRI分析可以改善AS诊断,减少对CT的依赖.

研究的目的:

评估在常规MRI上训练的卷积神经网络 (CNN) 的有效性,以进行AS分类.
为了确定基于CNN的AS诊断的最佳MRI序列组合.
开发一种用于MRI扫描中检测关节 (SIJ) 的自动化方法.

主要方法:

从56名患者中提取了534个AS和606个控制SIJ,通过三种传统的MRI序列.
与ResNet50,InceptionV3和VGG16 CNN架构进行比较,这些架构通过6倍的交叉验证进行了微调.
开发了一种基于YOLOv5的探测器,用于自动 SIJ 提取.
在RGB组合的MRI序列和单个序列上训练有素的CNN.

主要成果:

在综合MRI序列上训练的CNN显著超过在单个序列上训练的CNN.
优化的ResNet50架构实现了95.26%的精度,96.25%的灵敏度,94.39%的特异性和99.1%的ROC AUC.
一个减少参数的模型实现了可比性能,只有83%的参数更少.
在YOLOv5 SIJ探测器实现了96.88-99.88%的mAP@0.5.5.

结论:

深度学习模型可以从传统的MRI数据有效地诊断AS.
结合MRI序列可以提高诊断性能,从而减少CT的必要性.
自动SIJ检测和CNN分析为AS诊断提供了一个有希望的方法.

关键词:

结性脊柱炎是一种疾病.分类分类分类分类.深度学习是一种深度学习.磁共振成像技术磁共振成像技术对象检测检测对象检测对象检测

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