基于深度学习的膝盖MRI分类,用于常见的脚部神经麻与脚下降
Kyung Min Chung1, Hyunjae Yu2, Jong-Ho Kim3
1Department of Neurosurgery, Hallym University College of Medicine, Chuncheon 24252, Republic of Korea.
Biomedicines
|December 23, 2023
概括
深度学习准确地识别了常见的部神经 (CPN) 损伤,只使用膝盖MRI扫描来导致脚下降. 这种AI方法有助于诊断CPN损伤,改善患者的治疗结果.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 神经学 神经学
背景情况:
- 脚下垂是一种具有挑战性的疾病,有多种原因,包括常见腹神经 (CPN) 损伤.
- 准确诊断CPN损伤对于有效治疗和管理落脚至关重要.
研究的目的:
- 开发和评估一种深度学习算法,用于使用膝盖MRI轴形象对CPN损伤引起的脚下降进行分类.
- 为了比较不同卷积神经网络 (CNN) 模型在识别CPN损伤方面的性能.
主要方法:
- 一项回顾性研究利用膝盖MRI数据,来自已确认CPN损伤的患者和非创伤性膝盖疼痛患者.
- 培训和验证包括EfficientNet-B5,ResNet152和VGG19在内的CNN模型在2286个MRI扫描的数据集上.
- 使用诸如接收器操作特征曲线 (AUC) 下的面积,精度,回忆,准确性和F1分数等指标进行性能评估.
主要成果:
- 在CPN损伤与非CPN病例的分类中,EfficientNet-B5模型获得了最高的性能,AUC为0.946.
- 与ResNet152和VGG19.2相比,EfficientNet-B5表现出更高的精度,回忆,准确性和F1得分.
- Saliency地图分析表明,EfficientNet-B5专注于神经区域,以准确检测CPN损伤.
结论:
- 膝盖MRI图像的深度学习分析是一种成功的方法,用于区分CPN受伤患者的脚下降.
- EfficientNet-B5算法显示了临床应用的巨大潜力,用于诊断CPN相关的落脚.
- 用人工智能驱动的医学成像分析为改善神经疾病的诊断准确性提供了一个有希望的途径.
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