通过视觉转换器和基于规则的分类来自动检测椎脊柱狭窄和带压缩
David L Payne1,2, Xuan Xu2, Farshid Faraji3,2
1From the Department of Radiology (D.L.P., F.F., K.J., K.F.P., V.V.B., L.B.), Stony Brook University Hospital, Stony Brook, New York David.payne@stonybrookmedicine.edu.
AJNR. American journal of neuroradiology
|February 15, 2024
概括
一种新的视力变压器 (ViT) 模型准确地检测到椎脊髓压缩,优于卷积神经网络 (CNN). 这种人工智能工具显示出改善放射科医生的工作流程和诊断一致性的潜力.
科学领域:
- 放射学中的人工智能
- 医学成像分析 医学成像分析
- 深度学习用于诊断支持
背景情况:
- 椎脊髓压缩可能导致严重的神经缺陷,需要及时干预.
- 目前的诊断方法缺乏自动化工具,以提醒放射科医生对带压缩的注意.
- 准确及时检测至关重要,以防止不良的临床结果.
研究的目的:
- 评估视觉变压器 (ViT) 模型在检测椎脊髓压缩方面的有效性.
- 将ViT的性能与传统卷积神经网络 (CNN) 模型进行比较.
- 评估一个自动化工具在改善临床工作流程方面的潜力.
主要方法:
- 分析了一组142个椎脊柱MRI的队列,其中存在不同程度的狭窄和压缩.
- 一个视力变压器 (ViT) 模型进行了微调,以进行部分级严重性预测.
- 性能与ResNet50和DenseNet121CNN模型在部分和患者层面进行了比较.
主要成果:
- 与ResNet50 (72%) 和DenseNet121 (78%) 相比,ViT模型实现了更高的截面精度 (82%).
- ViT表现出优异的患者级分类准确性 (93%),具有高灵敏度 (0.90) 和特异性 (0.95).
- 与两种CNN相比,ViT模型显示了接收器操作特征曲线下的优越区域.
结论:
- 结合基于规则的分类,ViT模型有效地检测了患者水平的椎脊髓压缩.
- 在这个任务中,ViT模型显著超过了传统的CNN.
- 这种自动化工具的临床实施可以提高神经放射学工作流程的效率和一致性.
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