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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
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时间依赖的深度学习预测多发性硬化症残疾

John D Mayfield1, Ryan Murtagh2, John Ciotti3

  • 1USF Health Department of Radiology, 2 Tampa General Circle, STC 6103, Tampa, FL, 33612, USA. jdmayfield@usf.edu.

Journal of imaging informatics in medicine
|June 13, 2024
PubMed
概括

深度学习模型可以使用纵向MRI数据预测多发性硬化症 (MS) 的残疾进展. 视频视觉变压器 (ViViT) 在基于延长残疾严重程度得分 (EDSS) 的长期患者预测结果方面表现有希望.

关键词:
人工智能的人工智能是人工智能.医学成像医学成像多发性硬化症是多发性硬化症.时间依赖的深度学习视频变压器 视频变压器

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科学领域:

  • 人工智能的人工智能
  • 医疗成像医学成像
  • 神经学 神经学

背景情况:

  • 当前的深度学习模型通常分析单个时间点,与与随时间变化相关联的临床实践不同.
  • 多发性硬化症 (MS) 研究一直专注于病变细分,对长期残疾进展的分析有限.
  • 纵向分析对于了解疾病轨迹和告知患者管理至关重要.

研究的目的:

  • 提出和评估时间依赖的深度学习模型,用于预测MS的长期残疾.
  • 为了对视频视觉转换器 (ViViT) 与卷积神经网络长期短期记忆 (CNN-LSTM) 和视觉转换器-LSTM (ViT-LSTM) 架构进行比较.
  • 评估模型使用扩展残疾严重程度评分 (EDSS) 预测残疾进展的能力.

主要方法:

  • 使用了703名患有椎脊椎MRI (2002-2023) 的患者队列.
  • 视频视觉变压器 (ViViT) 与基于VGG-16的CNN-LSTM和ViT-LSTM进行了比较.
  • 进行了废弃分析,以确定模型性能的时间依赖性.

主要成果:

  • 在6年内预测三元EDSS时,ViViT的AUC (0.84) 比VGG16-LSTM (0.74) 高 (p < 0.001).
  • 在分析较短的持续时间 (2年MRI数据) 时,VGG16-LSTM的表现优于ViViT.
  • 准确的EDSS分类 (回归或分类) 总体上产生了更差的表现.

结论:

  • 时间依赖的深度学习模型可以有效地预测MS残疾使用三元分层,反映临床评估.
  • 模型架构的选择可能取决于可用的纵向数据持续时间.
  • 需要在外部队列和临床试验中进一步验证以证实这些发现.