在自闭症谱系障碍中用于状交叉体细分的概率深度学习方法
Filippo Bargagna1,2,3, Thomas M Morin1,4, Ya-Chin Chen1,5
1A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
NPP - digital psychiatry and neuroscience
|January 30, 2026
概括
在MRI扫描中,CHOROID PLEXus自动细分 (ASCHOPLEX) 准确地细分了脑结构. 这种深度学习工具对自闭症谱系障碍研究有前途,但需要根据年龄进行特定调整,以确保儿童的最佳表现.
科学领域:
- 神经成像是一种神经成像.
- 人工智能在医学中的应用
- 发育神经科学的发展神经科学.
背景情况:
- 胸膜是一个关键的大脑屏障,参与神经免疫功能.
- 在一些患有自闭症谱系障碍 (ASD) 的个体中,观察到状的形态变化.
- 使用磁共振成像 (MRI) 精确细分状,对于大规模的ASD人群研究至关重要.
研究的目的:
- 评估CHOROID PLEXUS (ASCHOPLEX) 自动细分的深度学习工具在患有ASD的个体中进行状结质细分的可通用性.
- 评估ASCHOPLEX在不同年龄组 (儿童和成人) 的性能.
- 调查概率方法用于量化细分不确定性和模型信心的实用性.
主要方法:
- 微调ASCHOPLEX深度学习工具在ASD和对照参与者的本地数据集上.
- 实施一个概率版本的ASCHOPLEX来量化细分不确定性.
- 在自闭症脑成像数据交换 (ABIDE) 数据集上测试ASCHOPLEX的性能,包括儿童和成人.
主要成果:
- 在当地和ABIDE数据集中,ASCHOPLEX在成年人中表现出良好的概括性和精确的冠状交叉体细分.
- 在儿童中,该工具的准确性下降,这表明在没有进一步微调的情况下,对这个年龄组的概括性有限.
- 概率方法提供了信心指标,增强了细分模型的可靠性评估.
结论:
- ASCHOPLEX 是一种有能力的深度学习工具,用于在以前未见的MRI数据中对状的细分,特别是在成年人中.
- 特定年龄的微调是必要的,以确保ASCHOPLEX在儿科患者群体中的准确性和通用性.
- 纳入概率方法通过为临床和研究应用提供关键的信心评估来加强深度学习工具的实用性.
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