莫亚莫亚病患者的加速大脑年龄:深度学习方法和与疾病严重程度的相关性
Wenjie Li1, Suhua Chen1, Xin Chen1
1Department of Neurosurgery, Peking University Third Hospital, Beijing, China.
Frontiers in neuroscience
|October 13, 2025
概括
莫亚莫亚病患者表现出加速的大脑衰老,较高的预测大脑年龄与疾病严重程度有关. 这种深度学习方法可能为莫亚莫亚病 (MMD) 的诊断和预后提供了一个新的生物标志物.
科学领域:
- 神经成像是一种神经成像.
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 莫亚莫亚病 (MMD) 是一种罕见的脑血管疾病,其特点是内动脉的渐进性狭窄.
- 准确评估MMD进展及其对大脑健康的影响对于患者管理至关重要.
研究的目的:
- 开发和验证一个深度学习框架 (DenseNet),用于预测MMD患者的大脑年龄.
- 调查预测的大脑年龄与MMD严重程度之间的相关性.
主要方法:
- 分析了432名MMD患者和565名对照组的无增强型MRI扫描.
- 使用DenseNet121架构进行大脑年龄预测.
- 预测的大脑年龄与时间年龄和MRA得分相关联.
主要成果:
- 与慢性年龄相比,MMD患者的预测大脑年龄显著更高 (37.9岁对35.8岁).
- 预测的大脑年龄与MRA分数呈正相关,表明大脑加快衰老与狭窄增加.
- 正常对照对象在预测和时间大脑年龄之间没有显著差异.
结论:
- 基于深度学习的脑年龄预测可以识别MMD中加速的大脑衰老.
- 大脑年龄作为MMD严重程度和进展的潜在成像生物标志物.
- 研究结果支持将大脑年龄预测用于个性化的MMD治疗策略.
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