人工智能在中的神经成像
Sophie Adler1,2,3, Konrad Wagstyl1,2
1Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London.
Current opinion in neurology
|February 20, 2026
概括
人工智能 (AI) 正在彻底改变神经成像,机器学习模型有助于病变检测和发作局部化. 需要进一步开发这些先进的人工智能工具的更广泛的临床整合.
科学领域:
- 神经成像是一种神经成像.
- 人工智能的人工智能
- 的研究研究.
背景情况:
- 人工智能 (AI) 能力和神经成像数据集的快速进步加速了AI在研究中的作用.
- 人工智能为分析复杂的神经成像数据提供了新的方法,以改善的诊断和管理.
研究的目的:
- 审查人工智能 (AI) 在神经成像领域的主要应用.
- 识别和建议未来的研究方向,用于AI在神经成像.
主要方法:
- 审查各种机器学习方法,包括多层感知子和卷积神经网络 (CNN).
- 人工智能的应用用于预测,检测病变,发作区域定位和图像细分.
主要成果:
- 人工智能模型已经成功地应用于预测,检测病变,定位发作发作区域,并细分术后腔.
- 机器学习技术的范围从传统模型到先进的体积和基于图形的CNN.
- 用于病变检测和定位的AI工具越来越多,并得到了验证.
结论:
- 症神经成像中的人工智能在很大程度上专注于病变检测和定位,可用既有工具.
- 在症神经成像中出现的AI应用需要进一步开发和验证.
- 解决临床整合挑战对于未来在治疗中采用人工智能工具至关重要.
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