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相关概念视频

Brain Imaging01:14

Brain Imaging

228
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
228

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相关实验视频

Updated: Jun 27, 2025

Artificial Intelligence Approaches to Assessing Primary Cilia
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使用人工智能方法进行神经图像分析:系统性审查.

Eric Jacob Bacon1,2, Dianning He1, N'bognon Angèle D'avilla Achi3

  • 1College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.

Medical & biological engineering & computing
|April 25, 2024
PubMed
概括

人工智能 (AI) 显著增强了对大脑疾病的神经图像分析. 与传统方法相比,机器学习和深度学习方法在疾病分类和病变细分方面表现优越.

关键词:
人工智能的人工智能是人工智能.深度学习是一种深度学习.机器学习是机器学习.精神疾病 精神疾病神经成像是一种神经成像.神经系统疾病 神经系统疾病

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Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
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Last Updated: Jun 27, 2025

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科学领域:

  • 神经成像是一种神经成像.
  • 人工智能的人工智能
  • 医疗数据分析 医学数据分析

背景情况:

  • 人工智能 (AI) 正在彻底改变神经成像数据分析,改善我们对复杂大脑功能的理解.
  • 人工智能技术的整合为神经科学中的诊断能力提供了潜在的进步.

研究的目的:

  • 研究人工智能技术对神经成像数据分析的影响.
  • 提高诊断能力,并推进人工智能驱动的神经成像领域.

主要方法:

  • 从2013年到2023年,在PubMed,IEEE Xplore和Scopus进行了系统的文献搜索.
  • 策划了456篇关于人工智能驱动的神经成像分析的文章,其中104篇根据严格的纳入标准和质量评估进行了选择.
  • 研究的重点是针对精神和神经障碍的各种神经成像模式,采用精确的数据提取协议.

主要成果:

  • 该审查包括了104项研究,其中19.2%的研究重点是精神疾病,80.7%的研究重点是神经障碍.
  • 确定的主要临床应用是疾病分类 (58.7%) 和病变细分 (28.9%).
  • 机器学习和深度学习算法在神经成像分析中表现出比传统方法更好的性能.

结论:

  • 人工智能驱动的神经成像分析具有改变研究和临床应用的巨大潜力.
  • 这项研究强调了人工智能,特别是机器学习和深度学习在推进神经影像诊断和理解大脑疾病方面的有效性.