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相关概念视频

Arteries of the Lower Limbs01:24

Arteries of the Lower Limbs

172
Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
Various factors can trigger epilepsy, including genetic factors, brain damage, metabolic causes, and unknown etiology. Diagnosis of epilepsy involves electroencephalography (EEG), which...
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Brain Imaging01:14

Brain Imaging

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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...
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相关实验视频

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A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
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人工智能应用于的成像:当前状态和未来的前景.

M Berger1, R Licandro2, K-H Nenning3

  • 1Department of Neurology, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria.

Revue neurologique
|April 2, 2025
PubMed
概括

人工智能 (AI),包括深度学习 (DL) 和机器学习 (ML),正在彻底改变研究和神经影像. 这些人工智能工具旨在改善的诊断,治疗和预测结果.

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.的成像成像研究机器学习是机器学习.扣押检测的检测 扣押检测

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科学领域:

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

背景情况:

  • 人工智能 (AI) 在医学研究中变得越来越重要,对瘤学有重大影响.
  • 深度学习 (DL) 和机器学习 (ML) 是人工智能的核心组成部分,推动了研究的进步.

研究的目的:

  • 探索AI在症神经成像中的应用.
  • 研究AI在病变检测,发作焦点定位和预测术后结果方面的作用.
  • 评估AI在区分患者与健康对照者的潜力.

主要方法:

  • 在多种神经成像模式中研究各种AI驱动的方法.
  • 专注于DL和ML技术来分析与有关的数据.

主要成果:

  • 人工智能应用在病变检测,横向化和发性区域的局部化方面表现有前途.
  • 人工智能工具正在开发中,用于预测术后结果,并将患者与健康个体区分开来.
  • 计算能力的进步正在加速人工智能开发和治疗中的临床整合.

结论:

  • 人工智能为症神经成像提供了一个变革性的机会,增强了诊断和治疗.
  • 在临床实践中更广泛地采用人工智能,需要对患者数据安全采取强有力的监管措施.
  • 未来的进展取决于促进合作和扩大开放访问数据集,以有效地训练ML和DL模型.