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通过人工智能模型探索亨廷顿病诊断:综合性审查

Sowmiyalakshmi Ganesh¹, Thillai Chithambaram¹, Nadesh Ramu Krishnan²

¹School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India.

Diagnostics (Basel, Switzerland)

|December 9, 2023

概括

人工智能 (AI) 显示出对诊断亨廷顿病 (HD) 的承诺. 机器学习和深度学习算法可以分析各种数据,以改善早期检测和患者管理.

科学领域:

神经科学是一个神经科学.
医疗信息学医疗信息学
计算生物学计算生物学

背景情况:

亨廷顿病 (HD) 是一种严重的神经退行性疾病,具有运动,认知和精神症状.
早期和准确的诊断对于管理HD和改善患者的治疗结果至关重要.
现有的诊断方法可能是侵入性的或耗时的.

研究的目的:

审查人工智能 (AI) 算法在诊断亨廷顿病中的应用.
为了确定AI驱动的HD诊断的趋势,方法和挑战.
突出机器学习 (ML) 和深度学习 (DL) 在自动化HD检测方面的潜力.

主要方法:

对HD诊断中的AI现有文献进行系统审查.
分析利用临床,遗传和神经成像数据的研究.
对诊断准确性的ML和DL方法的评估.

主要成果:

人工智能,特别是ML和DL,显示出自动化亨廷顿病诊断的巨大潜力.
分析多模式数据 (临床,遗传,神经成像) 可以提高诊断准确度.
确定了人工智能用于HD的应用中的关键趋势和方法.

结论:

由人工智能驱动的算法为亨廷顿病的早期和准确诊断提供了一个有希望的途径.
需要进一步的研究,以解决广泛临床采用的局限性和伦理考虑.
本综述是人工智能和神经退行性疾病研究交叉的专业人士的资源.

关键词:

人工智能的人工智能亨廷顿病是亨廷顿氏病的一种疾病.深度学习是一种深度学习.诊断诊断诊断诊断诊断诊断机器学习是机器学习.

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