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使用人工智能来改善身体的铁量化:一个范围审查.

Abdulqadir J Nashwan1, Ibraheem M Alkhawaldeh2, Nour Shaheen3

  • 1Department of Nursing, Hazm Mebaireek General Hospital, Hamad Medical Corporation, Doha, Qatar; Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar.

Blood reviews
|September 25, 2023
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概括

人工智能 (AI) 显示出改善查,诊断和监测身体铁障碍的承诺,如贫血和铁过载. 需要进一步的研究才能充分实现人工智能.

关键词:
贫血 贫血 是一种疾病.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.血红色素变异症是什么 血红色素变异症是什么铁过载是因为铁过载.肝脏 铁的度 铁的度机器学习 机器学习

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科学领域:

  • 生物医学信息学是生物医学信息学.
  • 计算病理学计算病理学
  • 医疗人工智能的人工智能

背景情况:

  • 身体铁含量障碍,包括铁过载和贫血,对全球健康构成重大挑战.
  • 目前用于铁相关疾病的诊断和监测方法可能是侵入性的或缺乏精度.
  • 人工智能 (AI) 的整合为医学诊断提供了潜在的进步.

研究的目的:

  • 对AI在体内铁水平障碍的查,诊断和监测中的应用进行范围审查.
  • 识别和分类用于与铁代谢及其相关条件相关的研究中的机器学习算法.
  • 评估人工智能在管理铁过载和贫血方面的现阶段和未来潜力.

主要方法:

  • 系统的文献搜索用于在铁相关疾病的背景下使用机器学习的研究.
  • 审查和综合确定研究,重点是AI算法,使用的数据类型和研究特征.
  • 分析AI在量化铁度及其诊断能力方面的作用.

主要成果:

  • 一系列不同的机器学习算法已经应用于与铁有关的疾病.
  • 大多数审查的研究主要使用单一的数据类型用于AI模型开发.
  • 在不同研究的样本大小,参与者人口统计和地理范围方面观察到显著的异质性.

结论:

  • 人工智能用于量化体内铁度的应用还处于起步阶段,但显示出巨大的潜力.
  • 人工智能驱动的诊断生物标志物可能为早期检测和治疗铁性疾病提供新的策略.
  • 需要进一步开发和验证,以使人工智能成为铁代谢障碍查,诊断和监测的可靠工具.