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使用机器学习预测牛皮的严重程度:一个系统的审查.

Eric P McMullen1, Yousif A Al Naser2,3, Mahan Maazi4

  • 1Division of Dermatology, Department of Medicine, University of Toronto, Toronto, ON, Canada.

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PubMed
概括
此摘要是机器生成的。

机器学习 (ML) 在预测牛皮的严重程度方面表现有前途,主要使用基于图像的模型. 然而,有限的潜在临床应用和数据异质性需要进一步的大规模试验,使人工智能能够推进牛皮的治疗.

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科学领域:

  • 皮肤病学 皮肤病学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 机器学习 (ML) 在皮肤病理学中的应用超出了皮肤病变的诊断范围.
  • 对于ML在预测牛皮的严重程度方面存在系统性审查差距.

研究的目的:

  • 系统地审查有关ML算法的文献,以预测牛皮的严重程度.
  • 确定这些ML工具当前临床应用的局限性.

主要方法:

  • 在Embase,MEDLINE,ACM数字图书馆,Scopus和IEEE Xplore中进行全面的搜索.
  • 包括标准适用于从创立到2024年8月的产品.

主要成果:

  • 其中包括30篇文章,1篇使用血清生物标志物,29篇使用基于图像的模型.
  • 牛皮面积和严重程度指数 (PASI) 是最常见的得分 (15篇文章),其次是身体表面积 (5篇文章).

结论:

  • 小研究规模和异质性限制了当前审查的结果.
  • ML/AI 显示了 psoriasis 管理的潜力,特别是在非图像应用中.
  • 用多样化的图像数据进行大规模前性试验对于验证AI预测模型至关重要.