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Changes in Skin Color: Clinical Perspectives01:14

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The first thing a clinician sees is the skin, so the examination of the skin should be part of any thorough physical examination. Most skin disorders are relatively benign, but a few, including melanomas, can be fatal if untreated. A couple of the more noticeable disorders, albinism and vitiligo, affect the appearance of the skin and its accessory organs.
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A thorough assessment of respiratory health is paramount in clinical settings to identify and manage respiratory distress and ensure adequate oxygenation. This article elaborates on the critical aspects of respiratory evaluation, including airway assessment, skin color examination, and the observation of accessory muscle use, which are integral to effectively diagnosing and managing patients with respiratory conditions.
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The color of the skin is influenced by a number of pigments, including melanin, carotene, and hemoglobin. Recall that melanin is produced by cells called melanocytes, which are found scattered throughout the stratum basale of the epidermis. The melanin is transferred to the keratinocytes via melanosomes.
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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
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用机器学习在教育材料中的表现皮肤色调分析 (STAR-ED)

Girmaw Abebe Tadesse1, Celia Cintas2, Kush R Varshney3

  • 1IBM Research - Africa, Nairobi, Kenya. girmawabebe@gmail.com.

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概括

医学教育材料缺乏多样化的皮肤色调,可能导致诊断差异. 一个新的人工智能工具STAR-ED (Skin Tone Analysis for Representation in EDucational materials) 自动化了教科书中的皮肤色调评估,揭示了皮肤色调较暗的皮肤色调显著不足.

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科学领域:

  • 医学教育 医学教育
  • 皮肤病学 皮肤病学
  • 机器学习 机器学习

背景情况:

  • 黑暗肤色的图像在医学教育材料中表现不足.
  • 这种代表性不足可能导致种族群体之间诊断皮肤疾病的差异.
  • 手动评估图像多样性是耗时且容易出现错误的.

研究的目的:

  • 开发和验证一个自动化框架,用于评估医学教育材料中的皮肤色调表示.
  • 在常用的医学教科书中量化皮肤色调不平衡的程度.
  • 为改善医学教育内容的多样性提供一个工具.

主要方法:

  • 在教育材料中代表性皮肤色调分析 (STAR-ED) 框架是使用机器学习开发的.
  • STAR-ED处理文件以提取文本,图像和表格.
  • 它在图像中识别皮肤,细分皮肤区域,并使用Fitzpatrick17k数据集估计皮肤色调.

主要成果:

  • 在检测皮肤图像 (0.96 AUROC) 和分类皮肤色调 (0.87 AUROC) 方面,STAR-ED表现出高性能.
  • 对四本医学教科书的外部测试显示,棕色和黑色皮肤色调的图像 (菲茨帕特里克V-VI) 仅占所有皮肤图像的10.5%.
  • 该框架成功量化了皮肤色调表示的显著不平衡.

结论:

  • 在医学教育中,STAR-ED为评估皮肤色调多样性提供了一个自动化和可扩展的解决方案.
  • 这项研究强调了当前医学教科书中对黑色肤色的代表性严重缺乏.
  • 这项技术可以帮助教育工作者,出版商和临床医生创造更具包容性和公平性的医学学习资源.