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人工智能与指甲诊断相遇:新兴的基于图像的传感平台用于非侵入性疾病检测.

Tejrao Panjabrao Marode1, Vikas K Bhangdiya1, Shon Nemane1

  • 1Department of Electronics & Telecommunication Engineering, Shri Sant Gajanan Maharaj College of Engineering, Shegaon 444203, Maharashtra, India.

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人工智能 (AI) 和机器学习 (ML) 可以分析指甲图像以诊断疾病. 本综述综合了用于指甲损伤分析的AI应用,克服了可访问医疗保健的临床采用障碍.

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.皮肤学 皮肤学可以解释的人工智能AI机器学习是机器学习.指甲图像分析指甲图像分析非侵入性诊断是一种非侵入性诊断.医疗点诊断的诊断方法基于智能手机的健康监测

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科学领域:

  • 皮肤病学 皮肤病学
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 人类指甲是数字健康诊断中未得到充分利用的生物基质.
  • 指甲病理作为非侵入性生物标志物用于系统性疾病,如贫血,糖尿病和黑色素瘤.
  • 目前的皮肤学评论缺乏对人工智能驱动的指甲损伤分析的具体关注.

研究的目的:

  • 提供图像分析技术的综合综合,包括AI/ML用于指甲损伤诊断.
  • 专注于与指甲病理相关的诊断和查应用.
  • 为了弥合临床皮肤病学,人工智能和基于指甲的诊断的移动健康之间的差距.

主要方法:

  • 审查技术模式,包括智能手机成像,皮肤镜检查和光学一致性断层扫描.
  • 分析图像处理技术,如颜色校正,细分和区域裁剪.
  • 评估从经典方法到深度学习的诊断模型,包括可解释AI (XAI) 和联合学习.

主要成果:

  • 对特定指甲疾病的人工智能应用的详细描述.
  • 讨论临床应用的障碍:数据稀缺,皮肤类型变异,注释错误和采用挑战.
  • 重点是新兴的解决方案,如XAI,联合学习和基于智能手机的诊断.

结论:

  • 人工智能支持的指甲分析有可能为可扩展,公平和值得信赖的医疗诊断提供潜力.
  • 跨学科创新对于将人工智能指甲分析从原型转化为常规医疗保健至关重要.
  • 在全球查计划和临床实践中倡导人工智能驱动的指甲分析.