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相关实验视频

Updated: Jul 1, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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在眼科中比较无代码和定制深度学习方法.

Carolyn Yu Tung Wong1,2,3, Ciara O'Byrne1,2, Priyal Taribagil1,2

  • 1Institute of Ophthalmology, University College London, 11-43 Bath St, London, EC1V 9EL, UK.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
|March 6, 2024
PubMed
概括

无代码深度学习 (CFDL) 使临床医生能够在没有编码的情况下创建AI模型. 虽然对眼科任务有希望,但它的优势超过专家设计的深度学习 (DL) 需要具体的评估.

关键词:
人工智能的人工智能是人工智能.机器学习的自动化没有代码的深度学习.机器学习是机器学习.

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科学领域:

  • 眼科医生 眼科 眼科
  • 人工智能的人工智能
  • 医疗信息学 医疗信息学

背景情况:

  • 无代码深度学习 (CFDL) 使缺乏编码技能的临床医生能够开发人工智能 (AI) 模型.
  • 本综述探讨了CFDL与定制,专家开发的深度学习 (DL) 模型相比,CFDL的优势.

研究的目的:

  • 在眼科中全面审查CFDL相对于定制DL的好处.
  • 分析CFDL在糖尿病视网膜病变查,视网膜多种疾病分类,手术视频分类,眼科和资源管理中的应用.

主要方法:

  • 在MEDLINE (PubMed) 进行了系统的文献搜索,查找"自动ML"和"眼科"的研究,直到2023年6月25日.
  • 确定了5项CFDL研究和相应的定制DL研究进行比较分析,总共包括10项相关研究.

主要成果:

  • 对于分析的眼科任务,研究通常倾向于CFDL而不是定制DL.
  • 关于CFDL优势的讨论往往缺乏深度和适用性,需要对临床医生的意图,患者接受度和成本效益进行特定的背景评估.

结论:

  • 对于没有深度学习专业知识的临床医生来说,CFDL促进了临床AI系统的原型设计.
  • CFDL和定制DL模型提供互补的优势,最佳选择取决于具体任务,需要多维评估.