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相关概念视频

Glaucoma: Overview01:25

Glaucoma: Overview

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Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...
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相关实验视频

Updated: May 10, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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比较无代码平台和深度学习模型用于从 Fundus 图像中检测 DrDeramus.

Mauro Gobira1, Luis F Nakayama2, Caio Vinicius S Regatieri2

  • 1Ophthalmology, Vision Institute - Instituto Paulista de Estudos e Pesquisas em Oftalmologia (IPEPO), São Paulo, BRA.

Cureus
|April 24, 2025
PubMed
概括
此摘要是机器生成的。

像Create ML和Teachable Machine这样的无代码人工智能平台在从 fundus 图像分类眼方面表现出强的表现,尽管ResNet200d仍然优越. 这些工具使医疗保健中的AI民主化.

关键词:
阿克里马数据集数据集创建一个ml ml ml.深度学习是一种深度学习.基金图片 基金图片 基金图片青光眼的检测仪 青光眼检测仪在 resnet200d 中使用.可教的机器可教的机器

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科学领域:

  • 眼科医生 眼科 眼科
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 青光眼的诊断依赖于解释视神经底部图像.
  • 传统的深度学习模型需要大量的专业知识和计算资源.
  • 无代码机器学习平台为医疗应用提供了可访问的AI开发.

研究的目的:

  • 将谷歌的Teachable Machine (TM) 和果的Create ML的诊断性能与传统的ResNet200d模型进行比较.
  • 通过使用ACRIMA数据集,评估无代码平台在视神经底部图像上对光病的分类中的有效性.

主要方法:

  • 从ACRIMA数据集中对705个标记的 fundus 图像进行了比较分析.
  • 训练和验证可教机器,创建ML和ResNet200d模型.
  • 评估性能指标,包括灵敏度,特异性和F1分数.

主要成果:

  • ResNet200d实现了最高的精度 (99.29%),灵敏度 (98.57%) 和特异性 (100%).
  • 创建ML显示高特异性 (98.48%) 和F1得分为95.83%.
  • 可教机器显示出更高的灵敏度 (95.71%),F1得分为95.04%.

结论:

  • 没有代码的平台,创建ML和TM,在 fundus 图像中表现出强大的玻璃眼瘤检测能力.
  • 虽然ResNet200d提供了卓越的诊断准确性,但无代码工具使人工智能在医疗保健中民主化,特别是在资源有限的环境中.
  • 建议对各种数据集进行进一步验证,以确认这些可访问的AI工具的潜力.