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Photoreceptors and Visual Pathways

Photoreceptors and Visual Pathways

At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

深度学习用于使用 fundus 图像检测视力受损的白内障.

He Xie¹, Zhongwen Li², Chengchao Wu³

¹National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China.

Frontiers in cell and developmental biology

|August 14, 2023

概括

一个新的深度学习系统 (DLS) 通过 fundus 图像有效地选视力受损的白内障. 这种人工智能工具显示出早期检测和及时转诊的潜力,在某些情况下表现优于专家.

关键词:

人工智能的人工智能是人工智能.白内障是白内障,白内障是白内障.深度学习是一种深度学习.基金图片基金图片基金图片视力受损视力受损视力受损

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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
医疗成像医学成像

背景情况:

在全球范围内,白内障是导致视力障碍的主要原因.
早期检测和查对于及时干预和预防视力丧失至关重要.
目前的查方法可能并不总是有效地识别视力受损的白内障.

研究的目的:

开发和评估一个深度学习系统 (DLS) 用于查视障白内障使用 fundus 图像.
将图像分为非白内障,轻度白内障和视力受损白内障.
评估DLS的表现与白内障专家相比.

主要方法:

利用了来自三个临床中心的5,245名受试者的8,395张 fundus图像.
训练并比较了三种深度学习算法:DenseNet121,Inception V3和ResNet50.0.
通过使用接收器操作特征曲线 (AUC) 下的面积,灵敏度和特异性来评估系统性能.

主要成果:

在内部和外部测试数据集中,DenseNet121算法实现了高AUC值,从0.938到0.999.
与白内障专家相比,DLS在检测视力障碍白内障方面表现优异 (p < 0.05).
该系统在分类白内障严重程度方面表现出卓越的准确性.

结论:

以功能为中心的DLS利用 fundus 图像显示了识别视力受损白内障的巨大潜力.
这种人工智能驱动的查工具可以促进患者及时转诊到专业的眼科护理.
DLS提供了一种有希望的方法来提高白内障查效率和有效性.