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后照光下眼前节的远红光、高分辨率、无反射图像。

Far-Red, High-Resolution, Reflection-Free Images of the Anterior Segment in Retro-Illumination.

作者信息

Ain Anthony, Poinard Sylvain, Lepine Thierry, Dorado-Cortez Oliver, Urbaniak Sébastien, Papillon Jean-Marie, Gain Philippe, Thuret Gilles

机构信息

Laboratory for Biology, Engineering and Imaging for Ophthalmology, BiiO, Faculty of Medicine, Health & Innovation Campus, Jean Monnet University, Saint-Etienne, France.

Institut d'Optique Graduate School, Saint-Etienne, France.

出版信息

Transl Vis Sci Technol. 2025 Jun 2;14(6):11. doi: 10.1167/tvst.14.6.11.

DOI:10.1167/tvst.14.6.11
PMID:40465286
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12147047/
Abstract

PURPOSE

Examination of the anterior segment using retro-illumination is part of the routine clinical examination, but capturing images that can be used by the clinician remains a challenge because reflections are always present, resolution is insufficient, and the patient's glare often causes motion blur. Our aim was therefore to overcome these limitations.

METHODS

We modified a slit lamp by using a far-red LED, a high-resolution camera, and by modifying the light path using polarizers and an obturator to suppress reflections while optimizing the light flux. We used this prototype to image various ocular conditions to illustrate its potential.

RESULTS

With the ×40 magnification of the slit lamp, the prototype provided images with a resolution of 6.2 µm, and a field of view of 9.7 mm. We obtained images without motion blur in all patients. The vast majority of images were reflection-free. Only the images of a few patients with pseudophakia showed persistent small reflection. The resolution was sufficient to highlight, for example, all the Descemetic excrescences (Guttae) constitutive of a Fuchs endothelial corneal dystrophy (FECD).

CONCLUSIONS

Our retro-illumination device prototype provides high-resolution images of the different structures of the anterior segment of the eye, easy to acquire mainly because they do not dazzle patients. We believe that these images have the potential to facilitate the diagnosis and monitoring of many different diseases both in routine use and in clinical trials where they could be used as objective endpoints.

TRANSLATIONAL RELEVANCE

Our prototype revisits retro-illumination with a potential to become a new imaging device.

摘要

目的

使用后照法检查眼前节是常规临床检查的一部分,但获取可供临床医生使用的图像仍然是一项挑战,因为反射总是存在,分辨率不足,而且患者的眩光常常导致运动模糊。因此,我们的目标是克服这些限制。

方法

我们通过使用远红发光二极管、高分辨率相机,并使用偏振器和遮光器修改光路以抑制反射,同时优化光通量,对裂隙灯进行了改进。我们使用这个原型对各种眼部状况进行成像,以说明其潜力。

结果

在裂隙灯40倍放大倍数下,该原型提供的图像分辨率为6.2微米,视野为9.7毫米。我们在所有患者中都获得了没有运动模糊的图像。绝大多数图像没有反射。只有少数人工晶状体植入患者的图像显示有持续的小反射。该分辨率足以突出显示例如构成Fuchs内皮角膜营养不良(FECD)的所有Descemet膜赘生物(角膜小滴)。

结论

我们的后照法设备原型提供了高分辨率的眼前节不同结构图像,易于获取,主要是因为它们不会使患者目眩。我们相信,这些图像有潜力在常规使用和临床试验中促进对许多不同疾病的诊断和监测,在临床试验中它们可作为客观终点。

转化相关性

我们的原型重新审视了后照法,有可能成为一种新的成像设备。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/e4657897d3bd/tvst-14-6-11-f011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/51f4873f80d0/tvst-14-6-11-f001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/efeacb60ae86/tvst-14-6-11-f002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/f82a8ab8d3fc/tvst-14-6-11-f003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/b31d56a63375/tvst-14-6-11-f004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/ba1d38b08545/tvst-14-6-11-f005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/67c6ef7c2f1b/tvst-14-6-11-f006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/151055020598/tvst-14-6-11-f007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/dcc264a627f0/tvst-14-6-11-f008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/6ee9c4fa2d3e/tvst-14-6-11-f009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/5cf9347812fc/tvst-14-6-11-f010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/e4657897d3bd/tvst-14-6-11-f011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/51f4873f80d0/tvst-14-6-11-f001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/efeacb60ae86/tvst-14-6-11-f002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/f82a8ab8d3fc/tvst-14-6-11-f003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/b31d56a63375/tvst-14-6-11-f004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/ba1d38b08545/tvst-14-6-11-f005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/67c6ef7c2f1b/tvst-14-6-11-f006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/151055020598/tvst-14-6-11-f007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/dcc264a627f0/tvst-14-6-11-f008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/6ee9c4fa2d3e/tvst-14-6-11-f009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/5cf9347812fc/tvst-14-6-11-f010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4df/12147047/e4657897d3bd/tvst-14-6-11-f011.jpg

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DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity.DeepLensNet:深度学习自动诊断和白内障类型及严重程度的定量分类。
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