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深度学习辅助的视神经全自动比色分析及其与视野检查和光学相干断层扫描在青光眼研究中的关联

Fully Automated Colorimetric Analysis of the Optic Nerve Aided by Deep Learning and Its Association with Perimetry and OCT for the Study of Glaucoma.

作者信息

Gonzalez-Hernandez Marta, Gonzalez-Hernandez Daniel, Perez-Barbudo Daniel, Rodriguez-Esteve Paloma, Betancor-Caro Nisamar, Gonzalez de la Rosa Manuel

机构信息

INSOFT S.L., 25 de Julio, 34, 38004 Santa Cruz de Tenerife, Spain.

Ophthalmology Department, Hospital Universitario de Canarias, Carretera Ofra s/n, 38320 San Cristobal de La Laguna, Spain.

出版信息

J Clin Med. 2021 Jul 22;10(15):3231. doi: 10.3390/jcm10153231.

DOI:10.3390/jcm10153231
PMID:34362014
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8347493/
Abstract

BACKGROUND

Laguna-ONhE is an application for the colorimetric analysis of optic nerve images, which topographically assesses the cup and the presence of haemoglobin. Its latest version has been fully automated with five deep learning models. In this paper, perimetry in combination with Laguna-ONhE or Cirrus-OCT was evaluated.

METHODS

The morphology and perfusion estimated by Laguna ONhE were compiled into a "Globin Distribution Function" (GDF). Visual field irregularity was measured with the usual pattern standard deviation (PSD) and the threshold coefficient of variation (TCV), which analyses its harmony without taking into account age-corrected values. In total, 477 normal eyes, 235 confirmed, and 98 suspected glaucoma cases were examined with Cirrus-OCT and different fundus cameras and perimeters.

RESULTS

The best Receiver Operating Characteristic (ROC) analysis results for confirmed and suspected glaucoma were obtained with the combination of GDF and TCV (AUC: 0.995 and 0.935, respectively. Sensitivities: 94.5% and 45.9%, respectively, for 99% specificity). The best combination of OCT and perimetry was obtained with the vertical cup/disc ratio and PSD (AUC: 0.988 and 0.847, respectively. Sensitivities: 84.7% and 18.4%, respectively, for 99% specificity).

CONCLUSION

Using Laguna ONhE, morphology, perfusion, and function can be mutually enhanced with the methods described for the purpose of glaucoma assessment, providing early sensitivity.

摘要

背景

Laguna-ONhE是一款用于视神经图像比色分析的应用程序，可对视杯和血红蛋白的存在进行地形学评估。其最新版本已通过五个深度学习模型实现了完全自动化。本文评估了视野检查与Laguna-ONhE或Cirrus-OCT相结合的情况。

方法

Laguna ONhE估计的形态和灌注被编制成一个“球蛋白分布函数”（GDF）。视野不规则性通过常规模式标准差（PSD）和阈值变异系数（TCV）进行测量，后者在不考虑年龄校正值的情况下分析其协调性。总共对477只正常眼睛、235例确诊青光眼病例和98例疑似青光眼病例进行了Cirrus-OCT、不同眼底相机和视野计检查。

结果

GDF和TCV相结合在确诊和疑似青光眼病例中获得了最佳的受试者操作特征（ROC）分析结果（AUC分别为0.995和0.935。在99%特异性时，敏感度分别为94.5%和45.9%）。OCT和视野检查的最佳组合是垂直杯盘比和PSD（AUC分别为0.988和0.847。在99%特异性时，敏感度分别为84.7%和18.4%）。

结论

使用Laguna ONhE，形态学、灌注和功能可以通过所述方法相互增强，以用于青光眼评估，从而提供早期敏感性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b84a/8347493/1d600729dd74/jcm-10-03231-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b84a/8347493/0a7faab26cdb/jcm-10-03231-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b84a/8347493/e22b4d147773/jcm-10-03231-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b84a/8347493/9cab3282f199/jcm-10-03231-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b84a/8347493/a7f923b3e24b/jcm-10-03231-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b84a/8347493/b5df8252d94d/jcm-10-03231-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b84a/8347493/1d600729dd74/jcm-10-03231-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b84a/8347493/0a7faab26cdb/jcm-10-03231-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b84a/8347493/e22b4d147773/jcm-10-03231-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b84a/8347493/9cab3282f199/jcm-10-03231-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b84a/8347493/a7f923b3e24b/jcm-10-03231-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b84a/8347493/b5df8252d94d/jcm-10-03231-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b84a/8347493/1d600729dd74/jcm-10-03231-g004.jpg

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