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Muscle Nerve. 2024 Jan;69(1):78-86. doi: 10.1002/mus.27995. Epub 2023 Nov 20.
2
Corneal nerve fiber involvement in chronic inflammatory demyelinating polyneuropathy.角膜神经纤维在慢性炎症性脱髓鞘性多发性神经病中的受累情况。
Neurol Sci. 2023 Jul;44(7):2509-2516. doi: 10.1007/s10072-023-06711-1. Epub 2023 Mar 1.
3
In Vivo Corneal Confocal Microscopy in Multiple Sclerosis: Can it Differentiate Disease Relapse in Multiple Sclerosis?多发性硬化症的活体角膜共焦显微镜检查:它能区分多发性硬化症的疾病复发吗?
Am J Ophthalmol. 2023 Jun;250:138-148. doi: 10.1016/j.ajo.2023.01.015. Epub 2023 Jan 18.
4
In vivo corneal confocal microscopy as a non-invasive test to assess obesity induced small fibre nerve damage and inflammation.体内角膜共聚焦显微镜作为一种非侵入性测试,可评估肥胖引起的小纤维神经损伤和炎症。
Eye (Lond). 2023 Aug;37(11):2226-2232. doi: 10.1038/s41433-022-02321-x. Epub 2022 Nov 28.
5
Semiautomated and Automated Quantitative Analysis of Corneal Sub-Basal Nerves in Patients With DED With Ocular Pain Using IVCM.使用IVCM对伴有眼痛的干眼患者角膜基底膜下神经进行半自动和自动定量分析。
Front Med (Lausanne). 2022 Feb 11;9:831307. doi: 10.3389/fmed.2022.831307. eCollection 2022.
6
Corneal confocal microscopy for the diagnosis of diabetic peripheral neuropathy: A systematic review and meta-analysis.角膜共焦显微镜在糖尿病周围神经病变诊断中的应用:系统评价和荟萃分析。
J Diabetes Investig. 2022 Jan;13(1):134-147. doi: 10.1111/jdi.13643. Epub 2021 Aug 27.
7
Artificial Intelligence-Based Classification of Diabetic Peripheral Neuropathy From Corneal Confocal Microscopy Images.基于人工智能的糖尿病周围神经病变角膜共聚焦显微镜图像分类
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8
Early detection of neuropathy in patients with type 2 diabetes with or without microalbuminuria in the absence of peripheral neuropathy and retinopathy.在无周围神经病变和视网膜病变的情况下,检测 2 型糖尿病合并或不合并微量白蛋白尿患者的神经病变。
J Fr Ophtalmol. 2021 Apr;44(4):485-493. doi: 10.1016/j.jfo.2020.09.027. Epub 2021 Feb 26.
9
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An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study.基于人工智能的深度学习算法利用角膜共聚焦显微镜诊断糖尿病周围神经病变:一项开发和验证研究。
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用于量化2型糖尿病患者和非2型糖尿病患者角膜神经形态的自动分析程序与手动分析程序的比较

Comparison of Automated Versus Manual Analysis Programs for Quantification of Corneal Nerve Morphology in Patients With or Without Type 2 Diabetes Mellitus.

作者信息

Aydin Fahri Onur, Ozkan Gamze, Turhan Semra Akkaya, Toker Ebru

机构信息

Department of Ophthalmology, Cam and Sakura City Hospital, Istanbul, Türkiye.

Department of Ophthalmology, Sultanbeyli State Hospital, Istanbul, Türkiye.

出版信息

Beyoglu Eye J. 2024 Dec 11;9(4):202-207. doi: 10.14744/bej.2024.92063. eCollection 2024.

DOI:10.14744/bej.2024.92063
PMID:40007679
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11849726/
Abstract

OBJECTIVES

To assess the agreement between the automated analysis program and a manual program for quantification of corneal nerve morphology.

METHODS

Twenty-seven non-diabetic controls (mean age: 48.6±5.9 years) and 60 subjects with diabetes (mean age: 52.1±6.5 years) were enrolled. Corneal nerve fiber density (CNFD), branch density (CNBD), and fiber length (CNFL) were quantified by the manual (CCMetrics software, University of Manchester, UK) and automated program (ACCMetrics software, University of Manchester, UK). Bland-Altman plots were generated to assess agreement between the two methods.

RESULTS

There were no differences in gender, age, total cholesterol, and LDL between the two groups, whereas BMI, HbA1c, and triglyceride were significantly higher and HDL was significantly lower in the T2DM group. CNFL was overestimated in the diabetic group and CNFD was underestimated in both groups with ACCMetrics (p=0.001, p<0.001, respectively). The Bland-Altman plots for both groups demonstrated good agreement for CNFL, with a wide limit of agreement (LoA) of 95% for CNFD and CNBD.

CONCLUSION

Manual and fully automated protocols for sub-basal nerve evaluation had lower agreement in the measurement of CNFD and CNBD than CNFL in healthy controls and subjects with diabetes.

摘要

目的

评估用于角膜神经形态定量分析的自动化分析程序与手动程序之间的一致性。

方法

纳入27名非糖尿病对照者(平均年龄:48.6±5.9岁)和60名糖尿病患者(平均年龄:52.1±6.5岁)。通过手动程序(英国曼彻斯特大学的CCMetrics软件)和自动化程序(英国曼彻斯特大学的ACCMetrics软件)对角膜神经纤维密度(CNFD)、分支密度(CNBD)和纤维长度(CNFL)进行定量分析。绘制Bland-Altman图以评估两种方法之间的一致性。

结果

两组在性别、年龄、总胆固醇和低密度脂蛋白方面无差异,而2型糖尿病组的体重指数、糖化血红蛋白和甘油三酯显著更高,高密度脂蛋白显著更低。在糖尿病组中,ACCMetrics高估了CNFL,在两组中均低估了CNFD(分别为p=0.001,p<0.001)。两组的Bland-Altman图显示CNFL具有良好的一致性,CNFD和CNBD的95%一致性界限较宽。

结论

在健康对照者和糖尿病患者中,用于基底膜下神经评估的手动和全自动方案在CNFD和CNBD测量方面的一致性低于CNFL。