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使用 Pentacam 通过泽尼克多项式对亚临床圆锥角膜进行整个角膜地形和断层扫描的特征。

Characteristic of entire corneal topography and tomography for the detection of sub-clinical keratoconus with Zernike polynomials using Pentacam.

机构信息

School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, Zhejiang, China.

Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, Florida, USA.

出版信息

Sci Rep. 2017 Nov 28;7(1):16486. doi: 10.1038/s41598-017-16568-y.

DOI:10.1038/s41598-017-16568-y
PMID:29184086
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5705674/
Abstract

The study aimed to characterize the entire corneal topography and tomography for the detection of sub-clinical keratoconus (KC) with a Zernike application method. Normal subjects (n = 147; 147 eyes), sub-clinical KC patients (n = 77; 77 eyes), and KC patients (n = 139; 139 eyes) were imaged with the Pentacam HR system. The entire corneal data of pachymetry and elevation of both the anterior and posterior surfaces were exported from the Pentacam HR software. Zernike polynomials fitting was used to quantify the 3D distribution of the corneal thickness and surface elevation. The root mean square (RMS) values for each order and the total high-order irregularity were calculated. Multimeric discriminant functions combined with individual indices were built using linear step discriminant analysis. Receiver operating characteristic curves determined the diagnostic accuracy (area under the curve, AUC). The 3rd-order RMS of the posterior surface (AUC: 0.928) obtained the highest discriminating capability in sub-clinical KC eyes. The multimeric function, which consisted of the Zernike fitting indices of corneal posterior elevation, showed the highest discriminant ability (AUC: 0.951). Indices generated from the elevation of posterior surface and thickness measurements over the entire cornea using the Zernike method based on the Pentacam HR system were able to identify very early KC.

摘要

本研究旨在应用泽尼克(Zernike)方法描述整个角膜地形图和断层图,以检测亚临床圆锥角膜(KC)。使用 Pentacam HR 系统对正常受试者(n=147;147 只眼)、亚临床 KC 患者(n=77;77 只眼)和 KC 患者(n=139;139 只眼)进行成像。从前表面和后表面的厚度和高度导出 Pentacam HR 软件中的整个角膜数据。使用泽尼克多项式拟合来量化角膜厚度和表面高度的 3D 分布。计算每个阶次的均方根(RMS)值和总高阶不规则性。使用线性逐步判别分析构建多元判别函数和单个指数。接收者操作特征曲线确定诊断准确性(曲线下面积,AUC)。后表面的三阶 RMS(AUC:0.928)在后临床 KC 眼中具有最高的区分能力。由角膜后表面高程的泽尼克拟合指数组成的多变量函数具有最高的判别能力(AUC:0.951)。使用基于 Pentacam HR 系统的泽尼克方法从后表面高度和整个角膜厚度测量值生成的指数能够识别非常早期的 KC。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/277f/5705674/58797f720c22/41598_2017_16568_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/277f/5705674/58797f720c22/41598_2017_16568_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/277f/5705674/58797f720c22/41598_2017_16568_Fig1_HTML.jpg

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Comparison of Corneal Deformation Parameters in Keratoconic and Normal Eyes Using a Non-contact Tonometer With a Dynamic Ultra-High-Speed Scheimpflug Camera.
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Diagnosis of forme fruste keratoconus with scheimpflug photography in Ghanaian patients.加纳患者的 Scheimpflug 摄影对不典型圆锥角膜的诊断。
BMC Ophthalmol. 2024 Jul 15;24(1):285. doi: 10.1186/s12886-024-03563-x.
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Artificial intelligence for detecting keratoconus.人工智能在圆锥角膜检测中的应用。
Cochrane Database Syst Rev. 2023 Nov 15;11(11):CD014911. doi: 10.1002/14651858.CD014911.pub2.
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