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使用双侧角膜对称性三维分析仪进行筛查。

Screening with the Bilateral Corneal Symmetry 3-D Analyzer.

作者信息

Mehravaran Shiva, Eghrari Allen, Yousefi Siamak, Khalifa Fahmi, Ghiasi Guita, Farahi Azadeh

机构信息

Department of Biology, School of Computer, Mathematical, and Natural Sciences, Morgan State University, Baltimore, MD 21251, USA.

Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.

出版信息

Int J Environ Res Public Health. 2025 May 9;22(5):747. doi: 10.3390/ijerph22050747.

DOI:10.3390/ijerph22050747
PMID:40427862
Abstract

This study aimed to evaluate the effectiveness of an innovative platform (the Bilateral Corneal Symmetry 3-D Analyzer-BiCSA) and a novel corneal symmetry index (the Volume Between Spheres-VBS) in differentiating normal corneas from those with keratoconus. Pentacam imaging data from 30 healthy corneas and 30 keratoconus cases were analyzed. BiCSA was utilized to determine the VBS for each case. Statistical analyses included comparing mean VBS values between groups and assessing sensitivity, specificity, and positive predictive values (PPVs). Keratoconus patients exhibited significantly higher VBS scores compared to healthy controls, particularly within the central 4.0 mm zone (11.4 versus 6.3). Using a VBS threshold of 11.3 in the central zone identified 40% of keratoconus cases (40% sensitivity), but 100% of cases surpassing the threshold were keratoconus (100% PPV). Lowering the threshold to 10.4 increased case detection to 90% while maintaining a high PPV (84.2%). These findings suggest that VBS, particularly when focused on the central 4.0 mm zone, can be a valuable tool for early keratoconus screening and identifying potential corneal abnormalities requiring further clinical evaluation. No healthy control corneas in this study exceeded a VBS threshold of 11.4 at 4 mm, indicating that values above this warrant further investigation.

摘要

本研究旨在评估一种创新平台(双侧角膜对称性三维分析仪-BiCSA)和一种新型角膜对称性指数(球间体积-VBS)在区分正常角膜与圆锥角膜方面的有效性。分析了30例健康角膜和30例圆锥角膜病例的Pentacam成像数据。利用BiCSA确定每个病例的VBS。统计分析包括比较组间平均VBS值以及评估敏感性、特异性和阳性预测值(PPV)。与健康对照组相比,圆锥角膜患者的VBS评分显著更高,尤其是在中央4.0毫米区域内(11.4对6.3)。在中央区域使用11.3的VBS阈值可识别出40%的圆锥角膜病例(敏感性40%),但超过该阈值的病例100%为圆锥角膜(PPV 100%)。将阈值降低至10.4可将病例检出率提高至90%,同时保持较高的PPV(84.2%)。这些发现表明,VBS,尤其是聚焦于中央4.0毫米区域时,可成为圆锥角膜早期筛查以及识别需要进一步临床评估的潜在角膜异常的有价值工具。本研究中没有健康对照角膜在4毫米处超过11.4的VBS阈值,这表明高于该值的情况需要进一步调查。

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本文引用的文献

1
Keratoconus Detection-based on Dynamic Corneal Deformation Videos Using Deep Learning.基于深度学习的动态角膜变形视频的圆锥角膜检测
Ophthalmol Sci. 2023 Aug 11;4(2):100380. doi: 10.1016/j.xops.2023.100380. eCollection 2024 Mar-Apr.
2
Interocular Symmetry Analysis of Corneal Elevation Using the Fellow Eye as the Reference Surface and Machine Learning.以对侧眼为参考面并运用机器学习的角膜高度眼间对称性分析
Healthcare (Basel). 2021 Dec 16;9(12):1738. doi: 10.3390/healthcare9121738.
3
Keratoconus Screening Based on Deep Learning Approach of Corneal Topography.
基于角膜地形深度学习方法的圆锥角膜筛查。
Transl Vis Sci Technol. 2020 Sep 25;9(2):53. doi: 10.1167/tvst.9.2.53. eCollection 2020 Sep.
4
Corneal Topography Raw Data Classification Using a Convolutional Neural Network.基于卷积神经网络的角膜地形原始数据分类。
Am J Ophthalmol. 2020 Nov;219:33-39. doi: 10.1016/j.ajo.2020.06.005. Epub 2020 Jun 10.
5
Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study.基于眼前节光学相干断层扫描的彩色编码图的深度学习在圆锥角膜检测中的应用:一项诊断准确性研究。
BMJ Open. 2019 Sep 27;9(9):e031313. doi: 10.1136/bmjopen-2019-031313.
6
Salzmann nodular degeneration: prevalence, impact, and management strategies.萨尔茨曼结节性变性:患病率、影响及管理策略。
Clin Ophthalmol. 2019 Jul 25;13:1305-1314. doi: 10.2147/OPTH.S166280. eCollection 2019.
7
KeratoDetect: Keratoconus Detection Algorithm Using Convolutional Neural Networks.KeratoDetect:基于卷积神经网络的圆锥角膜检测算法。
Comput Intell Neurosci. 2019 Jan 23;2019:8162567. doi: 10.1155/2019/8162567. eCollection 2019.
8
Keratoconus severity identification using unsupervised machine learning.使用无监督机器学习识别圆锥角膜严重程度。
PLoS One. 2018 Nov 6;13(11):e0205998. doi: 10.1371/journal.pone.0205998. eCollection 2018.
9
Intereye asymmetry in bilateral keratoconus, keratoconus suspect and normal eyes and its relationship with disease severity.双眼圆锥角膜、疑似圆锥角膜及正常眼的眼间不对称性及其与疾病严重程度的关系。
Br J Ophthalmol. 2017 Nov;101(11):1475-1482. doi: 10.1136/bjophthalmol-2016-309841. Epub 2017 Apr 21.
10
Detection of Keratoconus With a New Biomechanical Index.利用一种新的生物力学指标检测圆锥角膜
J Refract Surg. 2016 Dec 1;32(12):803-810. doi: 10.3928/1081597X-20160629-01.