角膜基质的生化成分:基于拉曼光谱和深度学习方法的近视分类研究。

IF 2.9 2区 医学 Q2 BIOCHEMICAL RESEARCH METHODS
Biomedical optics express Pub Date : 2024-12-03 eCollection Date: 2025-01-01 DOI:10.1364/BOE.539721
Zhe Yu, Yong Li, Tingyan Xing, Ming Han, Yaohua Zhang, Jinrong Gao, Jing Du, Jing Li, Qi Zeng, Xueli Chen
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引用次数: 0

摘要

本研究旨在利用拉曼光谱特征来确定不同程度近视的角膜基质晶状体的生化组成差异。选取38例行小切口晶状体摘除(SMILE)手术的角膜基质晶状体样本,分为低度(n = 9,球度数≧-3.00D)、中度(n = 23,球度数-6.00D)和高度近视(n = 6,球度数≦-6.00D)组。采用定制的显微共聚焦拉曼系统(MCRS)采集拉曼光谱,对采集到的拉曼光谱进行平滑、去噪、基线校准等处理,以细化原始数据。采用独立样本t检验分析各样本类型间的光谱特征峰。低、高度近视样本间差异显著(P -1、937 cm-1、1002 cm-1、1243 cm-1、1448 cm-1和2940 cm-1),尤其是在2940 cm-1处。在轻度和中度、中度和高度近视样本之间也发现了差异,尽管在轻度和高度近视样本之间的差异较小。在PLS-KNN训练下,三分类模型的判别准确率达到95%。同样,与PCA-KNN(93.3%)相比,PLS-KNN对低度和高度近视的两分类模型(94.4%)达到了更高的准确率。PLS降维略优于PCA,提高了分类精度。此外,在两种约简方法中,KNN算法显示出最高的精度和性能。最优PLS-KNN分类模型显示,低、中、高度近视对应的ROC曲线AUC分别为0.99、0.98、1.00。分类正确率分别为89.7%和96.9%,低度和高度近视分别为100%。双分类模型的准确率达到94.4%,AUC为0.98,在区分高低近视角膜基质方面表现较好。我们发现不同程度近视的角膜基质晶状体中胶原蛋白、脂质和核酸等生化差异显著,这表明拉曼光谱在阐明近视发病机制方面具有很大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Biochemical components of corneal stroma: a study on myopia classification based on Raman spectroscopy and deep learning methods.

The study aimed to identify differences in the biochemical composition of corneal stroma lenses across varying degrees of myopia using Raman spectrum characteristics. Corneal stroma lens samples from 38 patients who underwent small incision lens extraction (SMILE) surgery, were categorized into low (n = 9, spherical power -3.00D), moderate (n = 23, spherical power < -3.00D and > -6.00D), and high myopia (n = 6, spherical power ≦-6.00D) groups. A custom-built microscopic confocal Raman system (MCRS) was used to collect Raman spectra, which were processed by smoothing, denoising, and baseline calibrating to refine raw data. Independent sample t-tests were used to analyze spectral feature peaks among sample types. Significant differences (P < 0.001) were found in multiple Raman spectral characteristic peaks (854 cm-1, 937 cm-1, 1002 cm-1, 1243 cm-1, 1448 cm-1, and 2940 cm-1) between low and high myopia samples, particularly at 2940 cm-1. Differences were also found between low and moderate, and moderate and high myopia samples, although fewer than between low and high myopia samples. The three-classification model, particularly with PLS-KNN training, exhibited superior discriminative performance with accuracy rates of 95%. Similarly, the two-classification model for low and high myopia achieved high accuracy with PLS-KNN (94.4%) compared to PCA-KNN (93.3%). PLS dimensionality reduction slightly outperformed PCA, enhancing classification accuracy. In addition, in both reduction methods, the KNN algorithm demonstrated the highest accuracy and performance. The optimal PLS-KNN classification model showed AUC values of 0.99, 0.98, and 1.00 for ROC curves corresponding to low, moderate, and high myopia, respectively. Classification accuracy rates were 89.7% and 96.9%, and 100% for low and high myopia, respectively. For the two-classification model, accuracy reached 94.4% with an AUC of 0.98, indicating strong performance in distinguishing between high and low myopic corneal stroma. We found significant biochemical differences such as collagen, lipids, and nucleic acids in corneal stroma lenses across varying degrees of myopia, suggesting that Raman spectroscopy holds substantial potential in elucidating the pathogenesis of myopia.

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来源期刊
Biomedical optics express
Biomedical optics express BIOCHEMICAL RESEARCH METHODS-OPTICS
CiteScore
6.80
自引率
11.80%
发文量
633
审稿时长
1 months
期刊介绍: The journal''s scope encompasses fundamental research, technology development, biomedical studies and clinical applications. BOEx focuses on the leading edge topics in the field, including: Tissue optics and spectroscopy Novel microscopies Optical coherence tomography Diffuse and fluorescence tomography Photoacoustic and multimodal imaging Molecular imaging and therapies Nanophotonic biosensing Optical biophysics/photobiology Microfluidic optical devices Vision research.
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