Novel Machine-Learning Based Framework Using Electroretinography Data for the Detection of Early-Stage Glaucoma.
Novel Machine-Learning Based Framework Using Electroretinography Data for the Detection of Early-Stage Glaucoma.
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DOI：
10.3389/fnins.2022.869137
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发表时间：
2022
影响因子：
4.3
通讯作者：
Mehdizadeh, Amirfarhang
中科院分区：
文献类型：
--
作者：
Gajendran, Mohan Kumar;Rohowetz, Landon J.;Koulen, Peter;Mehdizadeh, Amirfarhang
青光眼的早期诊断一直是眼科学的难题。目前最先进的青光眼诊断技术并没有完全利用功能性测量，如视网膜电图的巨大潜力;相反，重点是结构性测量，如光学相干断层扫描。目前的研究旨在为开发一种新型可靠的预测框架迈出基础性的一步，该框架使用基于机器学习的算法，能够利用ERG信号包含的医学相关信息来早期检测青光眼。将来自DBA/2小鼠的60只眼睛的ERG信号分组用于基于年龄的二元分类。还基于眼内压（IOP）对信号进行分组以用于多类分类。统计和小波为基础的功能的工程和提取。确定了重要的预测因素（ERG测试和特征），并评估了五种基于机器学习的方法的性能。随机森林（袋装树）集成分类器提供了最好的性能，在二进制和多类分类的ERG信号。二进制和多类分类的准确率分别为91.7%和80%，这表明基于机器学习的模型可以检测ERG信号的细微变化，如果使用基于小波分析的高级特征进行训练的话。本研究描述了一种新的基于机器学习的方法来分析ERG信号，提供可用于检测早期青光眼的额外信息。基于使用所提出的基于机器学习的框架利用已建立的ERG数据集获得的有希望的性能指标，我们得出结论，该新框架允许检测小鼠中青光眼早期/各个阶段的功能缺陷。
Early-stage glaucoma diagnosis has been a challenging problem in ophthalmology. The current state-of-the-art glaucoma diagnosis techniques do not completely leverage the functional measures' such as electroretinogram's immense potential; instead, focus is on structural measures like optical coherence tomography. The current study aims to take a foundational step toward the development of a novel and reliable predictive framework for early detection of glaucoma using machine-learning-based algorithm capable of leveraging medically relevant information that ERG signals contain. ERG signals from 60 eyes of DBA/2 mice were grouped for binary classification based on age. The signals were also grouped based on intraocular pressure (IOP) for multiclass classification. Statistical and wavelet-based features were engineered and extracted. Important predictors (ERG tests and features) were determined, and the performance of five machine learning-based methods were evaluated. Random forest (bagged trees) ensemble classifier provided the best performance in both binary and multiclass classification of ERG signals. An accuracy of 91.7 and 80% was achieved for binary and multiclass classification, respectively, suggesting that machine-learning-based models can detect subtle changes in ERG signals if trained using advanced features such as those based on wavelet analyses. The present study describes a novel, machine-learning-based method to analyze ERG signals providing additional information that may be used to detect early-stage glaucoma. Based on promising performance metrics obtained using the proposed machine-learning-based framework leveraging an established ERG data set, we conclude that the novel framework allows for detection of functional deficits of early/various stages of glaucoma in mice.
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影响因子：
1.8
作者：
Aldebasi, YH;Drasdo, N;North, RV
通讯作者：
North, RV
影响因子：
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作者：
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影响因子：
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作者：
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DOI：
10.1016/j.ejmp.2013.03.006
发表时间：
2014-02-01
影响因子：
3.4
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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