Caixinha Miguel, Nunes Sandrina
a Department of Physics, Faculty of Sciences and Technology , University of Coimbra , Coimbra , Portugal.
b Department of Electrical and Computer Engineering, Faculty of Sciences and Technology , University of Coimbra , Coimbra , Portugal.
Curr Eye Res. 2017 Jan;42(1):1-15. doi: 10.1080/02713683.2016.1175019. Epub 2016 Jun 30.
This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration, and diabetic retinopathy, these ocular pathologies being the major causes of irreversible visual impairment.
本综述介绍并讨论了机器学习技术在临床视觉科学背景下对疾病诊断和监测的贡献。许多导致失明的眼部疾病如果在最早阶段被检测和治疗，是可以被阻止或延缓的。随着诊断设备、成像技术和基因组学的最新发展，现在有了用于早期疾病检测和患者管理的新数据来源。机器学习技术作为临床决策支持技术出现在生物医学领域，以提高疾病检测和监测的敏感性和特异性，客观上改善临床决策过程。本文献对基于机器学习方法的多模式眼部疾病诊断和监测进行了综述。在第一部分，将介绍与不同机器学习方法相关的技术问题。机器学习技术用于在给定数据集中自动识别复杂模式。当每个病例都有组标签时，这些技术可以创建同类组（无监督学习），或者创建一个预测新病例组成员身份的分类器（监督学习）。为确保机器学习技术在给定数据集中具有良好性能，应消除或最小化所有可能的偏差来源。为此，应确认输入数据集对真实总体的代表性，去除噪声，处理缺失数据，并调整数据维度（即数据集中参数/特征的数量和病例数量）。机器学习技术在眼部疾病诊断和监测中的应用将在本文的第二部分进行介绍和讨论。为展示机器学习在临床视觉科学中的临床益处，将在青光眼、年龄相关性黄斑变性和糖尿病视网膜病变中给出几个例子，这些眼部疾病是不可逆视力损害的主要原因。
