Bekollari Marsida, Dettoraki Maria, Stavrou Valentina, Glotsos Dimitris, Liaparinos Panagiotis
Department of Biomedical Engineering, University of West Attica, Ag. Spyridonos, 12243 Athens, Greece.
Department of Ophthalmology, "Elpis" General Hospital, 11522 Athens, Greece.
Diagnostics (Basel). 2024 Feb 6;14(4):349. doi: 10.3390/diagnostics14040349.
Glaucoma is a chronic, progressive eye disease affecting the optic nerve, which may cause visual damage and blindness. In this study, we present a machine-learning investigation to classify patients with glaucoma (case group) with respect to normal participants (control group). We examined 172 eyes at the Ophthalmology Clinic of the "Elpis" General Hospital of Athens between October 2022 and September 2023. In addition, we investigated the glaucoma classification in terms of the following: (a) eye selection and (b) gender. Our methodology was based on the features extracted via two diagnostic optical systems: (i) conventional optical coherence tomography (OCT) and (ii) a modern RETeval portable device. The machine-learning approach comprised three different classifiers: the Bayesian, the Probabilistic Neural Network (PNN), and Support Vectors Machines (SVMs). For all cases examined, classification accuracy was found to be significantly higher when using the RETeval device with respect to the OCT system, as follows: 14.7% for all participants, 13.4% and 29.3% for eye selection (right and left, respectively), and 25.6% and 22.6% for gender (male and female, respectively). The most efficient classifier was found to be the SVM compared to the PNN and Bayesian classifiers. In summary, all aforementioned comparisons demonstrate that the RETeval device has the advantage over the OCT system for the classification of glaucoma patients by using the machine-learning approach.
青光眼是一种影响视神经的慢性进行性眼病，可能导致视力损害和失明。在本研究中，我们进行了一项机器学习调查，以对青光眼患者（病例组）与正常参与者（对照组）进行分类。我们于2022年10月至2023年9月期间在雅典“埃尔皮斯”综合医院眼科诊所检查了172只眼睛。此外，我们还从以下方面对青光眼分类进行了研究：(a)眼睛选择和(b)性别。我们的方法基于通过两种诊断光学系统提取的特征：(i)传统光学相干断层扫描（OCT）和(ii)现代RETeval便携式设备。机器学习方法包括三种不同的分类器：贝叶斯分类器、概率神经网络（PNN）和支持向量机（SVM）。对于所有检查的病例，发现使用RETeval设备时的分类准确率相对于OCT系统显著更高，具体如下：所有参与者为14.7%，眼睛选择（分别为右眼和左眼）为13.4%和29.3%，性别（分别为男性和女性）为25.6%和22.6%。与PNN和贝叶斯分类器相比，发现最有效的分类器是SVM。总之，所有上述比较表明，通过机器学习方法，RETeval设备在青光眼患者分类方面优于OCT系统。
