The purpose of this study was to determine the ocular, sex- and age-specific, anthropometric, and hematologic factors that affect the implicit times and amplitudes of the flicker ERGs recorded with the RETeval system from individuals 40- to 89-years-of-age. Flicker ERGs were recorded with the RETeval system from 330 individuals who had normal fundus and OCT images. Univariate and multivariate regression analyses were performed to identify factors associated with the implicit times and amplitudes of the RETeval flicker ERGs. Univariate regression analyses showed significant correlations between the implicit times and the BCVA, age, axial length, blood sugar level, and BUN in both eyes. Multivariate regression analyses identified age and axial length as two independent factors that were significantly correlated with the implicit times of the RETeval flicker ERGs. Univariate regression analyses also showed significant correlations between the amplitudes and age, platelet count, HDL level, and creatinine level in both eyes. However, smoking habits, body mass index, and blood pressure were not correlated with the RETeval flicker ERGs. We conclude that age and some ophthalmologic and hematologic findings except for anthropometric findings were suggested to significantly affect the measurements of the RETeval flicker ERGs.
Eye checkups have become increasingly important to maintain good vision and quality of life. As the population requiring eye checkups increases, so does the clinical work burden of clinicians. An automatic screening algorithm to reduce the clinicians’ workload is necessary. Machine learning (ML) has recently become one of the chief techniques for automated image recognition and is a helpful tool for identifying ocular diseases. However, the accuracy of ML models is lower in a clinical setting than in the laboratory. The performance of ML models depends on the training dataset. Eye checkups often prioritize speed and minimize image processing. Data distribution differs from the training dataset and, consequently, decreases prediction performance. The study aim was to investigate an ML model to screen for retinal diseases from low-quality optical coherence tomography (OCT) images captured during actual eye chechups to prevent a dataset shift. The ensemble model with convolutional neural networks (CNNs) and random forest models showed high screening performance in the single-shot OCT images captured during the actual eye checkups. Our study indicates the strong potential of the ensemble model combining the CNN and random forest models in accurately predicting abnormalities during eye checkups.
The average life expectancy has increased globally, and the risk of visual impairment is expected to increase as well. Therefore, eye checkups have become increasingly important to maintain good vision and quality of life. As the population requiring eye checkups increases, so does the clinical work burden of physicians. Hence, an automatic discrimination algorithm to reduce the clinicians' workload is necessary. The convolutional neural network (CNN), a deep learning algorithm, has recently become one of the chief techniques for automated image recognition and is a helpful tool for identifying ocular diseases. However, the accuracy of a single CNN model ranges within 70%–95%, and it may fail to identify some diseases. In this study, we aimed to compare the diagnostic performances of four CNN models trained with optical coherence tomography (OCT) images, the machine-learning (ML) model trained with data on the retinal and choroidal area, and the ensemble model that integrated the CNNs and ML models using OCT images obtained during eye checkups. Our results show that the ensemble model had a superior diagnostic performance over the CNN and ML models. The ML model, which evaluated diseases using data regarding the temporal peripheral retinal area, improved on the CNN model, which misrecognized the temporal peripheral retinal structures. Our study indicates the strong potential of the ensemble model combining the CNN and ML models in accurately predicting abnormalities during eye checkups.
Purpose: To assess the macular function by focal macular electroretinography and static perimetry in eyes with retinitis pigmentosa.Methods: Eighty-eight eyes of 88 retinitis pigmentosa patients were analyzed. The relationships between the focal macular electroretinography components and the mean deviations (MDs) of the Humphrey Field Analyzer 10-2 were determined. Spectral-domain optical coherence tomography was used to determine the integrity of the ellipsoid zone (EZ) and the interdigitation zone.Results: Forward-backward stepwise regression analyses showed that the amplitudes (r = 0.45, P , 0.01) and implicit times (r = 20.29, P , 0.01) of the b-waves were significantly correlated with the MDs. Some of the eyes had reduced b-wave amplitudes (,1.0 mV) and disrupted interdigitation zone, despite having a better MD ($ 210.0 dB) and intact EZ. Subgroup analyses of eyes with better MD ($ 210.0 dB) showed that the EZ width was correlated with the MDs but not with the b-wave amplitude. The thickness of the EZ-retinal pigment epithelium as an alternative indicator of interdigitation zone was correlated with the b-wave amplitude (r = 0.32, P = 0.04) but not with the MDs (r = 20.10, P = 0.53). Conclusion:The fact that the focal macular electroretinography amplitudes are reduced before the shortening of the EZ in the early stage of retinitis pigmentosa indicates that the focal macular electroretinography amplitudes are an earlier indicator of macular dysfunction than the Humphrey Field Analyzer 10-2 findings.
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