The Influence of Parental Myopia on Children’s Myopia in Different Generations of Parent-Offspring Pairs in South Korea

<p><i>Purpose</i>: To compare the heritabilities of myopia and high myopia across three different generations in Korea. <i>Methods</i>: Parent-offspring pairs of different age groups were included: two parents and their offspring aged 10–19 (“young families”), two parents and their offspring aged 20–29 (“middle-aged families”), and two parents and their offspring aged 30–45 (“older families”) were selected from the 2008–2012 Korea National Health and Nutrition Examination Survey. Variance component methods were used to obtain the heritability estimates for myopia and high myopia using parent-offspring pairs from three generations. Spherical equivalents measured in the right eyes were used. <i>Results</i>: From the 2008–2012 data, 2,716, 1,211, and 477 offspring from 1,807 young, 956 middle-aged, and 434 older families were eligible for the study, respectively. For myopia, the additive genetic portion of phenotypic variance was smaller in the younger families (74.7% in the older families, 48.1% in the middle-aged families, and 40.1% in the young families), and the non-shared environmental portion was greater in the younger families (12.4% in older families, 24.9% in middle-aged families, and 46.5% in young families). In contrast, for high myopia, the additive genetic portion remained roughly constant at approximately 60% in all three generations. <i>Conclusions</i>: With myopia, the environmental portion of the phenotypic variance increased and the additive genetic portion decreased as South Korea became more urbanized. With high myopia, the additive genetic portion remained roughly constant at approximately 60%, despite the urbanization.</p

Incidence of Pterygium per 1,000 Person-years in South Korea.

<p>Incidence of Pterygium per 1,000 Person-years in South Korea.</p

Prevalence (%) of Pterygium Per 100 Persons in South Korea.

<p>Prevalence (%) of Pterygium Per 100 Persons in South Korea.</p

Sociodemographic Factors Associated with Pterygium and Surgically Removed Pterygium based on Multivariable Cox Regression (n = 1,116,364).

<p>Sociodemographic Factors Associated with Pterygium and Surgically Removed Pterygium based on Multivariable Cox Regression (n = 1,116,364).</p

Incidence and Prevalence of Clinically Diagnosed Pterygium in South Korea.

<p>(A) Incidences of pterygium (black dot and lines) and surgically removed pterygium (gray dot and lines) per 1,000 person-years according to year. (B) Incidences per 1,000 person-years according to age group and (C) prevalence (%) according to year.</p

Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database

<div><p>Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture. The classification results depended greatly on the number of categories. As the number of categories increased, the performance of deep learning models was diminished. When all 10 categories were included, we obtained results with an accuracy of 30.5%, relative classifier information (RCI) of 0.052, and Cohen’s kappa of 0.224. Considering three integrated normal, background diabetic retinopathy, and dry age-related macular degeneration, the multi-categorical classifier showed accuracy of 72.8%, 0.283 RCI, and 0.577 kappa. In addition, several ensemble classifiers enhanced the multi-categorical classification performance. The transfer learning incorporated with ensemble classifier of clustering and voting approach presented the best performance with accuracy of 36.7%, 0.053 RCI, and 0.225 kappa in the 10 retinal diseases classification problem. First, due to the small size of datasets, the deep learning techniques in this study were ineffective to be applied in clinics where numerous patients suffering from various types of retinal disorders visit for diagnosis and treatment. Second, we found that the transfer learning incorporated with ensemble classifiers can improve the classification performance in order to detect multi-categorical retinal diseases. Further studies should confirm the effectiveness of algorithms with large datasets obtained from hospitals.</p></div

Performance results by using classic machine learning and ensemble classification for multi-categorical 10 retinal diseases classification problem in the VGG-19 transfer learning setting.

<p>Performance results by using classic machine learning and ensemble classification for multi-categorical 10 retinal diseases classification problem in the VGG-19 transfer learning setting.</p

Receiver operating characteristic (ROC) curves of transfer learning with random forest based on VGG-19 structure (VGG19-TL-RF), transfer learning with random forest based on VGG-19 structure (VGG19-TL-SVM), and VGG-19, and AlexNet in predicting normal retina or retinal disease status using fundus photographs.

<p>We divided all data set (10,000 images) into training dataset (70%) and test dataset (30%). Retinal disease status includes diabetic retinopathy, age-related macular degeneration, retinal vein occlusion, retinal artery occlusion, hypertensive retinopathy, Coat’s disease, and retinitis.</p

Performance of deep learning methods with 5-fold cross validation according to the number of categories.

<p>(A) the performance plot of accuracy (B) the performance plot of relative classifier information (C) the performance plot of Kappa. AMD, age-related macular degeneration; BDR, background diabetic retinopathy; PDR, proliferative diabetic retinopathy; RVO, retinal vein occlusion; RAO, retinal artery occlusion; VGG19-TL-RF, transfer learning with random forest based on VGG-19 structure; VGG19-TL-SVM, transfer learning with one-vs-one support vector machine based on VGG-19 structure.</p

Results from multi-categorical deep learning models for different approaches combining fundus images of normal, diabetic retinopathy and age-related macular degeneration.

<p>Results from multi-categorical deep learning models for different approaches combining fundus images of normal, diabetic retinopathy and age-related macular degeneration.</p