scholarly journals GWAS-based Machine Learning for Prediction of Age-Related Macular Degeneration Risk

2019 ◽  
Author(s):  
Qi Yan ◽  
Yale Jiang ◽  
Heng Huang ◽  
Anand Swaroop ◽  
Emily Y. Chew ◽  
...  
Keyword(s):  
Machine Learning ◽  
Macular Degeneration ◽  
Genetic Information ◽  
Prediction Models ◽  
Age Related Macular Degeneration ◽  
Accurate Estimation ◽  
Learning Approaches ◽  
Age Related ◽  
Genome Wide ◽  
Normal Controls

ABSTRACTNumerous independent susceptibility variants have been identified for Age-related macular degeneration (AMD) by genome-wide association studies (GWAS). Since advanced AMD is currently incurable, an accurate prediction of a person’s AMD risk using genetic information is desirable for early diagnosis and clinical management. In this study, genotype data of 32,215 Caucasian individuals with age above 50 years from the International AMD Genomics Consortium in dbGAP were used to establish and validate prediction models for AMD risk using four different machine learning approaches: neural network, lasso regression, support vector machine, and random forest. A standard logistic regression model was also considered using a genetic risk score. To identify feature SNPs for AMD prediction models, we selected the genome-wide significant SNPs from GWAS. All methods achieved good performance for predicting normal controls versus advanced AMD cases (AUC=0.81∼0.82 in a separate test dataset) and normal controls versus any AMD (AUC=0.78∼0.79). By applying the state-of-art machine learning approaches on the large AMD GWAS data, the predictive models we established can provide an accurate estimation of an individual’s AMD risk profile across the person’s lifespan based on a comprehensive genetic information.

Assessment of CFH and HTRA1 polymorphisms in age-related macular degeneration using classic and machine-learning approaches

2020 ◽  
Vol 41 (6) ◽  
pp. 539-547
Author(s):  
Antonieta Martínez-Velasco ◽  
Andric C. Perez-Ortiz ◽  
Bani Antonio-Aguirre ◽  
Lourdes Martínez-Villaseñor ◽  
Esmeralda Lira-Romero ◽  
...  
Keyword(s):  
Machine Learning ◽  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Learning Approaches ◽  
Age Related

scholarly journals Optimized Prediction Models from Fundus Imaging and Genetics for Late Age-Related Macular Degeneration

2021 ◽  
Vol 11 (11) ◽  
pp. 1127
Author(s):  
Arun Govindaiah ◽  
Abdul Baten ◽  
R. Theodore Smith ◽  
Siva Balasubramanian ◽  
Alauddin Bhuiyan
Keyword(s):  
Machine Learning ◽  
Macular Degeneration ◽  
Retinal Image ◽  
Prediction Models ◽  
Retinal Imaging ◽  
Image Data ◽  
Age Related Macular Degeneration ◽  
Learning Models ◽  
Age Related ◽  
Machine Learning Models

Age-related macular degeneration (AMD) is a leading cause of blindness in the developed world. In this study, we compare the performance of retinal fundus images and genetic-information-based machine learning models for the prediction of late AMD. Using data from the Age-related Eye Disease Study, we built machine learning models with various combinations of genetic, socio-demographic/clinical, and retinal image data to predict late AMD using its severity and category in a single visit, in 2, 5, and 10 years. We compared their performance in sensitivity, specificity, accuracy, and unweighted kappa. The 2-year model based on retinal image and socio-demographic (S-D) parameters achieved a sensitivity of 91.34%, specificity of 84.49% while the same for genetic and S-D-parameters-based model was 79.79% and 66.84%. For the 5-year model, the retinal image and S-D-parameters-based model also outperformed the genetic and S-D parameters-based model. The two 10-year models achieved similar sensitivities of 74.24% and 75.79%, respectively, but the retinal image and S-D-parameters-based model was otherwise superior. The retinal-image-based models were not further improved by adding genetic data. Retinal imaging and S-D data can build an excellent machine learning predictor of developing late AMD over 2–5 years; the retinal imaging model appears to be the preferred prognostic tool for efficient patient management.

scholarly journals Imaging and artificial intelligence for progression of age-related macular degeneration

2021 ◽  
pp. 153537022110315
Author(s):  
Kathleen Romond ◽  
Minhaj Alam ◽  
Sasha Kravets ◽  
Luis de Sisternes ◽  
Theodore Leng ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Macular Degeneration ◽  
Vision Loss ◽  
External Validation ◽  
Geographic Atrophy ◽  
Age Related Macular Degeneration ◽  
Learning Approaches ◽  
Imaging Data ◽  
Age Related

Age-related macular degeneration (AMD) is a leading cause of severe vision loss. With our aging population, it may affect 288 million people globally by the year 2040. AMD progresses from an early and intermediate dry form to an advanced one, which manifests as choroidal neovascularization and geographic atrophy. Conversion to AMD-related exudation is known as progression to neovascular AMD, and presence of geographic atrophy is known as progression to advanced dry AMD. AMD progression predictions could enable timely monitoring, earlier detection and treatment, improving vision outcomes. Machine learning approaches, a subset of artificial intelligence applications, applied on imaging data are showing promising results in predicting progression. Extracted biomarkers, specifically from optical coherence tomography scans, are informative in predicting progression events. The purpose of this mini review is to provide an overview about current machine learning applications in artificial intelligence for predicting AMD progression, and describe the various methods, data-input types, and imaging modalities used to identify high-risk patients. With advances in computational capabilities, artificial intelligence applications are likely to transform patient care and management in AMD. External validation studies that improve generalizability to populations and devices, as well as evaluating systems in real-world clinical settings are needed to improve the clinical translations of artificial intelligence AMD applications.

scholarly journals Clustering of eyes with age-related macular degeneration or pachychoroid spectrum diseases based on choroidal thickness profile

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Young Ho Kim ◽  
Boram Lee ◽  
Edward Kang ◽  
Jaeryung Oh
Keyword(s):  
Macular Degeneration ◽  
Choroidal Thickness ◽  
Age Related Macular Degeneration ◽  
Age Related ◽  
Soft Drusen ◽  
Pachychoroid Neovasculopathy ◽  
The Common ◽  
Common Pathogenesis ◽  
Clustering Pattern ◽  
Normal Controls

AbstractChoroidal changes have been suggested to be involved in the pathophysiology of both age-related macular degeneration (AMD) and pachychoroid spectrum diseases (PSD). To find out the choroidal characteristics of each disease groups, various groups of AMD and PSD were classified into several clusters according to choroidal profiles based on subfoveal choroidal thickness (CT), peripapillary CT, the ratio of subfoveal CT to peripapillary CT and age. We retrospectively analyzed 661 eyes, including 190 normal controls and 471 with AMD or PSDs. In the AMD groups, eyes with soft drusen or reticular pseudodrusen were belonged to the same cluster as those with classic exudative AMD (all p < 0.001). However, eyes with pachydrusen were not clustered with eyes from other AMD groups; instead, they were classified in the same cluster as eyes from the PSD group (all p < 0.001). In the PSD group, eyes with pachychoroid neovasculopathy were grouped in the same cluster of those with polypoidal choroidal vasculopathy (p < 0.001). The cluster analysis based on the CT profiles, including subfoveal CT, peripapillary CT, and their ratio, revealed a clustering pattern of eyes with AMD and PSDs. These findings support the suggestion that pachydrusen has the common pathogenesis as PSD.

scholarly journals Genome-wide analysis of disease progression in age-related macular degeneration

2018 ◽  
Vol 27 (5) ◽  
pp. 929-940 ◽  
Author(s):  
Qi Yan ◽  
Ying Ding ◽  
Yi Liu ◽  
Tao Sun ◽  
Lars G Fritsche ◽  
...  
Keyword(s):  
Macular Degeneration ◽  
Disease Progression ◽  
Age Related Macular Degeneration ◽  
Genome Wide Analysis ◽  
Age Related ◽  
Genome Wide

scholarly journals The Relevance of Cataract as a Risk Factor for Age-Related Macular Degeneration: A Machine Learning Approach

2019 ◽  
Vol 9 (24) ◽  
pp. 5550
Author(s):  
Antonieta Martínez-Velasco ◽  
Lourdes Martínez-Villaseñor ◽  
Luis Miralles-Pechuán ◽  
Andric C. Perez-Ortiz ◽  
Juan C. Zenteno ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Macular Degeneration ◽  
Significant Risk ◽  
Age Related Macular Degeneration ◽  
Supervised Machine Learning ◽  
Current Debate ◽  
Learning Approach ◽  
Age Related ◽  
Machine Learning Approach

Age-related macular degeneration (AMD) is the leading cause of visual dysfunction and irreversible blindness in developed countries and a rising cause in underdeveloped countries. There is a current debate on whether or not cataracts are significant risk factors for AMD development. In particular, research regarding this association is so far inconclusive. For this reason, we aimed to employ here a machine-learning approach to analyze the relevance and importance of cataracts as a risk factor for AMD in a large cohort of Hispanics from Mexico. We conducted a nested case control study of 119 cataract cases and 137 healthy unmatched controls focusing on clinical data from electronic medical records. Additionally, we studied two single nucleotide polymorphisms in the CFH gene previously associated with the disease in various populations as positive control for our method. We next determined the most relevant variables and found the bivariate association between cataracts and AMD. Later, we used supervised machine-learning methods to replicate these findings without bias. To improve the interpretability, we detected the five most relevant features and displayed them using a bar graph and a rule-based tree. Our findings suggest that bilateral cataracts are not a significant risk factor for AMD development among Hispanics from Mexico.

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