scholarly journals Machine Learning to Analyze the Prognostic Value of Current Imaging Biomarkers in Neovascular Age-Related Macular Degeneration

2018 ◽  
Vol 2 (1) ◽  
pp. 24-30 ◽  
Author(s):  
Ursula Schmidt-Erfurth ◽  
Hrvoje Bogunovic ◽  
Amir Sadeghipour ◽  
Thomas Schlegl ◽  
Georg Langs ◽  
...  
Keyword(s):  
Machine Learning ◽  
Macular Degeneration ◽  
Prognostic Value ◽  
Age Related Macular Degeneration ◽  
Imaging Biomarkers ◽  
Age Related

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

Major Predictive Factors for Progression of Early to Late Age-Related Macular Degeneration

2020 ◽  
Vol 243 (6) ◽  
pp. 444-452 ◽  
Author(s):  
Vasilena Sitnilska ◽  
Eveline Kersten ◽  
Lebriz Altay ◽  
Tina Schick ◽  
Philip Enders ◽  
...  
Keyword(s):  
Prediction Model ◽  
Macular Degeneration ◽  
Area Under The Curve ◽  
Age Related Macular Degeneration ◽  
Advanced Age ◽  
Fundus Photography ◽  
Imaging Biomarkers ◽  
Nucleotide Polymorphisms ◽  
Phenotypic Profile ◽  
Age Related

<b><i>Introduction:</i></b> We present a prediction model for progression from early/intermediate to advanced age-related macular degeneration (AMD) within 5.9 years. <b><i>Objectives:</i></b> To evaluate the combined role of genetic, nongenetic, and phenotypic risk factors for conversion from early to late AMD over ≥5 years. <b><i>Methods:</i></b> Baseline phenotypic characteristics were evaluated based on color fundus photography, spectral-domain optical coherence tomography, and infrared images. Genotyping for 36 single-nucleotide polymorphisms as well as systemic lipid and complement measurements were performed. Multivariable backward logistic regression resulted in a final prediction model. <b><i>Results and Conclusions:</i></b> During a mean of 5.9 years of follow-up, 22.4% (<i>n</i> = 52) of the patients (<i>n</i> = 232) showed progression to late AMD. The multivariable prediction model included age, <i>CFH</i> variant rs1061170, pigment abnormalities, drusenoid pigment epithelial detachment (DPED), and hyperreflective foci (HRF). The model showed an area under the curve of 0.969 (95% confidence interval 0.948–0.990) and adequate calibration (Hosmer-Lemeshow test, <i>p</i> = 0.797). In addition to advanced age and carrying a <i>CFH</i> variant, pigment abnormalities, DPED, and HRF are relevant imaging biomarkers for conversion to late AMD. In clinical routine, an intensified monitoring of patients with a high-risk phenotypic profile may be suitable for the early detection of conversion to late AMD.

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.

Quantitative SD-OCT Imaging Biomarkers as Indicators of Age-Related Macular Degeneration Progression

2014 ◽  
Vol 55 (11) ◽  
pp. 7093 ◽  
Author(s):  
Luis de Sisternes ◽  
Noah Simon ◽  
Robert Tibshirani ◽  
Theodore Leng ◽  
Daniel L. Rubin
Keyword(s):  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Imaging Biomarkers ◽  
Age Related ◽  
Oct Imaging ◽  
Sd Oct

scholarly journals Topological analysis of single-cell data reveals shared glial landscape of macular degeneration and neurodegenerative diseases

2021 ◽  
Author(s):  
Manik Kuchroo ◽  
Marcello DiStasio ◽  
Eda Calapkulu ◽  
Maryam Ige ◽  
Le Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Alzheimer’S Disease ◽  
Macular Degeneration ◽  
Single Cell ◽  
Topological Analysis ◽  
Age Related Macular Degeneration ◽  
Learning Approach ◽  
Age Related ◽  
Machine Learning Approach

1One Sentence SummaryA novel topological machine learning approach applied to single-nucleus RNA sequencing from human retinas with age-related macular degeneration identifies interacting disease phase-specific glial activation states shared with Alzheimer’s disease and multiple sclerosis.2AbstractNeurodegeneration occurs in a wide range of diseases, including age-related macular degeneration (AMD), Alzheimer’s disease (AD), and multiple sclerosis (MS), each with distinct inciting events. To determine whether glial transcriptional states are shared across phases of degeneration, we sequenced 50,498 nuclei from the retinas of seven AMD patients and six healthy controls, generating the first single-cell transcriptomic atlas of AMD. We identified groupings of cells implicated in disease pathogenesis by applying a novel topologically-inspired machine learning approach called ‘diffusion condensation.’ By calculating diffusion homology features and performing persistence analysis, diffusion condensation identified activated glial states enriched in the early phases of AMD, AD, and MS as well as an AMD-specific proangiogenic astrocyte state promoting pathogenic neovascularization in advanced AMD. Finally, by mapping the expression of disease-associated genes to glial states, we identified key signaling interactions creating hypotheses for therapeutic intervention. Our topological analysis identified an integrated disease-phase specific glial landscape that is shared across neurodegenerative conditions affecting the central nervous system.

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