scholarly journals Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach

2015 ◽  
Vol 15 (1) ◽  
Author(s):  
Paolo Fraccaro ◽  
Massimo Nicolo ◽  
Monica Bonetto ◽  
Mauro Giacomini ◽  
Peter Weller ◽  
...  
Keyword(s):  
Machine Learning ◽  
Macular Degeneration ◽  
Clinical Signs ◽  
Patient Characteristics ◽  
Age Related Macular Degeneration ◽  
Learning Approach ◽  
Age Related ◽  
Machine Learning Approach

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.

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.

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 A Systematic Review of Deep Learning Methods Applied to Ocular Images

2019 ◽  
Vol 30 (1) ◽  
pp. 9-26 ◽  
Author(s):  
Oscar Julian Perdomo Charry ◽  
Fabio Augusto González Osorio
Keyword(s):  
Deep Learning ◽  
Macular Degeneration ◽  
Clinical Signs ◽  
Age Related Macular Degeneration ◽  
Ocular Diseases ◽  
Learning Methods ◽  
Age Related ◽  
Box Models ◽  
The One

Artificial intelligence is having an important effect on different areas of medicine, and ophthalmology has not been the exception. In particular, deep learning methods have been applied successfully to the detection of clinical signs and the classification of ocular diseases. This represents a great potential to increase the number of people correctly diagnosed. In ophthalmology, deep learning methods have primarily been applied to eye fundus images and optical coherence tomography. On the one hand, these methods have achieved an outstanding performance in the detection of ocular diseases such as: diabetic retinopathy, glaucoma, diabetic macular degeneration and age-related macular degeneration.  On the other hand, several worldwide challenges have shared big eye imaging datasets with segmentation of part of the eyes, clinical signs and the ocular diagnostic performed by experts. In addition, these methods are breaking the stigma of black-box models, with the delivering of interpretable clinically information. This review provides an overview of the state-of-the-art deep learning methods used in ophthalmic images, databases and potential challenges for ocular diagnosis

scholarly journals Treatment for neovascular age-related macular degeneration: reasonable expectations of physicians and patients

2021 ◽  
Vol 21 (3) ◽  
pp. 169-174
Author(s):  
A.B. Durasov ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Macular Degeneration ◽  
Treatment Outcomes ◽  
Randomized Clinical Trials ◽  
Patient Characteristics ◽  
Age Related Macular Degeneration ◽  
Strict Adherence ◽  
Treat And Extend ◽  
Anti Vegf ◽  
Age Related

Neovascular age-related macular degeneration (nAMD) is a progressive chronic multifactorial disease requiring long-term, lifelong anti- VEGF therapy. Treatment outcomes are not always in line with the results of randomized clinical trials and do not meet the expectations for therapy whose success is assessed differently by patients and physicians. Good functional and anatomical results are expected from antivasoproliferative therapy under certain conditions, e.g., accurate evaluation of some patient characteristics (baseline visual acuity, type of choroidal neovascularization, comorbidities, status of retinal fluid and its differentiation), timely (as early as possible) treatment initiation after verifying diagnosis, and strict adherence to a proactive personalized "Treat-and-Extend" (T&E) regimen that implies a required number of injections with individual intervals. Poor adherence to treatment (non-compliance or nonpersistence of anti-VEGF therapy) significantly affects treatment outcomes in real-world clinical practice. This paper reviews criteria which predict the response to antivasoproliferative therapy and improving treatment adherence. The authors describe four fundamental principles to be met by an ideal regimen of anti-VEGF therapy for nAMD. Keywords: neovascular age-related macular degeneration, nAMD, "Treat-and-Extend", T&E, adherence, nonpersistence, anti-VEGF. For citation: Durasov A.B. Treatment for neovascular age-related macular degeneration: reasonable expectations of physicians and patients. Russian Journal of Clinical Ophthalmology. 2021;21(3):169–174 (in Russ.). DOI: 10.32364/2311-7729-2021-21-3-169-174.

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