scholarly journals Automated analysis of retinal imaging using machine learning techniques for computer vision

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 1573 ◽  
Author(s):  
Jeffrey De Fauw ◽  
Pearse Keane ◽  
Nenad Tomasev ◽  
Daniel Visentin ◽  
George van den Driessche ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Macular Degeneration ◽  
Human Error ◽  
Machine Learning Algorithms ◽  
Age Related Macular Degeneration ◽  
Machine Learning Techniques ◽  
Sound Waves ◽  
Age Related ◽  
Fundus Photographs

There are almost two million people in the United Kingdom living with sight loss, including around 360,000 people who are registered as blind or partially sighted. Sight threatening diseases, such as diabetic retinopathy and age related macular degeneration have contributed to the 40% increase in outpatient attendances in the last decade but are amenable to early detection and monitoring. With early and appropriate intervention, blindness may be prevented in many cases. Ophthalmic imaging provides a way to diagnose and objectively assess the progression of a number of pathologies including neovascular (“wet”) age-related macular degeneration (wet AMD) and diabetic retinopathy. Two methods of imaging are commonly used: digital photographs of the fundus (the ‘back’ of the eye) and Optical Coherence Tomography (OCT, a modality that uses light waves in a similar way to how ultrasound uses sound waves). Changes in population demographics and expectations and the changing pattern of chronic diseases creates a rising demand for such imaging. Meanwhile, interrogation of such images is time consuming, costly, and prone to human error. The application of novel analysis methods may provide a solution to these challenges. This research will focus on applying novel machine learning algorithms to automatic analysis of both digital fundus photographs and OCT in Moorfields Eye Hospital NHS Foundation Trust patients. Through analysis of the images used in ophthalmology, along with relevant clinical and demographic information, Google DeepMind Health will investigate the feasibility of automated grading of digital fundus photographs and OCT and provide novel quantitative measures for specific disease features and for monitoring the therapeutic success.

scholarly journals Automated analysis of retinal imaging using machine learning techniques for computer vision

F1000Research ◽  
2017 ◽  
Vol 5 ◽  
pp. 1573 ◽  
Author(s):  
Jeffrey De Fauw ◽  
Pearse Keane ◽  
Nenad Tomasev ◽  
Daniel Visentin ◽  
George van den Driessche ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Macular Degeneration ◽  
Human Error ◽  
Machine Learning Algorithms ◽  
Age Related Macular Degeneration ◽  
Machine Learning Techniques ◽  
Sound Waves ◽  
Age Related ◽  
Fundus Photographs

There are almost two million people in the United Kingdom living with sight loss, including around 360,000 people who are registered as blind or partially sighted. Sight threatening diseases, such as diabetic retinopathy and age related macular degeneration have contributed to the 40% increase in outpatient attendances in the last decade but are amenable to early detection and monitoring. With early and appropriate intervention, blindness may be prevented in many cases. Ophthalmic imaging provides a way to diagnose and objectively assess the progression of a number of pathologies including neovascular (“wet”) age-related macular degeneration (wet AMD) and diabetic retinopathy. Two methods of imaging are commonly used: digital photographs of the fundus (the ‘back’ of the eye) and Optical Coherence Tomography (OCT, a modality that uses light waves in a similar way to how ultrasound uses sound waves). Changes in population demographics and expectations and the changing pattern of chronic diseases creates a rising demand for such imaging. Meanwhile, interrogation of such images is time consuming, costly, and prone to human error. The application of novel analysis methods may provide a solution to these challenges. This research will focus on applying novel machine learning algorithms to automatic analysis of both digital fundus photographs and OCT in Moorfields Eye Hospital NHS Foundation Trust patients. Through analysis of the images used in ophthalmology, along with relevant clinical and demographic information, DeepMind Health will investigate the feasibility of automated grading of digital fundus photographs and OCT and provide novel quantitative measures for specific disease features and for monitoring the therapeutic success.

scholarly journals Chances and challenges of machine learning based disease classification in genetic association studies illustrated on age-related macular degeneration

2019 ◽  
Author(s):  
Felix Günther ◽  
Caroline Brandl ◽  
Thomas W. Winkler ◽  
Veronika Wanner ◽  
Klaus Stark ◽  
...  
Keyword(s):  
Machine Learning ◽  
Macular Degeneration ◽  
Genetic Association ◽  
Association Studies ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Disease Classification ◽  
Age Related Macular Degeneration ◽  
Uk Biobank ◽  
Age Related

AbstractImaging technology and machine learning algorithms for disease classification set the stage for high-throughput phenotyping and promising new avenues for genome-wide association studies (GWAS). Despite emerging algorithms, there has been no successful application in GWAS so far. We established machine learning based disease classification in genetic association analysis as a misclassification problem. To evaluate chances and challenges, we performed a GWAS based on automated classification of age-related macular degeneration (AMD) in UK Biobank (images from 135,500 eyes; 68,400 persons). We quantified misclassification of automatically derived AMD in internal validation data (images from 4,001 eyes; 2,013 persons) and developed a maximum likelihood approach (MLA) to account for it when estimating genetic association. We demonstrate that our MLA guards against bias and artefacts in simulation studies. By combining a GWAS on automatically derived AMD classification and our MLA in UK Biobank data, we were able to dissect true association (ARMS2/HTRA1, CFH) from artefacts (near HERC2) and to identify eye color as relevant source of misclassification. On this example of AMD, we are able to provide a proof-of-concept that a GWAS using machine learning derived disease classification yields relevant results and that misclassification needs to be considered in the analysis. These findings generalize to other phenotypes and also emphasize the utility of genetic data for understanding misclassification structure of machine learning algorithms.

scholarly journals The prevalence of undiagnosed age-related sight threatening diseases in self-proclaimed healthy individuals

2019 ◽  
Author(s):  
Sophie Lemmens ◽  
João Barbosa Breda ◽  
Karel Van Keer ◽  
Tine Jacobs ◽  
Ruben Van Landeghem ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Macular Degeneration ◽  
Significant Proportion ◽  
Clinical Findings ◽  
Age Related Macular Degeneration ◽  
Ophthalmological Examination ◽  
Healthy Elderly ◽  
Age Related ◽  
Medical Questionnaire

Abstract Background Age-related conditions such as glaucoma, age-related macular degeneration (AMD), diabetic retinopathy (DRP) and cataract have become the major cause of visual impairment and blindness in high-income countries and carry a major socio-economic burden. The aim of the current study is to investigate the prevalence of age-related eye diseases such as glaucoma, age-related macular degeneration, diabetic retinopathy and cataract in a cohort of self-proclaimed healthy elderly, and thus get a rough estimation of the prevalence of undiagnosed age-related eye conditions in the Belgian population.Methods Individuals aged 55 and older without ophthalmological complaints were asked to fill in a general medical questionnaire and underwent an ophthalmological examination, which included a biomicroscopic examination, intraocular pressure measurement, axial length measurement, and acquisition of fundus pictures and Optical Coherence Tomography scans. Information regarding follow-up was collected in the subset of participants who received the advice of referral to an ophthalmologist or the advice to have more frequent follow-up visits, based on the ophthalmological changes detected in their evaluation.Results The cohort included 102 people and comprised 46% men (median age 70 years, range 57-85 years). Referral for additional examinations based on clinical findings, was made in 26 participants (25%). The advice to have more regular follow-up ophthalmologist visits was given to nine additional participants (9%). No significant correlations between baseline characteristics, including eye care consumption, and the need for referral could be identified. Follow-up information was available for 25 out of 26 referred volunteers (96%). Out of these, four (16%) underwent a therapeutical intervention based on study referral, up until 18 months after study participation. All four interventions took place in the age group 65 - 74 years.Conclusions This study shows that even in an elderly population with self-proclaimed healthy eyes and good general health, a significant proportion of subjects showed ocular findings that need regular follow up and/or intervention. Moreover, the frequency of prior ophthalmological examinations does not seem to be relevant to this proportion, meaning that everyone above 55 years old needs a routine ophthalmological evaluation.

scholarly journals Improving Efficiency in Separating Blood Vessels from Retinal Images with Deep Learning Techniques

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3637-3640
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Macular Degeneration ◽  
Blood Vessels ◽  
Age Related Macular Degeneration ◽  
Retinal Images ◽  
Retinal Vessels ◽  
Age Related ◽  
Learning Techniques ◽  
Retinal Pathologies

Retinal vessels ID means to isolate the distinctive retinal configuration issues, either wide or restricted from fundus picture foundation, for example, optic circle, macula, and unusual sores. Retinal vessels recognizable proof investigations are drawing in increasingly more consideration today because of pivotal data contained in structure which is helpful for the identification and analysis of an assortment of retinal pathologies included yet not restricted to: Diabetic Retinopathy (DR), glaucoma, hypertension, and Age-related Macular Degeneration (AMD). With the advancement of right around two decades, the inventive methodologies applying PC supported systems for portioning retinal vessels winding up increasingly significant and coming nearer. Various kinds of retinal vessels segmentation strategies discussed by using Deep Learning methods. At that point, the pre-processing activities and the best in class strategies for retinal vessels distinguishing proof are presented.

scholarly journals Epigenetic modifications in hyperhomocysteinemia: potential role in diabetic retinopathy and age-related macular degeneration

Oncotarget ◽  
2018 ◽  
Vol 9 (16) ◽  
pp. 12562-12590 ◽  
Author(s):  
Khaled Elmasry ◽  
Riyaz Mohamed ◽  
Isha Sharma ◽  
Nehal M. Elsherbiny ◽  
Yutao Liu ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Macular Degeneration ◽  
Potential Role ◽  
Epigenetic Modifications ◽  
Age Related Macular Degeneration ◽  
Age Related
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