scholarly journals Differentiation of Diabetic Status Using Statistical and Machine Learning Techniques on Optical Coherence Tomography Angiography Images

2020 ◽  
Vol 9 (4) ◽  
pp. 2 ◽  
Author(s):  
Tariq Mehmood Aslam ◽  
David Charles Hoyle ◽  
Vikram Puri ◽  
Goncalo Bento
Keyword(s):  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Optical Coherence Tomography Angiography ◽  
Machine Learning Techniques ◽  
Optical Coherence ◽  
Diabetic Status ◽  
Learning Techniques

scholarly journals Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques

PLoS ONE ◽  
2019 ◽  
Vol 14 (5) ◽  
pp. e0216410 ◽  
Author(s):  
Amaya Pérez del Palomar ◽  
José Cegoñino ◽  
Alberto Montolío ◽  
Elvira Orduna ◽  
Elisa Vilades ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Optical Coherence Tomography ◽  
Machine Learning Techniques ◽  
Optical Coherence ◽  
Swept Source ◽  
Learning Techniques ◽  
Multiple Sclerosis Disease

scholarly journals Machine learning in optical coherence tomography angiography

2021 ◽  
pp. 153537022110265
Author(s):  
David Le ◽  
Taeyoon Son ◽  
Xincheng Yao
Keyword(s):  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Image Analysis ◽  
Human Error ◽  
Optical Coherence Tomography Angiography ◽  
Disease Classification ◽  
Optical Coherence ◽  
Label Free ◽  
Machine Learning Classification ◽  
Recent Developments

Optical coherence tomography angiography (OCTA) offers a noninvasive label-free solution for imaging retinal vasculatures at the capillary level resolution. In principle, improved resolution implies a better chance to reveal subtle microvascular distortions associated with eye diseases that are asymptomatic in early stages. However, massive screening requires experienced clinicians to manually examine retinal images, which may result in human error and hinder objective screening. Recently, quantitative OCTA features have been developed to standardize and document retinal vascular changes. The feasibility of using quantitative OCTA features for machine learning classification of different retinopathies has been demonstrated. Deep learning-based applications have also been explored for automatic OCTA image analysis and disease classification. In this article, we summarize recent developments of quantitative OCTA features, machine learning image analysis, and classification.

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