Automatic detection of diabetic retinopathy features in ultra-wide field retinal images

2017 ◽  
Author(s):  
Anastasia Levenkova ◽  
Arcot Sowmya ◽  
Michael Kalloniatis ◽  
Angelica Ly ◽  
Arthur Ho
Keyword(s):  
Diabetic Retinopathy ◽  
Automatic Detection ◽  
Retinal Images ◽  
Wide Field

Lesion detection in ultra-wide field retinal images for diabetic retinopathy diagnosis

2018 ◽  
Author(s):  
Anastasia Levenkova ◽  
Michael Kalloniatis ◽  
Angelica Ly ◽  
Arthur Ho ◽  
Arcot Sowmya
Keyword(s):  
Diabetic Retinopathy ◽  
Lesion Detection ◽  
Retinal Images ◽  
Wide Field

scholarly journals Diagnostic accuracy of diabetic retinopathy grading by an artificial intelligence-enabled algorithm compared with a human standard for wide-field true-colour confocal scanning and standard digital retinal images

2020 ◽  
pp. bjophthalmol-2019-315394
Author(s):  
Abraham Olvera-Barrios ◽  
Tjebo FC Heeren ◽  
Konstantinos Balaskas ◽  
Ryan Chambers ◽  
Louis Bolter ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Screening Programme ◽  
Proliferative Retinopathy ◽  
Retinal Images ◽  
Wide Field ◽  
Diabetic Retinopathy Screening ◽  
Eye Screening ◽  
Confocal Scanning ◽  
Diabetic Eye Screening ◽  
Retinopathy Screening

BackgroundPhotographic diabetic retinopathy screening requires labour-intensive grading of retinal images by humans. Automated retinal image analysis software (ARIAS) could provide an alternative to human grading. We compare the performance of an ARIAS using true-colour, wide-field confocal scanning images and standard fundus images in the English National Diabetic Eye Screening Programme (NDESP) against human grading.MethodsCross-sectional study with consecutive recruitment of patients attending annual diabetic eye screening. Imaging with mydriasis was performed (two-field protocol) with the EIDON platform (CenterVue, Padua, Italy) and standard NDESP cameras. Human grading was carried out according to NDESP protocol. Images were processed by EyeArt V.2.1.0 (Eyenuk Inc, Woodland Hills, California). The reference standard for analysis was the human grade of standard NDESP images.ResultsWe included 1257 patients. Sensitivity estimates for retinopathy grades were: EIDON images; 92.27% (95% CI: 88.43% to 94.69%) for any retinopathy, 99% (95% CI: 95.35% to 100%) for vision-threatening retinopathy and 100% (95% CI: 61% to 100%) for proliferative retinopathy. For NDESP images: 92.26% (95% CI: 88.37% to 94.69%) for any retinopathy, 100% (95% CI: 99.53% to 100%) for vision-threatening retinopathy and 100% (95% CI: 61% to 100%) for proliferative retinopathy. One case of vision-threatening retinopathy (R1M1) was missed by the EyeArt when analysing the EIDON images, but identified by the human graders. The EyeArt identified all cases of vision-threatening retinopathy in the standard images.ConclusionEyeArt identified diabetic retinopathy in EIDON images with similar sensitivity to standard images in a large-scale screening programme, exceeding the sensitivity threshold recommended for a screening test. Further work to optimise the identification of ‘no retinopathy’ and to understand the differential lesion detection in the two imaging systems would enhance the use of these two innovative technologies in a diabetic retinopathy screening setting.

Automatic detection of papilledema through fundus retinal images using deep learning

2021 ◽  
Author(s):  
Tanzila Saba ◽  
Shahzad Akbar ◽  
Hoshang Kolivand ◽  
Saeed Ali Bahaj
Keyword(s):  
Deep Learning ◽  
Automatic Detection ◽  
Retinal Images
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