scholarly journals Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients

2020 ◽  
pp. bjophthalmol-2020-316594 ◽  
Author(s):  
Peter Heydon ◽  
Catherine Egan ◽  
Louis Bolter ◽  
Ryan Chambers ◽  
John Anderson ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Large Scale ◽  
Proliferative Retinopathy ◽  
Retinal Images ◽  
Technical Failure ◽  
Reference Standard ◽  
Diabetic Retinopathy Screening ◽  
Diabetic Eye Screening

Background/aimsHuman grading of digital images from diabetic retinopathy (DR) screening programmes represents a significant challenge, due to the increasing prevalence of diabetes. We evaluate the performance of an automated artificial intelligence (AI) algorithm to triage retinal images from the English Diabetic Eye Screening Programme (DESP) into test-positive/technical failure versus test-negative, using human grading following a standard national protocol as the reference standard.MethodsRetinal images from 30 405 consecutive screening episodes from three English DESPs were manually graded following a standard national protocol and by an automated process with machine learning enabled software, EyeArt v2.1. Screening performance (sensitivity, specificity) and diagnostic accuracy (95% CIs) were determined using human grades as the reference standard.ResultsSensitivity (95% CIs) of EyeArt was 95.7% (94.8% to 96.5%) for referable retinopathy (human graded ungradable, referable maculopathy, moderate-to-severe non-proliferative or proliferative). This comprises sensitivities of 98.3% (97.3% to 98.9%) for mild-to-moderate non-proliferative retinopathy with referable maculopathy, 100% (98.7%,100%) for moderate-to-severe non-proliferative retinopathy and 100% (97.9%,100%) for proliferative disease. EyeArt agreed with the human grade of no retinopathy (specificity) in 68% (67% to 69%), with a specificity of 54.0% (53.4% to 54.5%) when combined with non-referable retinopathy.ConclusionThe algorithm demonstrated safe levels of sensitivity for high-risk retinopathy in a real-world screening service, with specificity that could halve the workload for human graders. AI machine learning and deep learning algorithms such as this can provide clinically equivalent, rapid detection of retinopathy, particularly in settings where a trained workforce is unavailable or where large-scale and rapid results are needed.

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.

scholarly journals Simple, Mobile-based Artificial Intelligence Algorithm in the detection of Diabetic Retinopathy (SMART) study

2020 ◽  
Vol 8 (1) ◽  
pp. e000892 ◽  
Author(s):  
Bhavana Sosale ◽  
Sosale Ramachandra Aravind ◽  
Hemanth Murthy ◽  
Srikanth Narayana ◽  
Usha Sharma ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Sensitivity And Specificity ◽  
Smart Phone ◽  
High Sensitivity ◽  
Ground Truth ◽  
Cross Sectional Study ◽  
Retinal Images ◽  
Cross Sectional ◽  
Smart Study

IntroductionThe aim of this study is to evaluate the performance of the offline smart phone-based Medios artificial intelligence (AI) algorithm in the diagnosis of diabetic retinopathy (DR) using non-mydriatic (NM) retinal images.MethodsThis cross-sectional study prospectively enrolled 922 individuals with diabetes mellitus. NM retinal images (disc and macula centered) from each eye were captured using the Remidio NM fundus-on-phone (FOP) camera. The images were run offline and the diagnosis of the AI was recorded (DR present or absent). The diagnosis of the AI was compared with the image diagnosis of five retina specialists (majority diagnosis considered as ground truth).ResultsAnalysis included images from 900 individuals (252 had DR). For any DR, the sensitivity and specificity of the AI algorithm was found to be 83.3% (95% CI 80.9% to 85.7%) and 95.5% (95% CI 94.1% to 96.8%). The sensitivity and specificity of the AI algorithm in detecting referable DR (RDR) was 93% (95% CI 91.3% to 94.7%) and 92.5% (95% CI 90.8% to 94.2%).ConclusionThe Medios AI has a high sensitivity and specificity in the detection of RDR using NM retinal images.

scholarly journals Correction to: Artificial intelligence for diabetic retinopathy screening: a review

Eye ◽  
2019 ◽  
Vol 34 (3) ◽  
pp. 604-604
Author(s):  
Andrzej Grzybowski ◽  
Piotr Brona ◽  
Gilbert Lim ◽  
Paisan Ruamviboonsuk ◽  
Gavin S. W. Tan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Diabetic Retinopathy Screening ◽  
Retinopathy Screening

scholarly journals Saving sight in China and beyond: the Lifeline Express model

2018 ◽  
Vol 3 (4) ◽  
pp. e000766 ◽  
Author(s):  
Ian Yat Hin Wong ◽  
Michael Yuxuan Ni ◽  
Irene Oi Ling Wong ◽  
Nellie Fong ◽  
Gabriel M Leung
Keyword(s):  
Diabetic Retinopathy ◽  
Cataract Surgery ◽  
Large Scale ◽  
Rural China ◽  
Adult Population ◽  
Early Screening ◽  
Diabetic Retinopathy Screening ◽  
Health Strategy ◽  
Patients With Diabetes ◽  
Retinopathy Screening

Cataract and diabetic retinopathy are leading causes of blindness globally. Lifeline Express (LEX) has pioneered the provision of cataract surgery in rural China from custom-built trains and eye centres nationwide. Over the past two decades, LEX has provided free cataract surgery for over 180 000 patients in China. In China, half of the adult population has prediabetes and 113 million adults have diabetes. Recognising the rising threat of diabetic retinopathy, LEX has expanded to providing free diabetic retinopathy screening nationwide by establishing 29 Diabetic Retinopathy Screening Centres across China. Source of referrals included host hospitals, the community and out-reach mobile vans equipped with fundus cameras. Fundi photos taken in the mobile vans were electronically transferred to primary graders. LEX also leveraged the widespread smartphone use to provide electronic medical reports via WeChat, the most popular instant messenger app in China. From April 2014 to December 2016, 34 506 patients with diabetes underwent screening, of which 27.2% (9,396) were identified to have diabetic retinopathy. China’s latest national health strategy (‘Healthy China 2030 Plan’) has championed the ‘prevention first’ principle and early screening of chronic diseases. LEX has accordingly evolved to extend its services to save sight in China—from cataract surgery to diabetic retinopathy screening and most recently outreaching beyond its national borders in a pilot South–South collaboration. With health at the top of the China’s developmental agenda and the country’s growing role in global health—LEX’s large-scale telemedicine-enabled programme could represent a potentially scalable model for nationwide diabetic retinopathy screening elsewhere.

scholarly journals Can Commercially Available Handheld Retinal Cameras Effectively Screen Diabetic Retinopathy?

2016 ◽  
Vol 11 (1) ◽  
pp. 135-137 ◽  
Author(s):  
Jorge Cuadros ◽  
George Bresnick
Keyword(s):  
Clinical Trials ◽  
Diabetic Retinopathy ◽  
Screening Programme ◽  
Low Cost ◽  
Clinical Results ◽  
Diabetic Retinopathy Screening ◽  
The United Kingdom ◽  
Eye Screening ◽  
Diabetic Eye Screening ◽  
Retinopathy Screening

Organizations that care for people with diabetes have increasingly adopted telemedicine-based diabetic retinopathy screening (TMDRS) as a way to increase adherence to recommended retinal exams. Recently, handheld retinal cameras have emerged as a low-cost, lightweight alternative to traditional bulky tabletop retinal cameras. Few published clinical trials have been performed on handheld retinal cameras. Peer-reviewed articles about commercially available handheld retinal cameras have concluded that they are a usable alternative for TMDRS, however, the clinical results presented in these articles do not meet criteria published by the United Kingdom Diabetic Eye Screening Programme and the American Academy of Ophthalmology. The future will likely remedy the shortcomings of currently available handheld retinal cameras, and will create more opportunities for preventing diabetic blindness.

Sign in / Sign up

Export Citation Format

Share Document