60,000 Patient Image Sets from 6 Different Diabetic Eye Screening Programmes Analyzed Using EyeArt AI Eye Screening System
LOS ANGELES–(BUSINESS WIRE)–#ai4healthcare—Eyenuk, Inc., a global artificial intelligence (AI) medical technology and services company and the leader in real-world applications for AI Eye Screening™, announced that it has successfully fulfilled the contract awarded by Public Health England (PHE) to use Eyenuk’s EyeArt AI Eye Screening System to grade 60,000 patient image sets from 6 different National Health Service (NHS) Diabetic Eye Screening Programmes in England.
Diabetic retinopathy (DR) is a vision-threatening complication of diabetes and a leading cause of preventable vision loss globally.1 In England, an estimated 4.6 million are living with diabetes, one-third of whom are at risk of developing DR. Diabetes has become a growing health concern as the number of people diagnosed with diabetes in the U.K. has more than doubled in the last 20 years.2
The U.K. has been leading the world in diabetic retinopathy screening, achieving patient uptake rates of over 80% (screening nearly 2.5 million diabetes patients annually),3 as compared with most parts of the world where typically less than half of diabetes patients receive annual eye screening.4 As a result, diabetic retinopathy is no longer the leading cause of blindness in the working age group in England.5 However, the growing diabetes population poses significant challenges ahead.
Public Health England (PHE) is an executive agency of the Department of Health and Social Care (DH) that oversees the NHS national health screening programmes. An independent Health Technology Assessment from the Moorfields Eye Hospital to determine the screening performance and cost-effectiveness of multiple DR detection AI solutions was conducted and published in 2016.6 Subsequently, PHE initiated a tender process seeking to commission an automated retinal image grading software to grade 60,000 patient image sets from multiple diabetic eye screening programmes.
At the end of the competitive tender process, the contract was awarded to Eyenuk.7 The National Diabetic Eye Screening Programme (NDESP) identified 6 local diabetic eye screening (DES) programmes to participate in the project with Eyenuk. The project aim was to compare the number of image sets categorised as having no disease, as determined by human graders (manual programme grading), with the number as determined by the EyeArt AI eye screening system. Results from this latest real-world analysis, together with results from previous assessments have shown that the EyeArt system has excellent agreement and sensitivity and specificity for detecting diabetic retinopathy.
“Eyenuk was honored to have been awarded the PHE contract for diabetic retinopathy grading, and we are gratified that our EyeArt AI system delivered excellent results when compared with six DES programmes in England,” said Kaushal Solanki, Ph.D., founder and CEO of Eyenuk. “We look forward to expanding our work in the U.K. with hope to support all diabetic eye screening programmes in the future.”
The independent Health Technology Assessment (HTA) from Moorfields Eye Hospital involving more than 20,000 patients was conducted to determine the screening performance and cost-effectiveness of multiple automated retinal image analysis systems. This study demonstrated that the EyeArt AI System delivered much higher sensitivity (i.e., patient safety) for DR screening than other automated DR screening technologies investigated and that its use is cost-effective alternative to the current, purely manual grading approach. The HTA demonstrated that the EyeArt performance was not affected by ethnicity, gender, or camera type.
About the EyeArt® AI Eye Screening System
The EyeArt AI Eye Screening System provides fully automated DR screening, including retinal imaging, DR grading on international standards and the option of immediate reporting, during a diabetic patient’s regular office visit. Once the patient’s fundus images have been captured and submitted to the EyeArt AI System, the DR screening results are available in a PDF report in less than 60 seconds.
The EyeArt AI System was developed with funding from the U.S. National Institutes of Health (NIH) and is validated by the U.K. National Health Service (NHS). The EyeArt AI System has CE marking as a class IIa medical device in the European Union and a Health Canada license. In the U.S., the EyeArt AI System is limited by federal law to investigational use. It is designed to be General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act of 1996 (HIPAA) compliant.
About Eyenuk, Inc.
Eyenuk, Inc. is a global artificial intelligence (AI) medical technology and services company and the leader in real-world AI Eye Screening for autonomous disease detection and AI Predictive Biomarkers™ for risk assessment and disease surveillance. Eyenuk’s first product, the EyeArt AI Eye Screening System, is the most extensively validated AI technology for autonomous detection of DR. Eyenuk is on a mission to screen every eye in the world to ensure timely diagnosis of life- and vision-threatening diseases, including diabetic retinopathy, glaucoma, age-related macular degeneration, stroke risk, cardiovascular risk and Alzheimer’s disease. Find Eyenuk online on its website, Twitter, Facebook, and LinkedIn.
4 K. Fitch, T. Weisman, T. Engel, A. Turpcu, H. Blumen, Y. Rajput, and P. Dave. Longitudinal commercial claims-based cost analysis of diabetic retinopathy screening patterns. Am Health Drug Benefits. 2015;8(6):300–308.
5 G. Liew, M. Michaelides, C. Bunce. A comparison of the causes of blindness certifications in England and Wales in working age adults (16–64 years), 1999–2000 with 2009–2010. BMJ Open Bd. 4 (2014), Nr. 2
6 Adnan Tufail, Venediktos V Kapetanakis, Sebastian Salas-Vega, Catherine Egan, Caroline Rudisill, Christopher G Owen, Aaron Lee, et al. “An Observational Study to Assess If Automated Diabetic Retinopathy Image Assessment Software Can Replace One or More Steps of Manual Imaging Grading and to Determine Their Cost-Effectiveness.” Health Technology Assessment 20, no. 92 (December 2016). https://doi.org/10.3310/hta20920
Frank Cheng, Chief Commercial Officer
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