The new study, published in the journal PLOS One, identified novel biomarkers that could be used to detect diabetic retinopathy and possibly even diabetes.
Diabetes can affect the eyes in its early stages, before the changes are detectable on a routine clinical examination. However, new retinal research has discovered that by combining specialised optical techniques and computer analysis, these changes can be detected earlier than previously believed.
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The ability to detect biomarkers for this sight-threatening condition may result in the early identification of individuals at risk of diabetes or visual impairment, as well as an improvement in physicians’ ability to manage these patients.
Diabetic retinopathy is the most common diabetic eye disease and the leading cause of blindness in adults in the United States. Americans with diabetic retinopathy will nearly double from 7.7 million in 2010 to 14.6 million in 2050.
The new study is part of a growing body of work focusing on using artificial intelligence to detect diabetic retinopathy in retinal images. Some of these algorithms, on the other hand, detect changes based on features that appear much later than the changes observed in this study.
Because of the retinal image processing algorithms described in the study, the IU-led method allows for earlier detection.
They analysed data from diabetic volunteers as well as healthy control subjects. Additionally, data from a diabetic retinopathy screening of members of the underserved community at the University of California, Berkeley, and Alameda Health were collected.
Although the computer analysis was performed on retinal image data that are routinely collected in well-equipped clinics, much of the information used in this study is frequently overlooked during patient diagnosis and management.
Image Credit: GEtty
