Deep learning algorithm predicts diabetic retinopathy progression in individual patients

Diabetic retinopathy is a leading cause of vision loss in working age adults.  One the major issues with managing this disease is the ability to predict future progression.

Recent data has highlighted that we can prevent an event regress retinopathy progression by applying anti-VEGF agents to these patients.  And while many AI platforms to date have focused on detecting retinopathy, few have aimed at evaluating disease progression.  

The purpose of this study was to develop a scale for progression over a year period when provided just with a single color retinal fundus image.  The authors developed a deep learning algorithm evaluating the 7 standard fields and achieve an area under the curve of 0.79.   Quite simply – we can use a single image to predict a 2 step retinopathy progression at 6, 12, or 24 months.  Thus we can use this algorithm when commercially available to set follow up periods more reliably for our patients. 

The one limitation is that the authors did use the RISE/RIDE prospective clinical trial data set and thus validating this algorithm in other data sets might help to further solidify its power of prediction.

Direct Link To Full Article: Nature Research

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