A deep learning algorithm developed by Genentech and Roche researchers predicts a patient’s diabetic retinopathy progression using color fundus photographs acquired at a single visit, according to a study.
“Our deep learning pilot study shows the potential for a tool that is highly accurate in predicting diabetic retinopathy progression at an individual patient level in a single doctor visit. Such a tool could help inform the patient’s treatment strategy — earlier treatment depending on the risk for fast progression — in order to protect and preserve their vision,” study author Zdenka Haskova, MD, PhD, medical director in clinical ophthalmology at Genentech, told Healio.com/OSN.
The retrospective study analyzed stereoscopic seven-field central fundus photographs obtained from eyes in the RIDE and RISE phase 3 studies at baseline. The eyes were treatment-naive to anti-VEGF therapy. Researchers generated algorithms from these data sets to predict worsening in untreated eyes from baseline over a period of 2 years.
Researchers then used seven-field central fundus photographs acquired from patients with diabetic retinopathy to train deep learning models to predict a two-step or more worsening on the ETDRS diabetic retinopathy severity scale over 2 years.
Worsening was predicted with an area under the curve of 0.68 ±0.13, a sensitivity of 66% and a specificity of 77% at 6 months. At 12 months, AUC was 0.79 ± 0.05, sensitivity 91% and specificity 65%. At 24 months, worsening was predicted with an AUC of 0.77 ± 0.04, a sensitivity of 79% and a specificity of 72%.
“This type of predictive algorithm could help patients get the individualized care they need, as their ophthalmologists could potentially tailor a patient’s treatment strategy knowing whether they are at risk for faster diabetic retinopathy progression,” Haskova said. – by Robert Linnehan
Disclosure: Haskova reports she is an employee and shareholder of Genentech.