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Among the most impactful diabetic complications are diabetic retinopathy, theleading cause of blindness among working class adults, and cardiovasculardisease, the leading cause of death worldwide. This study describes thedevelopment of improved machine learning based screening of these conditions.First, a random forest model was developed by retrospectively analyzing theinfluence of various risk factors (obtained quickly and non-invasively) oncardiovascular risk. Next, a deep-learning model was developed for predictionof diabetic retinopathy from retinal fundus images by a modified and re-trainedInceptionV3 image classification model. The input was simplified byautomatically segmenting the blood vessels in the retinal image. The techniqueof transfer learning enables the model to capitalize on existing infrastructureon the target device, meaning more versatile deployment, especially helpful inlow-resource settings. The models were integrated into a smartphone-baseddevice, combined with an inexpensive 3D-printed retinal imaging attachment.Accuracy scores, as well as the receiver operating characteristic curve, thelearning curve, and other gauges, were promising. This test is much cheaper andfaster, enabling continuous monitoring for two damaging complications ofdiabetes. It has the potential to replace the manual methods of diagnosing bothdiabetic retinopathy and cardiovascular risk, which are time consuming andcostly processes only done by medical professionals away from the point ofcare, and to prevent irreversible blindness and heart-related complicationsthrough faster, cheaper, and safer monitoring of diabetic complications. Aswell, tracking of cardiovascular and ocular complications of diabetes canenable improved detection of other diabetic complications, leading to earlierand more efficient treatment on a global scale.

Smartphone-Based Test and Predictive Models for Rapid, Non-Invasive, and  Point-of-Care Monitoring of Ocular and Cardiovascular Complications Related  to Diabetes