The deep-learning systems predicted diabetic retinopathy development using colour fundus photographs, and the systems were independent of and more informative than available risk factors. Such a risk stratification tool might help to optimise screening intervals to reduce costs while improving vision-related outcomes.
Nov 26, 2020
We aimed to create a deep-learning system to predict the risk of patients with diabetes developing diabetic retinopathy within 2 years. Methods: We created and ...
Aug 10, 2020 · We created and validated two versions of a deep learning system (DLS) to predict the development of mild-or-worse ("Mild+") DR in diabetic ...
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Jan 4, 2024 · We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images.
Sep 20, 2019 · Here, we describe an algorithm to predict DR progression by means of deep learning (DL), using as input color fundus photographs (CFPs) ...
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Predicting the future development of diabetic retinopathy using a deep ...
bmjophth.bmj.com › content
This research aimed to develop an artificial intelligence (AI) machine learning model which can predict the development of referable DR from fundus imagery of ...
In this study, we created a deep learning system (DLS) that uses color fundus photographs. (CFPs) to predict the development of mild-or-worse (“Mild+”) DR ...
Considerable research effort has recently focused on developing of retinopathy prediction models using machine learning based on individual risk factors, aiming ...
Purpose: Real-world evaluation of a deep learning model that prioritizes patients based on risk of progression to moderate or worse (MOD+) diabetic ...
We created and validated two versions of a deep learning system (DLS) to predict the development of mild-or-worse ("Mild+") DR in diabetic patients undergoing ...