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Found 7 papers, 1 papers with code
Discovering novel systemic biomarkers in photos of the external eye
On validation sets B and C, with substantial patient population differences compared to the development sets, the DLS outperformed the baseline for ACR>=300 and Hgb<11 by 7. 3-13. 2%.
Predicting Risk of Developing Diabetic Retinopathy using Deep Learning
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 DR screening.
Scientific Discovery by Generating Counterfactuals using Image Translation
Model explanation techniques play a critical role in understanding the source of a model's performance and making its decisions transparent.
Detecting Anemia from Retinal Fundus Images
Despite its high prevalence, anemia is often undetected due to the invasiveness and cost of screening and diagnostic tests.
Predicting Progression of Age-related Macular Degeneration from Fundus Images using Deep Learning
For predicting progression specifically from iAMD, the DL algorithm's sensitivity (57+/-6%) was also higher compared to the 9-step grades (36+/-8%) and the 4-category grades (20+/-0%).
Deep learning for predicting refractive error from retinal fundus images
Mean absolute error (MAE) of the algorithm's prediction compared to the refractive error obtained in the AREDS and UK Biobank.
Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning
Traditionally, medical discoveries are made by observing associations and then designing experiments to test these hypotheses.
