Search Results for author:
Found 9 papers, 1 papers with code
Using generative AI to investigate medical imagery models and datasets
In this paper, we present a method for automatic visual explanations leveraging team-based expertise by generating hypotheses of what visual signals in the images are correlated with the task.
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%.
Large-scale machine learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology
A GWAS of ML-based VCDR identified 299 independent genome-wide significant (GWS; $P\leq5\times10^{-8}$) hits in 156 loci.
Detecting hidden signs of diabetes in external eye photographs
In validation set A (n=27, 415 patients, all undilated), the DLS detected poor blood glucose control (HbA1c > 9%) with an area under receiver operating characteristic curve (AUC) of 70. 2; moderate-or-worse DR with an AUC of 75. 3; diabetic macular edema with an AUC of 78. 0; and vision-threatening DR with an AUC of 79. 4.
Improving Medical Annotation Quality to Decrease Labeling Burden Using Stratified Noisy Cross-Validation
For instance, Noisy Cross-Validation splits the training data into halves, and has been shown to identify low-quality labels in computer vision tasks; but it has not been applied to medical imaging tasks specifically.
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.
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 and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photos
An algorithm trained on fundus images alone can detect referable GON with higher sensitivity than and comparable specificity to eye care providers.
