University of Wisconsin–Madison

Tag: AI

Prediction of Geographic Atrophy Enlargement using Various Deep Learning Approaches (2023)

Amitha Domalpally, Robert Slater, Mark Banghart, Roomasa Channa, Donald S. Fong, Barbara Blodi Abstract Purpose: Fundus autofluorescence (FAF) imaging is used to monitor geographic atrophy (GA) growth inclinical trials. There is significant variability in the growth rate of GA with studies reporting between 0.5 – 2.6mm/year. Multiple imaging risk factors for rapid enlargement have been …

Influence of Race on Training Data Quality for Artificial Intelligence (AI) Algorithms (2022)

Amitha Domalpally, Rick Voland, Robert Slater, Ellie Corkery, Pamela Vargo, Rebecca Kuhtz, James Reimers, Roomasa Channa, Barbara Blodi Abstract Purpose: Diabetic retinopathy (DR) severity level is evaluated from stereoscopic 7-field color photographs by masked graders at the Wisconsin Reading Center and used as a reference standard for training and validation of AI algorithms. Training data …

Implementation of a Large-Scale Retinal Image Curation Workflow Using Deep Learning Framework (2022)

Rohit Balaji, Jen Heathcote, Robert Slater, Nancy Barrett, Rick Voland, Vesna Tomic, Jared McDonald, Barbara A. Blodi, Amitha Domalpally Abstract Purpose: The development of artificial intelligence (AI) algorithms for analyzing retinal pathologies requires training based on well-organized, labeled images. The goals of this project are to develop an AI model to curate 7-field retinal photographs …

Artificial Intelligence (AI) enabled pre-screening for Diabetic Retinopathy (DR) clinical trials (2022)

Nancy Barrett, Robert Slater, Roomasa Channa, Barbara Blodi, Amitha Domalpally Abstract Purpose: Preventive therapies to reduce progression of non-proliferative DR (NPDR) to proliferative DR are underway. Clinical trial enrollment criteria typically include a baseline DR severity scale (DRSS) of moderately severe (level 47) or severe NPDR (level 53) determined from stereoscopic 7-field retinal color photographs …