Machine Learning Predicts Functional Outcomes in DME w/ Daniela Ferrara, MD, PhD

A new analysis, presented at the 2024 Association for Research in Vision and Ophthalmology (ARVO) Meeting, centered around the performance of machine learning models using baseline features to predict functional outcomes across different treatment durations of faricimab, a bispecific antibody, blocking both VEGF-A and Ang-2 molecules, for diabetic macular edema.
In an interview with HCPLive, Daniela Ferrara, MD, PhD, a principal medical director in the ophthalmology imaging program at Genentech, described her thoughts on the potential performance metrics of the machine learning model, particularly the promising accuracy of the visual function response at the 1- and 6-month time points.
“I think the relevance for clinicians like myself is we know that visual function prediction is a challenge clinically,” Ferrara told HCPLive. “In other words, when I have a patient sitting in front of me at the clinic, or a patient enrolling in a clinical trial, based on solely clinical considerations, it’s very difficult to predict what is going to be the vision over time. We’re very excited about seeing that some models were able to make such a prediction.”
Highlights
0:08 Purpose of YOSEMITE and RHINE analysis
1:13 Description of the machine learning models
3:48 Capability of machine learning for DME outcomes
6:42 Next steps to improve the model’s performance
8:52 Opinion on the use of AI in ophthalmology
Check out more ARVO coverage: www.hcplive.com/conference/arvo
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