Machine Learning to Predict Faricimab Treatment Outcome in Neovascular Age-Related Macular Degeneration.

ABSTRACT:

Purpose

To develop machine learning (ML) models to predict, at baseline, treatment outcomes at month 9 in patients with neovascular age-related macular degeneration (nAMD) receiving faricimab.

Design

Retrospective proof of concept study.

Participants

Patients enrolled in the phase II AVENUE trial (NCT02484690) of faricimab in nAMD.

Methods

Baseline characteristics and spectral domain-OCT (SD-OCT) image data from 185 faricimab-treated eyes were split into 80% training and 20% test sets at the patient level. Input variables were baseline age, sex, best-corrected visual acuity (BCVA), central subfield thickness (CST), low luminance deficit, treatment arm, and SD-OCT images. A regression problem (BCVA) and a binary classification problem (reduction of CST by 35%) w

SUBMITTER: Kikuchi Y 

PROVIDER: S-EPMC10585644 | biostudies-literature | 2024 Mar-Apr

REPOSITORIES: biostudies-literature