Prediction of fellow eye neovascularization in type 3 macular neovascularization (Retinal angiomatous proliferation) using deep learning.

ABSTRACT:

Purpose

To establish a deep learning artificial intelligence model to predict the risk of long-term fellow eye neovascularization in unilateral type 3 macular neovascularization (MNV).

Methods

This retrospective study included 217 patients (199 in the training/validation of the AI model and 18 in the testing set) with a diagnosis of unilateral type 3 MNV. The purpose of the AI model was to predict fellow eye neovascularization within 24 months after the initial diagnosis. The data used to train the AI model included a baseline fundus image and horizontal/vertical cross-hair scan optical coherence tomography images in the fellow eye. The neural network of this study for AI-learning was based on the visual geometry group with modification. The precision, recall, accuracy, and

SUBMITTER: Yoon WT 

PROVIDER: S-EPMC11524474 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature