Predicting subretinal fluid absorption with machine learning in patients with central serous chorioretinopathy.

ABSTRACT:

Background

Machine learning was used to predict subretinal fluid absorption (SFA) at 1, 3 and 6 months after laser treatment in patients with central serous chorioretinopathy (CSC).

Methods

The clinical and imaging data from 480 eyes of 461 patients with CSC were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The data included clinical features from electronic medical records and measured features from fundus fluorescein angiography (FFA), indocyanine green angiography (ICGA), optical coherence tomography angiography (OCTA), and optical coherence tomography (OCT). A ZOC dataset was used for training and internal validation. An XEC dataset was used for external validation. Six machine learning algorithms and a blending algorithm were trained to pr

SUBMITTER: Xu F 

PROVIDER: S-EPMC7940879 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature