Prediction of Functional and Anatomic Progression in Lamellar Macular Holes.

ABSTRACT:

Purpose

To use artificial intelligence to identify imaging biomarkers for anatomic and functional progression of lamellar macular hole (LMH) and elaborate a deep learning (DL) model based on OCT and OCT angiography (OCTA) for prediction of visual acuity (VA) loss in untreated LMHs.

Design

Multicentric retrospective observational study.

Participants

Patients aged >18 years diagnosed with idiopathic LMHs with availability of good quality OCT and OCTA acquisitions at baseline and a follow-up >2 years were recruited.

Methods

A DL model based on soft voting of 2 separate models (OCT and OCTA-based respectively) was trained for identification of cases with VA loss >5 ETDRS letters (attributable to LMH progression only) during a 2-year follow-up. Biomarkers of anatomi

SUBMITTER: Crincoli E 

PROVIDER: S-EPMC11401036 | biostudies-literature | 2024 Nov-Dec

REPOSITORIES: biostudies-literature