DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity.

ABSTRACT:

Purpose

To develop deep learning models to perform automated diagnosis and quantitative classification of age-related cataract from anterior segment photographs.

Design

DeepLensNet was trained by applying deep learning models to the Age-Related Eye Disease Study (AREDS) dataset.

Participants

A total of 18 999 photographs (6333 triplets) from longitudinal follow-up of 1137 eyes (576 AREDS participants).

Methods

Deep learning models were trained to detect and quantify nuclear sclerosis (NS; scale 0.9-7.1) from 45-degree slit-lamp photographs and cortical lens opacity (CLO; scale 0%-100%) and posterior subcapsular cataract (PSC; scale 0%-100%) from retroillumination photographs. DeepLensNet performance was compared with that of 14 ophthalmologists and 24 medical s

SUBMITTER: Keenan TDL 

PROVIDER: S-EPMC9038670 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature