Deep Learning-Based Cataract Detection and Grading from Slit-Lamp and Retro-Illumination Photographs: Model Development and Validation Study.

ABSTRACT:

Purpose

To develop and validate an automated deep learning (DL)-based artificial intelligence (AI) platform for diagnosing and grading cataracts using slit-lamp and retroillumination lens photographs based on the Lens Opacities Classification System (LOCS) III.

Design

Cross-sectional study in which a convolutional neural network was trained and tested using photographs of slit-lamp and retroillumination lens photographs.

Participants

One thousand three hundred thirty-five slit-lamp images and 637 retroillumination lens images from 596 patients.

Methods

Slit-lamp and retroillumination lens photographs were graded by 2 trained graders using LOCS III. Image datasets were labeled and divided into training, validation, and test datasets. We trained and validated AI

SUBMITTER: Son KY 

PROVIDER: S-EPMC9559082 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature