Non-invasive and objective tear film breakup detection on interference color images using convolutional neural networks.

ABSTRACT:

Purpose

Dry eye disease affects hundreds of millions of people worldwide and is one of the most common causes for visits to eye care practitioners. The fluorescein tear breakup time test is currently widely used to diagnose dry eye disease, but it is an invasive and subjective method, thus resulting in variability in diagnostic results. This study aimed to develop an objective method to detect tear breakup using the convolutional neural networks on the tear film images taken by the non-invasive device KOWA DR-1α.

Methods

The image classification models for detecting characteristics of tear film images were constructed using transfer learning of the pre-trained ResNet50 model. The models were trained using a total of 9,089 image patches extracted from video data of 350 eyes o

SUBMITTER: Kikukawa Y 

PROVIDER: S-EPMC10010540 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature