Deep Learning for the Discrimination Among Different Types of Keratits: a Nationwide Study

October 25, 2023 updated by: Ningbo Eye Hospital

Deep Learning for the Discrimination Among Bacterial, Fungal, Viral, Amebic and Noninfectious Keratitis: a Nationwide Study

Detecting the cause of keratitis fast is the premise of providing targeted therapy for reducing vision loss and preventing severe complications. Due to overlapping inflammatory features, even expert cornea specialists have relatively poor performance in the identification of causative pathogen of infectious keraitis. In this project, the investigators aim to develop an automated and accurate deep learning system to discriminate among bacterial, fungal, viral, amebic and noninfectious keratitis based on slit-lamp images and evaluated this system using the datasets obtained from mutiple independent clinical centers across China.

Study Overview

Status

Completed

Conditions

Study Type

Observational

Enrollment (Actual)

10369

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

China
- - Wenzhou, China
    - Eye Hospital of Wenzhou Medical University
- Zhejiang
  - Ningbo, Zhejiang, China
    - Ningbo Eye Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

1 week to 100 years (Child, Adult, Older Adult)

Accepts Healthy Volunteers

Sampling Method

Probability Sample

Study Population

Child, Adult, Older Adult

Description

Inclusion Criteria:

Slit-lamp images with sufficient diagnostic certainty and showing keratitis at the active phase.

Exclusion Criteria:

Poor-quality images
Images presenting mixed infections (i.e., cornea infected by two or more causative pathogens)

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Observational Models: Other
Time Perspectives: Other

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Time Frame
Area under the receiver operating characteristic curve of the deep learning system Time Frame: 2020-2022	2020-2022

Secondary Outcome Measures

Outcome Measure	Time Frame
Accuracy of the deep learning system Time Frame: 2020-2022	2020-2022
Sensitivity of the deep learning system Time Frame: 2020-2022	2020-2022
Specificity of the deep learning system Time Frame: 2020-2022	2020-2022

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Ningbo Eye Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

July 1, 2020

Primary Completion (Actual)

September 30, 2023

Study Completion (Actual)

October 20, 2023

Study Registration Dates

First Submitted

September 11, 2022

First Submitted That Met QC Criteria