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

NCT ID: NCT05538793

Last Updated: 2023-10-27

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

10369 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-07-01

Study Completion Date

2023-10-20

Brief Summary

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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.

Detailed Description

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Conditions

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Keratitis Automatic Judgement Image

Study Design

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Observational Model Type

OTHER

Study Time Perspective

OTHER

Eligibility Criteria

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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)
Minimum Eligible Age

1 Week

Maximum Eligible Age

100 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Ningbo Eye Hospital

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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Ningbo Eye Hospital

Ningbo, Zhejiang, China

Site Status

Eye Hospital of Wenzhou Medical University

Wenzhou, , China

Site Status

Countries

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China

Other Identifiers

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NEH2022091015

Identifier Type: -

Identifier Source: org_study_id

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