NCT05538793

Brief Summary

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.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
10,369

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Jul 2020

Typical duration for all trials

Geographic Reach
1 country

2 active sites

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

July 1, 2020

Completed
2.2 years until next milestone

First Submitted

Initial submission to the registry

September 11, 2022

Completed
3 days until next milestone

First Posted

Study publicly available on registry

September 14, 2022

Completed
1 year until next milestone

Primary Completion

Last participant's last visit for primary outcome

September 30, 2023

Completed
20 days until next milestone

Study Completion

Last participant's last visit for all outcomes

October 20, 2023

Completed
Last Updated

October 27, 2023

Status Verified

October 1, 2023

Enrollment Period

3.2 years

First QC Date

September 11, 2022

Last Update Submit

October 25, 2023

Conditions

KeratitisAutomatic JudgementImage

Keywords

KeratitisSlit-lamp ImageDeep Learning

Outcome Measures

Primary Outcomes (1)

  • Area under the receiver operating characteristic curve of the deep learning system

    2020-2022

Secondary Outcomes (3)

  • Accuracy of the deep learning system

    2020-2022

  • Sensitivity of the deep learning system

    2020-2022

  • Specificity of the deep learning system

    2020-2022

Eligibility Criteria

Age1 Week - 100 Years
Sexall
Healthy VolunteersNo
Age GroupsChild (0-17), Adult (18-64), Older Adult (65+)
Sampling MethodProbability Sample
Study Population

Child, Adult, Older Adult

You may qualify if:

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

You may not qualify if:

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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (2)

Ningbo Eye Hospital

Ningbo, Zhejiang, China

Location

Eye Hospital of Wenzhou Medical University

Wenzhou, China

Location

MeSH Terms

Conditions

Keratitis

Condition Hierarchy (Ancestors)

Corneal DiseasesEye Diseases

Study Design

Study Type
observational
Observational Model
OTHER
Time Perspective
OTHER
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

September 11, 2022

First Posted

September 14, 2022

Study Start

July 1, 2020

Primary Completion

September 30, 2023

Study Completion

October 20, 2023

Last Updated

October 27, 2023

Record last verified: 2023-10

Locations

CN(2)