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

NCT05538793 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 10369

Last updated 2023-10-27

No results posted yet for this study

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.

Conditions

  • Keratitis
  • Automatic Judgement
  • Image

Sponsors & Collaborators

  • Ningbo Eye Hospital

    lead OTHER

Eligibility

Min Age
1 Week
Max Age
100 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-07-01
Primary Completion
2023-09-30
Completion
2023-10-20

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05538793 on ClinicalTrials.gov