Deep Learning for the Discrimination Among Different Types of Keratits: a Nationwide Study
- Conditions
- ImageAutomatic JudgementKeratitis
- Registration Number
- NCT05538793
- Lead Sponsor
- Ningbo Eye Hospital
- 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.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 10369
Slit-lamp images with sufficient diagnostic certainty and showing keratitis at the active phase.
- Poor-quality images
- Images presenting mixed infections (i.e., cornea infected by two or more causative pathogens)
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Area under the receiver operating characteristic curve of the deep learning system 2020-2022
- Secondary Outcome Measures
Name Time Method Accuracy of the deep learning system 2020-2022 Specificity of the deep learning system 2020-2022 Sensitivity of the deep learning system 2020-2022
Trial Locations
- Locations (2)
Ningbo Eye Hospital
🇨🇳Ningbo, Zhejiang, China
Eye Hospital of Wenzhou Medical University
🇨🇳Wenzhou, China