Automatic Judgement, Image, Keratitis
Conditions
Keywords
Keratitis, Slit-lamp Image, Deep Learning
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.
Interventions
None listed
Sponsors
Ningbo Eye Hospital
Study design
Observational model
OTHER
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
1 Weeks to 100 Years
Healthy volunteers
No
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)
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Area under the receiver operating characteristic curve of the deep learning system | 2020-2022 |
Secondary
| Measure | Time frame |
|---|---|
| Accuracy of the deep learning system | 2020-2022 |
| Sensitivity of the deep learning system | 2020-2022 |
| Specificity of the deep learning system | 2020-2022 |
Countries
China
Outcome results
None listed