Keratitis Clinical Trial
Official title:
Deep Learning for the Discrimination Among Bacterial, Fungal, Viral, Amebic and Noninfectious Keratitis: a Nationwide Study
NCT number | NCT05538793 |
Other study ID # | NEH2022091015 |
Secondary ID | |
Status | Completed |
Phase | |
First received | |
Last updated | |
Start date | July 1, 2020 |
Est. completion date | October 20, 2023 |
Verified date | October 2023 |
Source | Ningbo Eye Hospital |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
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.
Status | Completed |
Enrollment | 10369 |
Est. completion date | October 20, 2023 |
Est. primary completion date | September 30, 2023 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 1 Week to 100 Years |
Eligibility | 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) |
Country | Name | City | State |
---|---|---|---|
China | Ningbo Eye Hospital | Ningbo | Zhejiang |
China | Eye Hospital of Wenzhou Medical University | Wenzhou |
Lead Sponsor | Collaborator |
---|---|
Ningbo Eye Hospital |
China,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Area under the receiver operating characteristic curve of the deep learning system | 2020-2022 | ||
Secondary | Accuracy of the deep learning system | 2020-2022 | ||
Secondary | Sensitivity of the deep learning system | 2020-2022 | ||
Secondary | Specificity of the deep learning system | 2020-2022 |
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