Trials / Completed
CompletedNCT05538793
Deep Learning for the Discrimination Among Different Types of Keratits: a Nationwide Study
Deep Learning for the Discrimination Among Bacterial, Fungal, Viral, Amebic and Noninfectious Keratitis: a Nationwide Study
- Status
- Completed
- Phase
- —
- Study type
- Observational
- Enrollment
- 10,369 (actual)
- Sponsor
- Ningbo Eye Hospital · Academic / Other
- Sex
- All
- Age
- 1 Week – 100 Years
- Healthy volunteers
- Not accepted
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
Timeline
- Start date
- 2020-07-01
- Primary completion
- 2023-09-30
- Completion
- 2023-10-20
- First posted
- 2022-09-14
- Last updated
- 2023-10-27
Locations
2 sites across 1 country: China
Source: ClinicalTrials.gov record NCT05538793. Inclusion in this directory is not an endorsement.