NCT04213430

Development and Validation of a Deep Learning System for Multiple Ocular Fundus Diseases Using Retinal Images

Development and Validation of a Deep Learning System for Multiple Ocular Fundus Diseases Using Retinal Images: a Multi-center Prospective Study

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 300,000 (estimated)
• Sponsor: Sun Yat-sen University · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Accepted

Summary

Retinal images can reflect both fundus and systemic conditions (diabetes and cardiovascular disease) and firstly to be used for medical artificial intelligence (AI) algorithm training due to its advantages of clinical significance and easy to obtain. Here, the investigators developed a single network model that can mine the characteristics among multiple fundus diseases, which was trained by plenty of fundus images with one or several disease labels (if they have) in each of them. The model performance was compared with those of both native and international ophthalmologists. The model was further tested by datasets with different camera types and validated by three external datasets prospectively collected from the clinical sites where the model would be applied.

Conditions

• Ophthalmological Disorder

Interventions

Type	Name	Description
OTHER	diagnostic	Training dataset was used to train the deep learning model, which was validated and tested by other two datasets.

Timeline

• Start date: 2014-01-01
• Primary completion: 2020-02-01
• Completion: 2020-05-01
• First posted: 2019-12-30
• Last updated: 2019-12-30

Locations

1 site across 1 country: China