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

Study Overview
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Brief Summary
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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.

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

Conditions
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Ophthalmological Disorder

Intervention / Treatment
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* Other: diagnostic


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: The quality of fundus images should clinical acceptable. More than 80% of the fundus image area including four main regions (optic disk, macular, upper and lower retinal vessel archs) are easy to read and discriminate. Exclusion Criteria: Images with light leakage (>30% of area), spots from lens flares or stains, and overexposure were excluded from further analysis.
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Training dataset<br>Retinal images collected from hospitals and multiple screening sites all over China | |
| Validation dataset<br>Retinal images separated from training dataset | Other: diagnostic<br>* Training dataset was used to train the deep learning model, which was validated and tested by other two datasets.<br>|
| Testing dataset<br>Retinal images prospectively collected from the hospitals and ocular disease screening sites totally different from training dataset | Other: diagnostic<br>* Training dataset was used to train the deep learning model, which was validated and tested by other two datasets.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Area under the receiver operating characteristic curve of the deep learning system | The investigators will calculate the area under the receiver operating characteristic curve of deep learning system and compare this index between deep learning system and human doctors. | baseline | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Sensitivity of the deep learning system | The investigators will calculate the sensitivity of deep learning system and compare this index between deep learning system and human doctors. | baseline | 
| Specificity of the deep learning system | The investigators will calculate the specificity of deep learning system and compare this index between deep learning system and human doctors. | baseline |