Real-time Artificial Intelligence System for Detecting Multiple Ocular Fundus Lesions by Ultra-widefield Fundus Imaging

Study Overview
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Brief Summary
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This prospective multicenter study will evaluate the efficacy of a real-time artificial intelligence system for detecting multiple ocular fundus lesions by ultra-widefield fundus imaging in real-world settings.

Detailed Description
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The ocular fundus can show signs of both ocular diseases (e.g., lattice degeneration, retinal detachment and glaucoma) and systemic diseases (e.g., hypertension, diabetes and leukemia). The routine fundus examination is conducive for early detection of these diseases. However, manual conducting fundus examination needs an experienced retina ophthalmologist, and is time-consuming and labor-intensive, which is difficult for its routine implementation on large scale. This study will develop an artificial intelligence system integrating with ultra-widefield fundus imaging to automatically screen for multiple ocular fundus lesions in real time and evaluate its performance in different real-world settings. The efficacy of the system will compare to the final diagnoses of each participant made by experienced ophthalmologists.

Official Title
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Real-time Artificial Intelligence System for Detecting Multiple Ocular Fundus Lesions by Ultra-widefield Fundus Imaging: A Prospective Multicenter Study

Conditions
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Artificial Intelligence, Diagnostic Imaging, Abnormality of the Fundus, Diagnostic Screening Programs

Intervention / Treatment
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* Device: Taking an ultra-widefield fundus image


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: All the participants who agree to take ultra-widefield fundus images. Exclusion Criteria: Patients who cannot cooperate with a photographer such as some paralytics, the patients with dementia and severe psychopaths. Patients who do not agree to sign informed consent.
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 |
| --- | --- |
| Zhongshan Ophthalmic Center<br>The participant only needs to take an ultra-widefield fundus image as usual. | Device: Taking an ultra-widefield fundus image<br>* The participant only needs to take an ultra-widefield fundus image as usual.<br>|
| Shenzhen Ophthalmic Center<br>The participant only needs to take an ultra-widefield fundus image as usual. | Device: Taking an ultra-widefield fundus image<br>* The participant only needs to take an ultra-widefield fundus image as usual.<br>|
| Beijin Tongren Hospital<br>The participant only needs to take an ultra-widefield fundus image as usual. | Device: Taking an ultra-widefield fundus image<br>* The participant only needs to take an ultra-widefield fundus image as usual.<br>|
| Xudong Ophthalmic Center<br>The participant only needs to take an ultra-widefield fundus image as usual. | Device: Taking an ultra-widefield fundus image<br>* The participant only needs to take an ultra-widefield fundus image as usual.<br>|
| IKang Physical Examination Center<br>The participant only needs to take an ultra-widefield fundus image as usual. | Device: Taking an ultra-widefield fundus image<br>* The participant only needs to take an ultra-widefield fundus image as usual.<br>|
| Yangxi General Hospital People's Hospital<br>The participant only needs to take an ultra-widefield fundus image as usual. | Device: Taking an ultra-widefield fundus image<br>* The participant only needs to take an ultra-widefield fundus image as usual.<br>|
| Guangdong Provincial People's Hospital<br>The participant only needs to take an ultra-widefield fundus image as usual. | Device: Taking an ultra-widefield fundus image<br>* The participant only needs to take an ultra-widefield fundus image as usual.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Accuracy | Performance of artificial intelligence system for detecting multiple ocular fundus lesions based on ultra-widefield fundus imaging. | 8 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Sensitivity | Performance of artificial intelligence system for detecting multiple ocular fundus lesions based on ultra-widefield fundus imaging. | 8 months | 
| Specificity | Performance of artificial intelligence system for detecting multiple ocular fundus lesions based on ultra-widefield fundus imaging. | 8 months | 
| Cohen's kappa coefficient | The comparison between the performacne of AI system and ophthalmologists of three degrees of expertise. | 8 months | 
| False-positive rate | Features of Misclassification | 8 months | 
| False-negative rate | Features of Misclassification | 8 months | 
| Data processing time of AI system | Data processing time of AI system. | 8 months | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Artificial Intelligence, Deep learning, Ultra-widefield Fundus Imaging, Ocular Fundus Lesions, Diagnostic Screening Programs