Real-time Artificial Intelligence System for Detecting Multiple Ocular Fundus Lesions by Ultra-widefield Fundus Imaging
- Conditions
- Diagnostic Screening ProgramsArtificial IntelligenceDiagnostic ImagingAbnormality of the Fundus
- Interventions
- Device: Taking an ultra-widefield fundus image
- Registration Number
- NCT04859634
- Lead Sponsor
- Sun Yat-sen University
- Brief Summary
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
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.
Recruitment & Eligibility
- Status
- UNKNOWN
- Sex
- All
- Target Recruitment
- 2000
All the participants who agree to take ultra-widefield fundus images.
- 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.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description IKang Physical Examination Center Taking an ultra-widefield fundus image The participant only needs to take an ultra-widefield fundus image as usual. Beijin Tongren Hospital Taking an ultra-widefield fundus image The participant only needs to take an ultra-widefield fundus image as usual. Shenzhen Ophthalmic Center Taking an ultra-widefield fundus image The participant only needs to take an ultra-widefield fundus image as usual. Xudong Ophthalmic Center Taking an ultra-widefield fundus image The participant only needs to take an ultra-widefield fundus image as usual. Zhongshan Ophthalmic Center Taking an ultra-widefield fundus image The participant only needs to take an ultra-widefield fundus image as usual. Yangxi General Hospital People's Hospital Taking an ultra-widefield fundus image The participant only needs to take an ultra-widefield fundus image as usual. Guangdong Provincial People's Hospital Taking an ultra-widefield fundus image The participant only needs to take an ultra-widefield fundus image as usual.
- Primary Outcome Measures
Name Time Method Accuracy 8 months Performance of artificial intelligence system for detecting multiple ocular fundus lesions based on ultra-widefield fundus imaging.
- Secondary Outcome Measures
Name Time Method False-positive rate 8 months Features of Misclassification
Sensitivity 8 months Performance of artificial intelligence system for detecting multiple ocular fundus lesions based on ultra-widefield fundus imaging.
Cohen's kappa coefficient 8 months The comparison between the performacne of AI system and ophthalmologists of three degrees of expertise.
Specificity 8 months Performance of artificial intelligence system for detecting multiple ocular fundus lesions based on ultra-widefield fundus imaging.
False-negative rate 8 months Features of Misclassification
Data processing time of AI system 8 months Data processing time of AI system.
Trial Locations
- Locations (1)
Zhongshan Ophthalmic Center, Sun Yat-sen University
🇨🇳Guangzhou, Guangdong, China