Artificial IntelligenceNCT04859634
Real-time Artificial Intelligence System for Detecting Multiple Ocular Fundus Lesions by Ultra-widefield Fundus Imaging
An observational study of Artificial Intelligence and Diagnostic Imaging, run by Sun Yat-sen University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence, Diagnostic Imaging, Abnormality of the Fundus
- What is being tested?
- Taking an ultra-widefield fundus image
- Type of treatment
- Medical device
- How many people
- 2,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Nov 2020to 25 Dec 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: All the participants who agree to take ultra-widefield fundus images.
Who may be able to join
- All the participants who agree to take ultra-widefield fundus images.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Zhongshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong, 510060
Haotian Lin, M.D., Ph.D, 8613802793086, haot.lin@hotmail.com
Xiaohang Wu, M.D., Ph.D, 8615913177657, wuxiaohang_zoc@qq.com
About this study
Official title: Real-time Artificial Intelligence System for Detecting Multiple Ocular Fundus Lesions by Ultra-widefield Fundus Imaging: A Prospective Multicenter Study
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.
Read the 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.
- Conditions
- Artificial Intelligence, Diagnostic Imaging, Abnormality of the Fundus, Diagnostic Screening Programs
- Being tested
- Taking an ultra-widefield fundus image
- Keywords
- Artificial Intelligence, Deep learning, Ultra-widefield Fundus Imaging, Ocular Fundus Lesions, Diagnostic Screening Programs
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Taking an ultra-widefield fundus imageDevice
The participant only needs to take an ultra-widefield fundus image as usual.
| Study group | Role | What they receive |
|---|---|---|
| Zhongshan Ophthalmic Center | The participant only needs to take an ultra-widefield fundus image as usual. Receives: Device: Taking an ultra-widefield fundus image | |
| Shenzhen Ophthalmic Center | The participant only needs to take an ultra-widefield fundus image as usual. Receives: Device: Taking an ultra-widefield fundus image | |
| Beijin Tongren Hospital | The participant only needs to take an ultra-widefield fundus image as usual. Receives: Device: Taking an ultra-widefield fundus image | |
| Xudong Ophthalmic Center | The participant only needs to take an ultra-widefield fundus image as usual. Receives: Device: Taking an ultra-widefield fundus image | |
| IKang Physical Examination Center | The participant only needs to take an ultra-widefield fundus image as usual. Receives: Device: Taking an ultra-widefield fundus image | |
| Yangxi General Hospital People's Hospital | The participant only needs to take an ultra-widefield fundus image as usual. Receives: Device: Taking an ultra-widefield fundus image | |
| Guangdong Provincial People's Hospital | The participant only needs to take an ultra-widefield fundus image as usual. Receives: Device: Taking an ultra-widefield fundus image |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy
Performance of artificial intelligence system for detecting multiple ocular fundus lesions based on ultra-widefield fundus imaging.
Measured: 8 months
Secondary outcomes (6)
Sensitivity
Performance of artificial intelligence system for detecting multiple ocular fundus lesions based on ultra-widefield fundus imaging.
Measured: 8 months
Specificity
Performance of artificial intelligence system for detecting multiple ocular fundus lesions based on ultra-widefield fundus imaging.
Measured: 8 months
Cohen's kappa coefficient
The comparison between the performacne of AI system and ophthalmologists of three degrees of expertise.
Measured: 8 months
False-positive rate
Features of Misclassification
Measured: 8 months
False-negative rate
Features of Misclassification
Measured: 8 months
Data processing time of AI system
Data processing time of AI system.
Measured: 8 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Apr 2021
- Study start1 Nov 2020
- Primary completion1 Feb 2022 (estimated)
- Study completion25 Dec 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artificial IntelligenceStudies of the same condition, open and under way.
- More trials run by Sun Yat-sen UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Collaborators
- Shenzhen Eye Hospital, Xudong Ophthalmic Hospital, IKang Physical Examination Center, Beijing Tongren Hospital, Guangdong Provincial People's Hospital, Yangxi General Hospital People's Hospital
- Responsible party
- Principal investigator, Haotian Lin, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04859634
- Sponsor's protocol ID
- UWFAIDS2019-China-06
- Status last verified
- Apr 2021
- First submitted
- 15 Apr 2021
- Last updated
- 26 Apr 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 26 Apr 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Real-time Artificial Intelligence System for Detecting Multiple Ocular Fundus Lesions by Ultra-widefield Fundus Imaging: A Prospective Multicenter Study
- ClinicalTrials.gov ID
- NCT04859634
- Acronym / sponsor ID
- UWFAIDS2019-China-06
- Conditions
- Artificial Intelligence, Diagnostic Imaging, Abnormality of the Fundus, Diagnostic Screening Programs
- Keywords
- Artificial Intelligence, Deep learning, Ultra-widefield Fundus Imaging, Ocular Fundus Lesions, Diagnostic Screening Programs
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Sun Yat-sen UniversityUniversity, hospital or other
- Collaborators
- Shenzhen Eye Hospital, Xudong Ophthalmic Hospital, IKang Physical Examination Center, Beijing Tongren Hospital, Guangdong Provincial People's Hospital, Yangxi General Hospital People's Hospital
- Study start
- 1 Nov 2020 (actual)
- Primary completion
- 1 Feb 2022 (estimated)
- Study completion
- 25 Dec 2022 (estimated)
- First posted
- 26 Apr 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 7
| Arm | Type | Receives |
|---|---|---|
| Zhongshan Ophthalmic Center | – | Device: Taking an ultra-widefield fundus imageThe participant only needs to take an ultra-widefield fundus image as usual. |
| Shenzhen Ophthalmic Center | – | Device: Taking an ultra-widefield fundus imageThe participant only needs to take an ultra-widefield fundus image as usual. |
| Beijin Tongren Hospital | – | Device: Taking an ultra-widefield fundus imageThe participant only needs to take an ultra-widefield fundus image as usual. |
| Xudong Ophthalmic Center | – | Device: Taking an ultra-widefield fundus imageThe participant only needs to take an ultra-widefield fundus image as usual. |
| IKang Physical Examination Center | – | Device: Taking an ultra-widefield fundus imageThe participant only needs to take an ultra-widefield fundus image as usual. |
| Yangxi General Hospital People's Hospital | – | Device: Taking an ultra-widefield fundus imageThe participant only needs to take an ultra-widefield fundus image as usual. |
| Guangdong Provincial People's Hospital | – | Device: Taking an ultra-widefield fundus imageThe participant only needs to take an ultra-widefield fundus image as usual. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- All the participants who agree to take ultra-widefield fundus images.
Key inclusion criteria (1 listed)
- All the participants who agree to take ultra-widefield fundus images.
Key exclusion criteria (2 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Taking an ultra-widefield fundus imageThe participant only needs to take an ultra-widefield fundus image as usual. | Device | Beijin Tongren Hospital, Guangdong Provincial People's Hospital, IKang Physical Examination Center, Shenzhen Ophthalmic Center, Xudong Ophthalmic Center, Yangxi General Hospital People's Hospital, Zhongshan Ophthalmic Center |
Primary outcome
Accuracy
Time frame: 8 months
Performance of artificial intelligence system for detecting multiple ocular fundus lesions based on ultra-widefield fundus imaging.
6 secondary outcomes
Sensitivity
Time frame: 8 months
Specificity
Time frame: 8 months
Cohen's kappa coefficient
Time frame: 8 months
False-positive rate
Time frame: 8 months
False-negative rate
Time frame: 8 months
Data processing time of AI system
Time frame: 8 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Apr 2021.
Related trials for Artificial Intelligence
Enrolling by invitationObservational study
Digital Phenotypes for Predicting Depression_Validation
Major Depressive Disorder, Bipolar I Disorder
Not yet recruiting
Effect of AI-Based Image Generation on Children's Ecological Footprint Awareness
Environmental Awareness, Ecological Footprint
Active, not recruiting
AI-Assisted Child Health Podcast Education for Nursing Students
Nursing Education
RecruitingObservational study
Patient-Specific Biomechanical Modeling of Incisional Hernias Using Artificial Intelligence and Finite Element Analysis
Incisional Hernia
Not yet recruiting
AI-Driven Video Avatars vs. Customized GPTs: Professional English Learning Among Nursing Students
Artifical Intelligence, Generative Artificial Intelligence
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Artificial Intelligence and Diagnostic Imaging, run by Sun Yat-sen University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 26 Apr 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Haotian Lin, MD, PhD (8613802793086, haot.lin@hotmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy (time frame: 8 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Apr 2021.