Ophthalmological DisorderNCT04213430
Development and Validation of a Deep Learning System for Multiple Ocular Fundus Diseases Using Retinal Images
An observational study of Ophthalmological Disorder, run by Sun Yat-sen University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Ophthalmological Disorder
- What is being tested?
- diagnostic
- How many people
- 300,000 participants
- Where
- 1 siteChina
- Study dates
- Jan 2014to May 2020
- 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: Retinal images were collected from different health care institutes all over China and other countries around the world.
Who may be able to join
- 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.
Who cannot join
- Images with light leakage (>30% of area), spots from lens flares or stains, and overexposure were excluded from further analysis.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
Guangzhou, Guangdong, 510060
Haotian Lin, Ph.D, +86-020-87330274, gddlht@aliyun.com
Haotian Lin, Ph.D
About this study
Official title: Development and Validation of a Deep Learning System for Multiple Ocular Fundus Diseases Using Retinal Images: a Multi-center Prospective Study
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
- Being tested
- diagnostic
- Medical subject headings
- Eye Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
diagnosticOther
Training dataset was used to train the deep learning model, which was validated and tested by other two datasets.
| Study group | Role | What they receive |
|---|---|---|
| Training dataset | Retinal images collected from hospitals and multiple screening sites all over China | |
| Validation dataset | Retinal images separated from training dataset Receives: Other: diagnostic | |
| Testing dataset | Retinal images prospectively collected from the hospitals and ocular disease screening sites totally different from training dataset Receives: Other: diagnostic |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: baseline
Secondary outcomes (2)
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.
Measured: 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.
Measured: baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Dec 2019
- Study startJan 2014
- Primary completionFeb 2020 (estimated)
- Study completionMay 2020 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Ophthalmological DisorderStudies 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
- Other
- Time perspective
- Other
- Enrolment
- 300,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Haotian Lin, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04213430
- Sponsor's protocol ID
- CCPMOH2019- China8
- Status last verified
- Dec 2019
- First submitted
- 23 Dec 2019
- Last updated
- 30 Dec 2019
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Lin D, Xiong J, Liu C, Zhao L, Li Z, Yu S, Wu X, Ge Z, Hu X, Wang B, Fu M, Zhao X, Wang X, Zhu Y, Chen C, Li T, Li Y, Wei W, Zhao M, Li J, Xu F, Ding L, Tan G, Xiang Y, Hu Y, Zhang P, Han Y, Li JO, Wei L, Zhu P, Liu Y, Chen W, Ting DSW, Wong TY, Chen Y, Lin H. Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study. Lancet Digit Health. 2021 Aug;3(8):e486-e495. doi: 10.1016/S2589-7500(21)00086-8.
PubMed 34325853
Study at a glance
From the registry record, last updated by the sponsor on 30 Dec 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of a Deep Learning System for Multiple Ocular Fundus Diseases Using Retinal Images: a Multi-center Prospective Study
- ClinicalTrials.gov ID
- NCT04213430
- Acronym / sponsor ID
- CCPMOH2019- China8
- Conditions
- Ophthalmological Disorder
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 300,000 participants (estimated)
- Lead sponsor
- Sun Yat-sen UniversityUniversity, hospital or other
- Study start
- Jan 2014 (actual)
- Primary completion
- Feb 2020 (estimated)
- Study completion
- May 2020 (estimated)
- First posted
- 30 Dec 2019 (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.
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.
Study design
- Observational model
- Other
- Time perspective
- Other
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Training dataset | – | –Retinal images collected from hospitals and multiple screening sites all over China |
| Validation dataset | – | Other: diagnosticRetinal images separated from training dataset |
| Testing dataset | – | Other: diagnosticRetinal images prospectively collected from the hospitals and ocular disease screening sites totally different from training dataset |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Retinal images were collected from different health care institutes all over China and other countries around the world.
Key inclusion criteria (1 listed)
- 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.
Key exclusion criteria (1 listed)
- Images with light leakage (>30% of area), spots from lens flares or stains, and overexposure were excluded from further analysis.
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 |
|---|---|---|
| diagnosticTraining dataset was used to train the deep learning model, which was validated and tested by other two datasets. | Other | Testing dataset, Validation dataset |
Primary outcome
Area under the receiver operating characteristic curve of the deep learning system
Time frame: baseline
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.
2 secondary outcomes
Sensitivity of the deep learning system
Time frame: baseline
Specificity of the deep learning system
Time frame: baseline
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Dec 2019.
- Derived from this study: PubMed 34325853Lin D, Xiong J, Liu C, Zhao L, Li Z, Yu S, Wu X, Ge Z, Hu X, Wang B, Fu M, Zhao X, Wang X, Zhu Y, Chen C, Li T, Li Y, Wei W, Zhao M, Li J, Xu F, Ding L, Tan G, Xiang Y, Hu Y, Zhang P, Han Y, Li JO, Wei L, Zhu P, Liu Y, Chen W, Ting DSW, Wong TY, Chen Y, Lin H. Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study. Lancet Digit Health. 2021 Aug;3(8):e486-e495. doi: 10.1016/S2589-7500(21)00086-8.
- Individual participant dataSharing planned: No
Related trials for Ophthalmological Disorder
Not yet recruitingPhase 2
A Phase 2 Study to Compare Efficacy and Safety of a Novel Topical Therapy (APT-001, Spinosad 1.8%) in Patients With Blepharitis
Blepharitis
Not yet recruiting
PAUL® Glaucoma Implant Safety and Effectiveness Study
Refractory Glaucoma
Not yet recruitingObservational study
Corneal Biomechanics and Stromal Tissue Characteristics in Patients Undergoing SMILE
Myopia
Active, not recruitingPhase 4
A Study About the Safety of ASP3021 Eye Injections and if They Help People in Japan With Vision Loss From Age-related Macular Degeneration
Geographic Atrophy, Age-Related Macular Degeneration
RecruitingPhase 2
A Proof-Of-Concept (POC) Study of SFG-02 Transdermal Formulation in Participants With Underactive Bladder (UAB)
Participants With Underactive Bladder (UAB)
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 Ophthalmological Disorder, 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 1 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 30 Dec 2019. 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, PhD (13802793086, gddlht@aliyun.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under the receiver operating characteristic curve of the deep learning system (time frame: baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04213430, which also lists 1 linked publication.
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 30 Dec 2019.