OphthalmopathyNCT03499145
Validation of the Utility of Ophthalmology Intelligent Diagnostic System
An observational study of Ophthalmopathy and Artificial Intelligence, run by Sun Yat-sen University at 1 site in China.
Trial summary
The prevention and treatment of diseases via artificial intelligence represents an ultimate goal in computational medicine. Application scenarios of the current medical algorithms are too simple to be generally applied to real-world complex clinical settings. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Ophthalmopathy, Artificial Intelligence
- What is being tested?
- Ophthalmology diagnostic system.
- Type of treatment
- Medical device
- How many people
- 615 participants
- Where
- 1 siteChina
- Study dates
- 1 Apr 2018to 31 Aug 2019
- 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: A prospective study of patients and residents who use the web platform for diagnosis.
Who may be able to join
- Patients and residents who underwent ophthalmic examination of the eye and recorded their ocular information in the outpatient clinic and community.
Who cannot join
None listed.
Trial locations and contacts
1 site in 1 country.
China1 site
Zhongshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong, 510000
About this study
Official title: Validation of the Utility of Ophthalmology Intelligent Diagnostic System: A Clinical Trial
The prevention and treatment of diseases via artificial intelligence represents an ultimate goal in computational medicine. Application scenarios of the current medical algorithms are too simple to be generally applied to real-world complex clinical settings. Here, the investigators use "deep learning" and "visionome technique", an novel annotation method for artificial intelligence in medical, to create an automatic detection and classification system for four key clinical scenarios: 1) mass screening, 2) comprehensive clinical triage, 3) hyperfine diagnostic assessment, and 4) multi-path treatment planning. The investigator also establish a telemedicine system and conduct clinical trial and website-based study to validate its versatility.
- Conditions
- Ophthalmopathy, Artificial Intelligence
- Being tested
- Ophthalmology diagnostic system.
- Keywords
- conjunctivitis, keratitis, pterygium, cataracts
- Medical subject headings
- Eye Diseases, Conjunctivitis, Keratitis, Pterygium, Cataract
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Ophthalmology diagnostic system.Device
An artificial intelligence to make comprehensive evaluation and treatment decision of ocular diseases.
| Study group | Role | What they receive |
|---|---|---|
| Eligible patients for AI test. | Device: ophthalmology diagnostic system. An artificial intelligence to make comprehensive evaluation and treatment decision of ocular diseases. Receives: Device: Ophthalmology diagnostic system. |
What is being measured
The primary outcome is the main question the study is designed to answer.
The proportion of accurate, mistaken and miss detection of the ophthalmology diagnostic system.
Measured: Up to 5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Apr 2018
- Study start1 Apr 2018
- Primary completion31 Aug 2019
- Study completion31 Aug 2019
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 OphthalmopathyStudies 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
- 615 participants (actual)
Sponsor and investigators
- Lead sponsor
- Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Collaborators
- Ministry of Health, China, Xidian University
- Responsible party
- Principal investigator, Haotian Lin, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03499145
- Sponsor's protocol ID
- CCPMOH2018-China-2
- Status last verified
- Oct 2019
- First submitted
- 11 Apr 2018
- Last updated
- 21 Oct 2019
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Li W, Yang Y, Zhang K, Long E, He L, Zhang L, Zhu Y, Chen C, Liu Z, Wu X, Yun D, Lv J, Liu Y, Liu X, Lin H. Dense anatomical annotation of slit-lamp images improves the performance of deep learning for the diagnosis of ophthalmic disorders. Nat Biomed Eng. 2020 Aug;4(8):767-777. doi: 10.1038/s41551-020-0577-y. Epub 2020 Jun 22.
PubMed 32572198
Study at a glance
From the registry record, last updated by the sponsor on 21 Oct 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validation of the Utility of Ophthalmology Intelligent Diagnostic System: A Clinical Trial
- ClinicalTrials.gov ID
- NCT03499145
- Acronym / sponsor ID
- CCPMOH2018-China-2
- Conditions
- Ophthalmopathy, Artificial Intelligence
- Keywords
- conjunctivitis, keratitis, pterygium, cataracts
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 615 participants (actual)
- Lead sponsor
- Sun Yat-sen UniversityUniversity, hospital or other
- Collaborators
- Ministry of Health, China, Xidian University
- Study start
- 1 Apr 2018 (actual)
- Primary completion
- 31 Aug 2019 (actual)
- Study completion
- 31 Aug 2019 (actual)
- First posted
- 17 Apr 2018 (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.
The prevention and treatment of diseases via artificial intelligence represents an ultimate goal in computational medicine. Application scenarios of the current medical algorithms are too simple to be generally applied to real-world complex clinical settings. Here, the investigators use "deep learning" and "visionome technique", an novel annotation method for artificial intelligence in medical, to create an automatic detection and classification system for four key clinical scenarios: 1) mass screening, 2) comprehensive clinical triage, 3) hyperfine diagnostic assessment, and 4) multi-path treatment planning. The investigator also establish a telemedicine system and conduct clinical trial and website-based study to validate its versatility.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Eligible patients for AI test. | – | Device: Ophthalmology diagnostic system.Device: ophthalmology diagnostic system. An artificial intelligence to make comprehensive evaluation and treatment decision of ocular diseases. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- A prospective study of patients and residents who use the web platform for diagnosis.
Key inclusion criteria (1 listed)
- Patients and residents who underwent ophthalmic examination of the eye and recorded their ocular information in the outpatient clinic and community.
Key exclusion criteria (0 listed)
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 |
|---|---|---|
| Ophthalmology diagnostic system.An artificial intelligence to make comprehensive evaluation and treatment decision of ocular diseases. | Device | Eligible patients for AI test. |
Primary outcome
The proportion of accurate, mistaken and miss detection of the ophthalmology diagnostic system.
Time frame: Up to 5 years
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Oct 2019.
- Derived from this study: PubMed 32572198Li W, Yang Y, Zhang K, Long E, He L, Zhang L, Zhu Y, Chen C, Liu Z, Wu X, Yun D, Lv J, Liu Y, Liu X, Lin H. Dense anatomical annotation of slit-lamp images improves the performance of deep learning for the diagnosis of ophthalmic disorders. Nat Biomed Eng. 2020 Aug;4(8):767-777. doi: 10.1038/s41551-020-0577-y. Epub 2020 Jun 22.
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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.
The record lists no contact. Use the official record to reach the sponsor.
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 Ophthalmopathy and Artificial Intelligence, 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 0 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: finished, as of the sponsor's last update on 21 Oct 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The proportion of accurate, mistaken and miss detection of the ophthalmology diagnostic system. (time frame: Up to 5 years). 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 NCT03499145, 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 21 Oct 2019.