OphthalmologyNCT04892316
Using Machine Learning to Adapt Visual Aids for Patients With Low Vision
An observational study of Ophthalmology and Artificial Intelligence, run by Sun Yat-sen University at 1 site in China.
Trial summary
According to the WHO's definition of visual impairment, as of 2018, there were approximately 1.3 billion people with visual impairment in the world, and only 10% of countries can provide assisting services for the rehabilitation of visual impairment. Read more about this study
- Who is it for?
- Ages 3 to 105 years
- Conditions the study is for
- Ophthalmology, Artificial Intelligence, Low Vision Aids
- What is being tested?
- Diagnostic test
- How many people
- 400 participants
- Where
- 1 siteChina
- Study dates
- 27 Jul 2020to 27 Dec 2021
- 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 3 to 105 years
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Visually disabled patients were referred by the Town Disability Federation in Fujian Province and Guangdong Province
Who may be able to join
- Low vision
- Aged 3 to 105
Who cannot join
- Severe systemic disease
- Failure to sign informed consent or unwilling to participate
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
2nd Affilliated Hospital of Fujian Medical University
Quanzhou, Fujian, 362000
Jianmin Hu, M.D., Ph.D., +8615359595888, doctorhjm@163.com
About this study
According to the WHO's definition of visual impairment, as of 2018, there were approximately 1.3 billion people with visual impairment in the world, and only 10% of countries can provide assisting services for the rehabilitation of visual impairment. Although China is one of the countries that can provide rehabilitation services for patients with visual impairment, due to restrictions on the number of professionals in various regions, uneven diagnosis and treatment, and regional differences in economic conditions, not all visually impaired patients can get the rehabilitation of assisting device fitting.
Traditional statistical methods were not enough to solve the problem of intelligent fitting of assisting devices. At present, there are almost no intelligent fitting models of assisting devices in the world. Therefore, in order to allow more low-vision patients to receive accurate and rapid rehabilitation services, we conducted a cross-sectional study on the assisting devices fitting for low-vision patients in Fujian Province, China in the past five years, and at the same time constructed a machine learning model to intelligently predict the adaptation result of the basic assisting devices for low vision patients.
- Conditions
- Ophthalmology, Artificial Intelligence, Low Vision Aids
- Being tested
- Diagnostic test
- Keywords
- Artificial Intelligence, Low Vision
- Medical subject headings
- Vision, Low
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Diagnostic testDiagnostic test
The training dataset was used to train the model, which was validated and tested by the other two datasets.
| Study group | Role | What they receive |
|---|---|---|
| Junior doctor group | Patients receive assisting devices fitting services from junior doctors Receives: Diagnostic Test: Diagnostic test | |
| Senior doctor group | Patients receive assisting devices fitting services from senior doctors Receives: Diagnostic Test: Diagnostic test | |
| Algorithm assisted group | Patients receive assisting devices fitting services from junior doctors assisted by the machine learning model Receives: Diagnostic Test: Diagnostic test |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of fitting results for assisting devices
The investigator will calculate the accuracy of fitting results for assisting devices in different group according to the ground truth.
Measured: baseline
Secondary outcomes (1)
Time cost for fitting assisting devices
The investigator will calculate time cost for fitting assisting devices in different group.
Measured: baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 May 2021
- Study start27 Jul 2020
- Primary completion27 Jul 2021 (estimated)
- Study completion27 Dec 2021 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for OphthalmologyStudies 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
- Prospective
- Enrolment
- 400 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
- NCT04892316
- Sponsor's protocol ID
- SFLV-2020
- Status last verified
- May 2021
- First submitted
- 17 May 2021
- Last updated
- 20 May 2021
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 20 May 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Using Machine Learning to Adapt Visual Aids for Patients With Low Vision
- ClinicalTrials.gov ID
- NCT04892316
- Acronym / sponsor ID
- SFLV-2020
- Conditions
- Ophthalmology, Artificial Intelligence, Low Vision Aids
- Keywords
- Artificial Intelligence, Low Vision
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 400 participants (estimated)
- Lead sponsor
- Sun Yat-sen UniversityUniversity, hospital or other
- Study start
- 27 Jul 2020 (actual)
- Primary completion
- 27 Jul 2021 (estimated)
- Study completion
- 27 Dec 2021 (estimated)
- First posted
- 19 May 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.
According to the WHO's definition of visual impairment, as of 2018, there were approximately 1.3 billion people with visual impairment in the world, and only 10% of countries can provide assisting services for the rehabilitation of visual impairment. Although China is one of the countries that can provide rehabilitation services for patients with visual impairment, due to restrictions on the number of professionals in various regions, uneven diagnosis and treatment, and regional differences in economic conditions, not all visually impaired patients can get the rehabilitation of assisting device fitting.
Traditional statistical methods were not enough to solve the problem of intelligent fitting of assisting devices. At present, there are almost no intelligent fitting models of assisting devices in the world. Therefore, in order to allow more low-vision patients to receive accurate and rapid rehabilitation services, we conducted a cross-sectional study on the assisting devices fitting for low-vision patients in Fujian Province, China in the past five years, and at the same time constructed a machine learning model to intelligently predict the adaptation result of the basic assisting devices for low vision patients.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Junior doctor group | – | Diagnostic Test: Diagnostic testPatients receive assisting devices fitting services from junior doctors |
| Senior doctor group | – | Diagnostic Test: Diagnostic testPatients receive assisting devices fitting services from senior doctors |
| Algorithm assisted group | – | Diagnostic Test: Diagnostic testPatients receive assisting devices fitting services from junior doctors assisted by the machine learning model |
Who may qualify
- Ages
- 3 to 105 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Visually disabled patients were referred by the Town Disability Federation in Fujian Province and Guangdong Province
Key inclusion criteria (2 listed)
- Low vision
- Aged 3 to 105
Key exclusion criteria (2 listed)
- Severe systemic disease
- Failure to sign informed consent or unwilling to participate
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 |
|---|---|---|
| Diagnostic testThe training dataset was used to train the model, which was validated and tested by the other two datasets. | Diagnostic test | Algorithm assisted group, Junior doctor group, Senior doctor group |
Primary outcome
Accuracy of fitting results for assisting devices
Time frame: baseline
The investigator will calculate the accuracy of fitting results for assisting devices in different group according to the ground truth.
1 secondary outcome
Time cost for fitting assisting devices
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 20 May 2021.
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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.
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 Ophthalmology 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 2 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 20 May 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 Jianmin Hu, M.D., Ph.D. (+8615359595888, doctorhjm@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of fitting results for assisting devices (time frame: baseline). 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 20 May 2021.