MyopiaNCT05835115
Development and Validation of a Deep Learning-based Myopia and Myopic Maculopathy Detection and Prediction System
An observational study of Myopia and Myopic Macular Degeneration, run by Shanghai Eye Disease Prevention and Treatment Center at 1 site in China.
Trial summary
Myopia has become a global public health issue. Myopia affects the psychological health of children and adolescents and poses a financial burden. Therefore, early detection and prediction of children at a high risk of myopia development and progression are critical for precise and effective interventions. Read more about this study
- Who is it for?
- Ages 4 to 18 years
- Conditions the study is for
- Myopia, Myopic Macular Degeneration
- What is being tested?
- A deep learning-based myopia and myopic maculopathy detection and prediction system
- How many people
- 30,526 participants
- Where
- 1 siteChina
- Study dates
- 1 Apr 2022to 1 Apr 2023
- 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 4 to 18 years
- All sexes
- No healthy volunteers
- Child, Adult
Study population: The SCALE, a prospective, school-based study, includes all children aged 4 to 14 years in Shanghai. The SCALE-HM, a population-based, prospective, examiner-masked study, includes children and adolescents aged between 4 and 18 years with high myopia. The STORM trial, a school-based, prospective, examiner-masked, cluster-randomized trial, includes children aged 6 to 9 years. The SMS study is a school-based cross-sectional survey from Shanghai, including kindergarten and primary school students in Year 1 and 2. The Beijing Children Eye study included children who came to the outpatient clinic of Beijing Friendship Hospital. The JFFT study contains cross-sectional data from Shanghai, Yunnan, Inner Mongolia, Xinjiang and Guangzhou. The Hong Kong Children Eye Study is a population-based cohort study of eye conditions in children aged 6-8 years.
Who may be able to join
- Subjects with fundus images in the Shanghai Child and Adolescent Large-scale Eye Study (SCALE) ;
- Subjects with fundus images in the Shanghai Time Outside to Reduce Myopia [STORM] trial;
- Subjects with fundus images in the High Myopia Registration Study [SCALE-HM]
- Subjects with fundus images in the Shanghai Myopia Screening (SMS) Study;
- Subjects with fundus images in the Beijing Children Eye Study
- Subjects with fundus images in the First Affiliated Hospital of Kunming Medical University;
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- Subjects with fundus images at the Ophthalmology Department of the First Affiliated Hospital of Xinjiang Medical University;
- Subjects with fundus images at the Ophthalmology Department of the Affiliated Hospital of Inner Mongolia Medical University;
- Subjects with fundus images at Zhongshan Eye Centre, Sun Yat-sen University;
- Subjects with fundus images in the Hong Kong Children Eye Study;
Who cannot join
- Participants with poor-quality fundus images
Trial locations and contacts
1 site in 1 country.
China1 site
Shanghai Eye Disease Prevention and Treatment Center
Shanghai, Shanghai Municipality, 200041
About this study
Myopia has become a global public health issue. Myopia affects the psychological health of children and adolescents and poses a financial burden. Therefore, early detection and prediction of children at a high risk of myopia development and progression are critical for precise and effective interventions. In this study, we developed a deep learning system DeepMyopia, based on fundus images with the following objectives: 1) to predict myopia onset and progression; 2) To detect myopic macular degeneration for AI-assisted diagnosis; 3) To predict the development of myopic macular degeneration; 4) evaluate its cost-effectiveness.
Read the detailed description
Myopia has become a global public health issue. Myopia affects the psychological health of children and adolescents and poses a financial burden. Furthermore, as myopia progresses it increases the risk of ocular complications such as myopic macular degeneration, leading to irreversible visual impairment or even blindness. According to the World Health Organization , more than 1 billion people worldwide are living with vision impairment caused by myopia, hyperopia, and other problems due to late detection. Therefore, early detection and prediction of children at a high risk of myopia development and progression are critical for precise and effective interventions.
In this study, we developed a deep learning system DeepMyopia, based on fundus images with the following objectives: 1) to predict myopia onset and progression; 2) To detect myopic macular degeneration for AI-assisted diagnosis; 3) To predict the development of myopic macular degeneration; 4) evaluate its cost-effectiveness.
- Conditions
- Myopia, Myopic Macular Degeneration
- Being tested
- A deep learning-based myopia and myopic maculopathy detection and prediction system
- Medical subject headings
- Myopia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
A deep learning-based myopia and myopic maculopathy detection and prediction systemDiagnostic test
This deep learning system is capable of analyzing fundus images for myopia staging, myopic maculopathy detection, cycloplegic refraction estimation and prediction, and risk stratification of myopia and myopic maculopathy onset.
| Study group | Role | What they receive |
|---|---|---|
| The training dataset | The training dataset was comprised of data from a school-based, prospective cohort (the Shanghai Time Outside to Reduce Myopia [STORM] trial) and data from another population-based, prospective study, the High Myopia Registration Study (SCALE-HM), with annual follow-up. Participants of the two studies were divided into a training set (70%), a tuning set (10%), and an internal test set (20%), which were not duplicated by each other at the participant level. Receives: Diagnostic Test: A deep learning-based myopia and myopic maculopathy detection and prediction system | |
| The internal validation dataset | The internal validation dataset was comprised of data from a school-based, prospective cohort (the Shanghai Time Outside to Reduce Myopia [STORM] trial) and data from another population-based, prospective study, the High Myopia Registration Study (SCALE-HM), with annual follow-up. Participants of the two studies were divided into a training set (70%), a tuning set (10%), and an internal test set (20%), which were not duplicated by each other at the participant level. Receives: Diagnostic Test: A deep learning-based myopia and myopic maculopathy detection and prediction system | |
| The external validation dataset | To test the extrapolation capabilities of the deep learning sysyem, two independent datasets, the Joint Five-site Fundus Test (JFFT) and the Hong Kong Children Eye Study (HKCES), were applied as external test sets. The JFFT study, a multi-site dataset, contains cross-sectional data from Shanghai, Yunnan, Inner Mongolia, Xinjiang and Guangzhou. HKCES, a population-based cohort study of eye conditions in children aged 6-8 years. Receives: Diagnostic Test: A deep learning-based myopia and myopic maculopathy detection and prediction system |
What is being measured
The primary outcome is the main question the study is designed to answer.
myopia staging detection possibility score
output of myopia staging task
Measured: immediately after inputting the data
myopic maculopathy detection possibility score
output of myopic maculopathy detection task
Measured: immediately after inputting the data
predicted spherical equivalent
output of assessing spherical equivalent task
Measured: immediately after inputting the data
predicted future annual spherical equivalent
output of predicting future spherical equivalent task
Measured: immediately after inputting the data
risk score of myopia and myopic maculopathy progression
output of the progression of myopia and myopic maculopathy predicion task
Measured: immediately after inputting the data
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Apr 2023
- Study start1 Apr 2022
- Primary completion1 Apr 2023
- Study completion1 Apr 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for MyopiaStudies of the same condition, open and under way.
- 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
- Retrospective
- Enrolment
- 30,526 participants (actual)
Sponsor and investigators
- Lead sponsor
- Shanghai Eye Disease Prevention and Treatment Center
- Sponsor type
- University, hospital or other
- Collaborators
- Shanghai Jiao Tong University School of Medicine, Beijing Friendship Hospital, Peking Union Medical College Hospital, Zhongshan Ophthalmic Center, Sun Yat-sen University, First Affiliated Hospital of Kunming Medical University, The Affiliated Hospital of Inner Mongolia Medical University, First Affiliated Hospital of Xinjiang Medical University, Chinese University of Hong Kong
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05835115
- Sponsor's protocol ID
- 2022SQ023
- Status last verified
- Apr 2023
- First submitted
- 18 Apr 2023
- Last updated
- 28 Apr 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 28 Apr 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of a Deep Learning-based Myopia and Myopic Maculopathy Detection and Prediction System
- ClinicalTrials.gov ID
- NCT05835115
- Acronym / sponsor ID
- 2022SQ023
- Conditions
- Myopia, Myopic Macular Degeneration
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 30,526 participants (actual)
- Lead sponsor
- Shanghai Eye Disease Prevention and Treatment CenterUniversity, hospital or other
- Collaborators
- Shanghai Jiao Tong University School of Medicine, Beijing Friendship Hospital, Peking Union Medical College Hospital, Zhongshan Ophthalmic Center, Sun Yat-sen University, First Affiliated Hospital of Kunming Medical University, The Affiliated Hospital of Inner Mongolia Medical University, First Affiliated Hospital of Xinjiang Medical University, Chinese University of Hong Kong
- Study start
- 1 Apr 2022 (actual)
- Primary completion
- 1 Apr 2023 (actual)
- Study completion
- 1 Apr 2023 (actual)
- First posted
- 28 Apr 2023 (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.
Myopia has become a global public health issue. Myopia affects the psychological health of children and adolescents and poses a financial burden. Therefore, early detection and prediction of children at a high risk of myopia development and progression are critical for precise and effective interventions. In this study, we developed a deep learning system DeepMyopia, based on fundus images with the following objectives: 1) to predict myopia onset and progression; 2) To detect myopic macular degeneration for AI-assisted diagnosis; 3) To predict the development of myopic macular degeneration; 4) evaluate its cost-effectiveness.
Detailed description
Myopia has become a global public health issue. Myopia affects the psychological health of children and adolescents and poses a financial burden. Furthermore, as myopia progresses it increases the risk of ocular complications such as myopic macular degeneration, leading to irreversible visual impairment or even blindness. According to the World Health Organization , more than 1 billion people worldwide are living with vision impairment caused by myopia, hyperopia, and other problems due to late detection. Therefore, early detection and prediction of children at a high risk of myopia development and progression are critical for precise and effective interventions.
In this study, we developed a deep learning system DeepMyopia, based on fundus images with the following objectives: 1) to predict myopia onset and progression; 2) To detect myopic macular degeneration for AI-assisted diagnosis; 3) To predict the development of myopic macular degeneration; 4) evaluate its cost-effectiveness.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| The training dataset | – | Diagnostic Test: A deep learning-based myopia and myopic maculopathy detection and prediction systemThe training dataset was comprised of data from a school-based, prospective cohort (the Shanghai Time Outside to Reduce Myopia [STORM] trial) and data from another population-based, prospective study, the High Myopia Registration Study (SCALE-HM), with annual follow-up. Participants of the two studies were divided into a training set (70%), a tuning set (10%), and an internal test set (20%), which were not duplicated by each other at the participant level. |
| The internal validation dataset | – | Diagnostic Test: A deep learning-based myopia and myopic maculopathy detection and prediction systemThe internal validation dataset was comprised of data from a school-based, prospective cohort (the Shanghai Time Outside to Reduce Myopia [STORM] trial) and data from another population-based, prospective study, the High Myopia Registration Study (SCALE-HM), with annual follow-up. Participants of the two studies were divided into a training set (70%), a tuning set (10%), and an internal test set (20%), which were not duplicated by each other at the participant level. |
| The external validation dataset | – | Diagnostic Test: A deep learning-based myopia and myopic maculopathy detection and prediction systemTo test the extrapolation capabilities of the deep learning sysyem, two independent datasets, the Joint Five-site Fundus Test (JFFT) and the Hong Kong Children Eye Study (HKCES), were applied as external test sets. The JFFT study, a multi-site dataset, contains cross-sectional data from Shanghai, Yunnan, Inner Mongolia, Xinjiang and Guangzhou. HKCES, a population-based cohort study of eye conditions in children aged 6-8 years. |
Who may qualify
- Ages
- 4 to 18 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult
- Study population
- The SCALE, a prospective, school-based study, includes all children aged 4 to 14 years in Shanghai. The SCALE-HM, a population-based, prospective, examiner-masked study, includes children and adolescents aged between 4 and 18 years with high myopia. The STORM trial, a school-based, prospective, examiner-masked, cluster-randomized trial, includes children aged 6 to 9 years. The SMS study is a school-based cross-sectional survey from Shanghai, including kindergarten and primary school students in Year 1 and 2. The Beijing Children Eye study included children who came to the outpatient clinic of Beijing Friendship Hospital. The JFFT study contains cross-sectional data from Shanghai, Yunnan, Inner Mongolia, Xinjiang and Guangzhou. The Hong Kong Children Eye Study is a population-based cohort study of eye conditions in children aged 6-8 years.
Key inclusion criteria (10 listed)
- Subjects with fundus images in the Shanghai Child and Adolescent Large-scale Eye Study (SCALE) ;
- Subjects with fundus images in the Shanghai Time Outside to Reduce Myopia [STORM] trial;
- Subjects with fundus images in the High Myopia Registration Study [SCALE-HM]
- Subjects with fundus images in the Shanghai Myopia Screening (SMS) Study;
- Subjects with fundus images in the Beijing Children Eye Study
Key exclusion criteria (1 listed)
- Participants with poor-quality fundus images
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 |
|---|---|---|
| A deep learning-based myopia and myopic maculopathy detection and prediction systemThis deep learning system is capable of analyzing fundus images for myopia staging, myopic maculopathy detection, cycloplegic refraction estimation and prediction, and risk stratification of myopia and myopic maculopathy onset. | Diagnostic test | The external validation dataset, The internal validation dataset, The training dataset |
Primary outcome
myopia staging detection possibility score
Time frame: immediately after inputting the data
output of myopia staging task
myopic maculopathy detection possibility score
Time frame: immediately after inputting the data
output of myopic maculopathy detection task
predicted spherical equivalent
Time frame: immediately after inputting the data
output of assessing spherical equivalent task
predicted future annual spherical equivalent
Time frame: immediately after inputting the data
output of predicting future spherical equivalent task
risk score of myopia and myopic maculopathy progression
Time frame: immediately after inputting the data
output of the progression of myopia and myopic maculopathy predicion task
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 28 Apr 2023.
- Individual participant dataSharing planned: No
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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 Myopia and Myopic Macular Degeneration, run by Shanghai Eye Disease Prevention and Treatment Center 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 10 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: finished, as of the sponsor's last update on 28 Apr 2023. 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?
myopia staging detection possibility score (time frame: immediately after inputting the data). The study lists 5 primary outcomes in all. 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 28 Apr 2023; AzTrials last refreshed its copy on 28 Oct 2026.