MaculopathyNCT03476291
Research of Automated Maculopathy Screening Based on AI Techniques Using OCT Images
An observational study of Maculopathy, run by The First Affiliated Hospital with Nanjing Medical University at 1 site in China.
Trial summary
The investigators expect to develop an algorithm that can interpret OCT images and automated determine whether the macula is normal or not by using OCT image-based deep learning techniques. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Maculopathy
- How many people
- 20,000 participants
- Where
- 1 siteChina
- Study dates
- 30 Jun 2017to 31 Dec 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
- Child, Adult, Older adult
Study population: no age limited, no gender-based
Who may be able to join
- All patients attending the Ophthalmology Department of the First Affiliated Hospital of Nanjing Medical University within 5 years and who received known, clear diagnoses with digital retinal imaging (including OCT, fundus digital photographs and fundus fluorescein angiography, at least with OCT images) as part of their routine clinical care, will be eligible for inclusion in this study.
Who cannot join
- Hardcopy examinations (i.e., photos of paper reports of OCT imaging performed at other hospitals) will be ineligible.
- Data from patients who have previously manually requested that their data should not be shared, even for research purposes in anonymised form, and have informed the Ophthalmology Department of the First Affiliated Hospital of Nanjing Medical University of this desire (even in previously conducted studies or other on-going studies in this hospital), will be excluded, and their data will not be upload to the cloud platform before research begins.
- Data from eyes tamponed with silicone oil or gas (i.e., C3F8) will be ineligible.
- Data with poor image quality, such as incomplete images, inverted images, blurred or cracked images and images with a very weak signal (i.e., vitreous haemorrhage), will be ineligible.
Trial locations and contacts
1 site in 1 country.
China1 site
The First Affiliated Hospital with Nanjing Medical University
Nanjing, Jiangsu, 210029
About this study
Official title: Research of Automated Maculopathy Screening by Optical Coherent Tomography Image-based Deep Learning Techniques
The investigators expect to develop an algorithm that can interpret OCT images and automated determine whether the macula is normal or not by using OCT image-based deep learning techniques. And investigators wish to develop software applications that will help better screen and diagnose macular diseases in resource-limited areas.
Read the detailed description
The investigators will apply deep learning convolutional neural network by using ImageNet for an automated detection of multiple retinal diseases with OCT horizontal B-scans with a high-quality labeled database. Datasets, including training dataset, testing dataset and validation datasets, will be built by ophthalmologists of the First affiliated hospital of Nanjing Medical University according to the standardized annotation guidelines.
- Conditions
- Maculopathy
- Keywords
- macular diseases, screening, OCT, Deep learning, AI
- Medical subject headings
- Macular Degeneration
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Normal | normal macular structure of horizontal OCT B-scans | |
| Abnormal | abnormal macular structure of horizontal OCT B-scans, including many sub-categories of pathological features, like epiretinal membrane, pigment epithelium detachment, ect. |
What is being measured
The primary outcome is the main question the study is designed to answer.
receiver operating characteristic(ROC) curve of the algorithm
It is also called sensitivity curve. The ROC curve shows how sensitive the algorithm model is to automatically detect the desired output.
Measured: approximately 1 year
Area under the ROC curve(AUC)
It shows the operating value of the algorithm model, which can represent the effect of the model.
Measured: approximately 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Mar 2018
- Study start30 Jun 2017
- Primary completion1 Jun 2018 (estimated)
- Study completion31 Dec 2020 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for MaculopathyStudies 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
- Cross-sectional
- Enrolment
- 20,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The First Affiliated Hospital with Nanjing Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Songtao Yuan, doctor, Principal investigator, The First Affiliated Hospital with Nanjing Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03476291
- Sponsor's protocol ID
- JSPH-AIOCT-001
- Status last verified
- Feb 2018
- First submitted
- 26 Feb 2018
- Last updated
- 26 Mar 2018
- Results posted
- No
- Data monitoring committee
- Yes
- 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 26 Mar 2018. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research of Automated Maculopathy Screening by Optical Coherent Tomography Image-based Deep Learning Techniques
- ClinicalTrials.gov ID
- NCT03476291
- Acronym / sponsor ID
- JSPH-AIOCT-001
- Conditions
- Maculopathy
- Keywords
- macular diseases, screening, OCT, Deep learning, AI
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 20,000 participants (estimated)
- Lead sponsor
- The First Affiliated Hospital with Nanjing Medical UniversityUniversity, hospital or other
- Study start
- 30 Jun 2017 (actual)
- Primary completion
- 1 Jun 2018 (estimated)
- Study completion
- 31 Dec 2020 (estimated)
- First posted
- 26 Mar 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 investigators expect to develop an algorithm that can interpret OCT images and automated determine whether the macula is normal or not by using OCT image-based deep learning techniques. And investigators wish to develop software applications that will help better screen and diagnose macular diseases in resource-limited areas.
Detailed description
The investigators will apply deep learning convolutional neural network by using ImageNet for an automated detection of multiple retinal diseases with OCT horizontal B-scans with a high-quality labeled database. Datasets, including training dataset, testing dataset and validation datasets, will be built by ophthalmologists of the First affiliated hospital of Nanjing Medical University according to the standardized annotation guidelines.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Normal | – | –normal macular structure of horizontal OCT B-scans |
| Abnormal | – | –abnormal macular structure of horizontal OCT B-scans, including many sub-categories of pathological features, like epiretinal membrane, pigment epithelium detachment, ect. |
Who may qualify
- Sex
- All
- Age groups
- Child, Adult, Older adult
- Study population
- no age limited, no gender-based
Key inclusion criteria (1 listed)
- All patients attending the Ophthalmology Department of the First Affiliated Hospital of Nanjing Medical University within 5 years and who received known, clear diagnoses with digital retinal imaging (including OCT, fundus digital photographs and fundus fluorescein angiography, at least with OCT images) as part of their routine clinical care, will be eligible for inclusion in this study.
Key exclusion criteria (4 listed)
- Hardcopy examinations (i.e., photos of paper reports of OCT imaging performed at other hospitals) will be ineligible.
- Data from patients who have previously manually requested that their data should not be shared, even for research purposes in anonymised form, and have informed the Ophthalmology Department of the First Affiliated Hospital of Nanjing Medical University of this desire (even in previously conducted studies or other on-going studies in this hospital), will be excluded, and their data will not be upload to the cloud platform before research begins.
- Data from eyes tamponed with silicone oil or gas (i.e., C3F8) will be ineligible.
- Data with poor image quality, such as incomplete images, inverted images, blurred or cracked images and images with a very weak signal (i.e., vitreous haemorrhage), will be ineligible.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
receiver operating characteristic(ROC) curve of the algorithm
Time frame: approximately 1 year
It is also called sensitivity curve. The ROC curve shows how sensitive the algorithm model is to automatically detect the desired output.
Area under the ROC curve(AUC)
Time frame: approximately 1 year
It shows the operating value of the algorithm model, which can represent the effect of the model.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Mar 2018.
- Individual participant dataSharing planned: No
Related trials for Maculopathy
RecruitingObservational study
OCTA Evaluated the Efficacy of Glucocorticoid Retrobulbar Injection in Retinal Photo-injury
Retinal Light Damage, Photic Retinopathy
Recruiting
Predictors of Vitrectomy Outcomes in Myopic Foveoschisis
Myopic Traction Maculopathy
RecruitingPhase 1
Phase 1 Study of C.001 in Retinal Degeneration
Geographic Atrophy, Stargardt Disease
Recruiting
Stem Cell Ophthalmology Treatment Study II
Retinal Disease, Age-Related Macular Degeneration
Recruiting
AVIGA3 (Cohort 2.0)
Maculopathy
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.
Songtao Yuan, doctor principal investigator
The First Affiliated Hospital with Nanjing Medical University
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 Maculopathy, run by The First Affiliated Hospital with Nanjing Medical 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 4 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 Mar 2018. 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?
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?
receiver operating characteristic(ROC) curve of the algorithm (time frame: approximately 1 year). The study lists 2 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 26 Mar 2018; AzTrials last refreshed its copy on 30 Oct 2026.