Central Serous ChorioretinopathyIDLENCT04665102
Pilot Study on Deep Learning in the Eye
An observational study of Central Serous Chorioretinopathy and Diabetic Retinopathy, run by CRG UZ Brussel.
Trial summary
Deep learning allows you to classify images using a self-learning algorithm. Transfer learning builds on an existing self-learning algorithm to enable image classification with fewer images. In this study, this technique will be applied to different image modalities in different syndromes. Retrospective study design. Read more about this study
- Who is it for?
- Ages 18 to 100 yearsHealthy volunteers can take part
- Conditions the study is for
- Central Serous Chorioretinopathy, Diabetic Retinopathy, Cataract
- What is being tested?
- Image classification using deep learning algorithm
- How many people
- 120 participants
- Where
- No sites listed yet
- Study dates
- 1 Feb 2021to 1 Dec 2022
- 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 18 to 100 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Healthy and non-healthy subjects: extract data out of available images
Who may be able to join
- Availability of images, which allow discrimination.
Who cannot join
- No availability of clear data on disease differentiation
Trial locations and contacts
No sites are listed yet.
About this study
Official title: Validation of a Transfer Learning Deep Learning Algorithm for Image Classification in Multiple Pathologies
Deep learning allows you to classify images using a self-learning algorithm. Transfer learning builds on an existing self-learning algorithm to enable image classification with fewer images. In this study, this technique will be applied to different image modalities in different syndromes. Retrospective study design.
- Being tested
- Image classification using deep learning algorithm
- Medical subject headings
- Central Serous Chorioretinopathy, Diabetic Retinopathy, Cataract
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Image classification using deep learning algorithmOther
Image classification using deep learning algorithm
| Study group | Role | What they receive |
|---|---|---|
| No pathology | Not described | |
| Pathology | Not described Receives: Other: Image classification using deep learning algorithm |
What is being measured
The primary outcome is the main question the study is designed to answer.
Validation of Image classification by transfer learning algorithm
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Dec 2020
- Study start1 Feb 2021 (estimated)
- Primary completion1 Dec 2021 (estimated)
- Study completion1 Dec 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Central Serous ChorioretinopathyStudies 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 120 participants (estimated)
Sponsor and investigators
- Lead sponsor
- CRG UZ Brussel
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Pieter Nelis, CRG UZ Brussel
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04665102
- Sponsor's protocol ID
- IDLE1000
- Acronym
- IDLE
- Status last verified
- Jan 2021
- First submitted
- 7 Dec 2020
- Last updated
- 7 Jan 2021
- Results posted
- No
- Data monitoring committee
- 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 7 Jan 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validation of a Transfer Learning Deep Learning Algorithm for Image Classification in Multiple Pathologies
- ClinicalTrials.gov ID
- NCT04665102
- Acronym / sponsor ID
- IDLE / IDLE1000
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 120 participants (estimated)
- Lead sponsor
- CRG UZ BrusselUniversity, hospital or other
- Study start
- 1 Feb 2021 (estimated)
- Primary completion
- 1 Dec 2021 (estimated)
- Study completion
- 1 Dec 2022 (estimated)
- First posted
- 11 Dec 2020 (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.
Deep learning allows you to classify images using a self-learning algorithm. Transfer learning builds on an existing self-learning algorithm to enable image classification with fewer images. In this study, this technique will be applied to different image modalities in different syndromes. Retrospective study design.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| No pathology | – | – |
| Pathology | – | Other: Image classification using deep learning algorithm |
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Healthy and non-healthy subjects: extract data out of available images
Key inclusion criteria (1 listed)
- Availability of images, which allow discrimination.
Key exclusion criteria (1 listed)
- No availability of clear data on disease differentiation
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 |
|---|---|---|
| Image classification using deep learning algorithmImage classification using deep learning algorithm | Other | Pathology |
Primary outcome
Validation of Image classification by transfer learning algorithm
Time frame: 1 year
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Jan 2021.
- 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.
Questions about this trial
What is this clinical trial studying?
An observational study of Central Serous Chorioretinopathy and Diabetic Retinopathy, run by CRG UZ Brussel.
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 7 Jan 2021. Status can change between updates, so confirm with the study contact before making plans.
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 Pieter Nelis (+32494354198, nelispieter@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Validation of Image classification by transfer learning algorithm (time frame: 1 year). 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 7 Jan 2021.