Eye DiseasesNCT05105620
Deep Learning for Fluorescein Angiography and Optical Coherence Tomography Macular Thickness Map Image Translation
An observational study of Eye Diseases and Diabetic Retinopathy, run by Assiut University at 1 site in Egypt.
Trial summary
Diabetic macular edema (DME) is one of the leading causes of visual impairment in patients with diabetes. Fluorescein angiography (FA) plays an important role in diabetic retinopathy (DR) staging and evaluation of retinal vasculature. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Eye Diseases, Diabetic Retinopathy
- What is being tested?
- Fluorescein Angiography and Optical coherence tomography
- How many people
- 708 participants
- Where
- 1 siteEgypt
- Study dates
- 1 Aug 2018to 1 Feb 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients from the retina clinic in Assiut University Hospital who had simultaneously undergone same-day FA and OCT with a diagnosis of confirmed or suspected DME between Augyst 2018 and February 2021.
Who may be able to join
- Patients from the retina clinic in Assiut University Hospital who had simultaneously undergone same-day FA and OCT with a diagnosis of confirmed or suspected DME.
Who cannot join
- Significant media opacity that obscured the view of the fundus
- OCT images with high signal-to-noise ratio expressed by the device as"TopQ image quality," below 60
- Vitreoretinal interface disease distorting the OCT thickness map.
- Patients with concurrent ocular conditions interfering with blood flow
- Patients with uveitic diseases
- High myopia of more than -8.0 diopters.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Bridging the Resources Gap: Deep Learning for Fluorescein Angiography and Optical Coherence Tomography Macular Thickness Map Image Translation
Diabetic macular edema (DME) is one of the leading causes of visual impairment in patients with diabetes. Fluorescein angiography (FA) plays an important role in diabetic retinopathy (DR) staging and evaluation of retinal vasculature. However, FA is an invasive technique and does not permit the precise visualization of the retinal vasculature. Optical coherence tomography (OCT) is a non-invasive technique that has become popular in diagnosing and monitoring DR and its laser, medical, and surgical treatment. It provides a quantitative assessment of retinal thickness and location of edema in the macula. Automated OCT retinal thickness maps are routinely used in monitoring DME and its response to treatment. However, standard OCT provides only structural information and therefore does not delineate blood flow within the retinal vasculature. By combining the physiological information in FA with the structural information in the OCT, zones of leakage can be correlated to structural changes in the retina for better evaluation and monitoring of the response of DME to different treatment modalities. The occasional unavailability of either imaging modality may impair decision-making during the follow-up of patients with DME.
The problem of medical data generation particularly images has been of great interest, and as such, it has been deeply studied in recent years especially with the advent of deep convolutional neural networks(DCNN), which are progressively becoming the standard approach in most machine learning tasks such as pattern recognition and image classification. Generative adversarial networks (GANs) are neural network models in which a generation and a discrimination networks are trained simultaneously. Integrated network performance effectively generates new plausible image samples.
The aim of this work is to assess the efficacy of a GAN implementing pix2pix image translation for original FA to synthetic OCT color-coded macular thickness map image translation and the reverse (from original OCT color-coded macular thickness map to synthetic FA image translation).
- Conditions
- Eye Diseases, Diabetic Retinopathy
- Being tested
- Fluorescein Angiography, Optical coherence tomography
- Medical subject headings
- Eye Diseases, Diabetic Retinopathy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Fluorescein AngiographyDiagnostic test
Fluorescein Angiography for pateints with diabetes using fundus camera (TRC-NW8F retinal camera; Topcon Corporation, Tokyo, Japan).
Optical coherence tomographyDiagnostic test
Optical coherence tomography for pateints with diabetes using • Topcon DRI OCT Triton device (ver.10.13; Topcon Corporation, Tokyo, Japan).
What is being measured
The primary outcome is the main question the study is designed to answer.
Fréchet inception distance (FID) score.
Measured: 1 day
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Nov 2021
- Study start1 Aug 2018
- Primary completion1 Feb 2021
- Study completion1 Feb 2021
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 Eye DiseasesStudies of the same condition, open and under way.
- More trials run by Assiut 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
- Case only
- Time perspective
- Retrospective
- Enrolment
- 708 participants (actual)
Sponsor and investigators
- Lead sponsor
- Assiut University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Khaled Abdelazeem, Assiut University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05105620
- Sponsor's protocol ID
- 17300681
- Status last verified
- Nov 2021
- First submitted
- 26 Oct 2021
- Last updated
- 5 Nov 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Abdelmotaal H, Sharaf M, Soliman W, Wasfi E, Kedwany SM. Bridging the resources gap: deep learning for fluorescein angiography and optical coherence tomography macular thickness map image translation. BMC Ophthalmol. 2022 Sep 1;22(1):355. doi: 10.1186/s12886-022-02577-7.
PubMed 36050661
Study at a glance
From the registry record, last updated by the sponsor on 5 Nov 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Bridging the Resources Gap: Deep Learning for Fluorescein Angiography and Optical Coherence Tomography Macular Thickness Map Image Translation
- ClinicalTrials.gov ID
- NCT05105620
- Acronym / sponsor ID
- 17300681
- Conditions
- Eye Diseases, Diabetic Retinopathy
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 708 participants (actual)
- Lead sponsor
- Assiut UniversityUniversity, hospital or other
- Study start
- 1 Aug 2018 (actual)
- Primary completion
- 1 Feb 2021 (actual)
- Study completion
- 1 Feb 2021 (actual)
- First posted
- 3 Nov 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.
Diabetic macular edema (DME) is one of the leading causes of visual impairment in patients with diabetes. Fluorescein angiography (FA) plays an important role in diabetic retinopathy (DR) staging and evaluation of retinal vasculature. However, FA is an invasive technique and does not permit the precise visualization of the retinal vasculature. Optical coherence tomography (OCT) is a non-invasive technique that has become popular in diagnosing and monitoring DR and its laser, medical, and surgical treatment. It provides a quantitative assessment of retinal thickness and location of edema in the macula. Automated OCT retinal thickness maps are routinely used in monitoring DME and its response to treatment. However, standard OCT provides only structural information and therefore does not delineate blood flow within the retinal vasculature. By combining the physiological information in FA with the structural information in the OCT, zones of leakage can be correlated to structural changes in the retina for better evaluation and monitoring of the response of DME to different treatment modalities. The occasional unavailability of either imaging modality may impair decision-making during the follow-up of patients with DME.
The problem of medical data generation particularly images has been of great interest, and as such, it has been deeply studied in recent years especially with the advent of deep convolutional neural networks(DCNN), which are progressively becoming the standard approach in most machine learning tasks such as pattern recognition and image classification. Generative adversarial networks (GANs) are neural network models in which a generation and a discrimination networks are trained simultaneously. Integrated network performance effectively generates new plausible image samples.
The aim of this work is to assess the efficacy of a GAN implementing pix2pix image translation for original FA to synthetic OCT color-coded macular thickness map image translation and the reverse (from original OCT color-coded macular thickness map to synthetic FA image translation).
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients from the retina clinic in Assiut University Hospital who had simultaneously undergone same-day FA and OCT with a diagnosis of confirmed or suspected DME between Augyst 2018 and February 2021.
Key inclusion criteria (1 listed)
- Patients from the retina clinic in Assiut University Hospital who had simultaneously undergone same-day FA and OCT with a diagnosis of confirmed or suspected DME.
Key exclusion criteria (6 listed)
- Significant media opacity that obscured the view of the fundus
- OCT images with high signal-to-noise ratio expressed by the device as"TopQ image quality," below 60
- Vitreoretinal interface disease distorting the OCT thickness map.
- Patients with concurrent ocular conditions interfering with blood flow
- Patients with uveitic diseases
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 |
|---|---|---|
| Fluorescein AngiographyFluorescein Angiography for pateints with diabetes using fundus camera (TRC-NW8F retinal camera; Topcon Corporation, Tokyo, Japan). | Diagnostic test | – |
| Optical coherence tomographyOptical coherence tomography for pateints with diabetes using • Topcon DRI OCT Triton device (ver.10.13; Topcon Corporation, Tokyo, Japan). | Diagnostic test | – |
Primary outcome
Fréchet inception distance (FID) score.
Time frame: 1 day
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 5 Nov 2021.
- Derived from this study: PubMed 36050661Abdelmotaal H, Sharaf M, Soliman W, Wasfi E, Kedwany SM. Bridging the resources gap: deep learning for fluorescein angiography and optical coherence tomography macular thickness map image translation. BMC Ophthalmol. 2022 Sep 1;22(1):355. doi: 10.1186/s12886-022-02577-7.
- 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 Eye Diseases and Diabetic Retinopathy, run by Assiut University at 1 site in Egypt.
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 6 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 5 Nov 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 Egypt. 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?
Fréchet inception distance (FID) score. (time frame: 1 day). 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 NCT05105620, 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 5 Nov 2021.