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Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT05105620
Other study ID # 17300681
Secondary ID
Status Completed
Phase
First received
Last updated
Start date August 1, 2018
Est. completion date February 1, 2021

Study information

Verified date November 2021
Source Assiut University
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical 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. 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).


Recruitment information / eligibility

Status Completed
Enrollment 708
Est. completion date February 1, 2021
Est. primary completion date February 1, 2021
Accepts healthy volunteers No
Gender All
Age group N/A and older
Eligibility Inclusion Criteria: - 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. Exclusion Criteria: - 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.

Study Design


Related Conditions & MeSH terms


Intervention

Diagnostic Test:
Fluorescein Angiography
Fluorescein Angiography for pateints with diabetes using fundus camera (TRC-NW8F retinal camera; Topcon Corporation, Tokyo, Japan).
Optical coherence tomography
Optical coherence tomography for pateints with diabetes using • Topcon DRI OCT Triton device (ver.10.13; Topcon Corporation, Tokyo, Japan).

Locations

Country Name City State
Egypt Assiut University Assiut

Sponsors (1)

Lead Sponsor Collaborator
Assiut University

Country where clinical trial is conducted

Egypt, 

Outcome

Type Measure Description Time frame Safety issue
Primary Fréchet inception distance (FID) score. 1 day
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