El Ghatit, A., Abo shousha, M., Gomaa, A., Shafik Agaiby, M. (2025). EN FACE IMAGE BASED CLASSIFICATION OF DIABETIC MACULAR EDEMA USING SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY. ALEXMED ePosters, 7(1), 46-47. doi: 10.21608/alexpo.2025.361919.2099
Ali Metwaly El Ghatit; Mohsen Ahmed Abo shousha; Amir Ramadan Gomaa; Michael Rimon Shafik Agaiby. "EN FACE IMAGE BASED CLASSIFICATION OF DIABETIC MACULAR EDEMA USING SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY". ALEXMED ePosters, 7, 1, 2025, 46-47. doi: 10.21608/alexpo.2025.361919.2099
El Ghatit, A., Abo shousha, M., Gomaa, A., Shafik Agaiby, M. (2025). 'EN FACE IMAGE BASED CLASSIFICATION OF DIABETIC MACULAR EDEMA USING SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY', ALEXMED ePosters, 7(1), pp. 46-47. doi: 10.21608/alexpo.2025.361919.2099
El Ghatit, A., Abo shousha, M., Gomaa, A., Shafik Agaiby, M. EN FACE IMAGE BASED CLASSIFICATION OF DIABETIC MACULAR EDEMA USING SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY. ALEXMED ePosters, 2025; 7(1): 46-47. doi: 10.21608/alexpo.2025.361919.2099
EN FACE IMAGE BASED CLASSIFICATION OF DIABETIC MACULAR EDEMA USING SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY
1Department of Ophthalmology, Faculty of Medicine, Alexandria University.
2Department of Ophthalmology, faculty of Medicine, Alexandria University
3Department of Ophthalmology, Faculty of Medicine, Alexandria University
Abstract
Introduction Diabetic macular edema (DME) is the most common cause of visual loss in patients with diabetic retinopathy (DR). It is characterized by fluid accumulation and retinal thickening and can occur at any stage of DR To date, various classifications have been used to assess disease progression in patients with DME. In recent years, classifications based on the pattern of retinal thickening on B-scan images have been used, and these include diffuse retinal thickening, cystoid macular edema, and subretinal fluid (SF) The spectral domain OCT (SD-OCT), which contains algorithm called split spectrum technology, has enabled the acquisition of high-resolution, three dimensional (3D) images of the retinal structure. By using en face images constructed from the 3D images, the changes in the retinal structure can be visualized at an arbitrary retinal depth from a bird’s-eye view. For example, investigated epiretinal membrane (ERM) using en face imaging and revealed significant associations of the distribution of ERM and the depth of retinal folds due to retinal traction by ERM with visual functions.
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