The Diagnostic Function of OCT in Diabetic Maculopathy

Abstract

Diabetic maculopathy (DM) is one of the major causes of vision impairment in individuals with diabetes. The traditional approach to diagnosis of DM includes fundus ophthalmoscopy and fluorescein angiography. Although very useful clinically, these methods do not contribute much to the evaluation of retinal morphology and its thickness profile. That is why a new technique called optical coherence tomography (OCT) was utilized to perform cross-sectional imaging of the retina. It facilitates measuring the macular thickening, quantification of diabetic macular oedema, and detecting vitreoretinal traction. Thus, OCT may assist in patient selection with DM who can benefit from treatment, identify what treatment is indicated, guide its implementing, and allow precise monitoring of treatment response. It seems to be the technique of choice for the early detection of macular oedema and for the followup of DM.

Application of different imaging modalities for diagnosis of Diabetic Macular Edema: A review

“…Stereoscopic slit-lamp biomicroscopy and stereo colour fundus imaging are standard screening modalities recommended by ETDRS for DME [28]. But results obtained by these modalities are qualitative and may not be able to identify small changes in RT and localize ME.…”

“…Table 2 summarizes the studies conducted to diagnose the DME using retinal thickness using biomicroscopy, OCT and FA. OCT is used to perform high resolution and precise measurement of macular thickness [12,28]. Several authors have used OCT to measure Retinal Thickness (RT) in DME patients [12].…”

New Diagnostic and Therapeutic Approaches for Preventing the Progression of Diabetic Retinopathy

“…OCT can also demonstrate a number of microanatomical features in diabetic macular edema, as well as areas of subclinical macular edema [44]. Additionally, SD-OCT can be useful to evaluate choroidal thickness [45].…”

An enhanced PSO-DEFS based feature selection with biometric authentication for identification of diabetic retinopathy

“…It also used to recognize the anatomy of DME and intra-retinal damage. 2 Anitha et al originated an automated system based on Arti¯cial Neural Network (ANN) for eye disease classi¯cation. A back propagation neural network was used for the classi¯cation purpose to provide better performance results.…”