Computerized macular edema detection using OCT images: A review

Athira, S.C.; Roy, Reena M.

doi:10.1109/iccs1.2017.8326031

Cited by 4 publications

(1 citation statement)

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“…Athira and Roy[ 52 ] compared the detection techniques of DME, which helped in the identification of the best technique, and also concluded that better results depend on the segmentation algorithm, feature extraction, feature set formulation, and classification. According to the conclusion, Hassan et al .’s[ 53 ] model has achieved the highest accuracy of 97.77%.…”

Section: Discussionmentioning

confidence: 99%

Deep learning–based detection of diabetic macular edema using optical coherence tomography and fundus images

Manikandan

Raman

Rajalakshmi

et al. 2023

Indian Journal of Ophthalmology

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Diabetic macular edema (DME) is an important cause of visual impairment in the working-age group. Deep learning methods have been developed to detect DME from two-dimensional retinal images and also from optical coherence tomography (OCT) images. The performances of these algorithms vary and often create doubt regarding their clinical utility. In resource-constrained health-care systems, these algorithms may play an important role in determining referral and treatment. The survey provides a diversified overview of macular edema detection methods, including cutting-edge research, with the objective of providing pertinent information to research groups, health-care professionals, and diabetic patients about the applications of deep learning in retinal image detection and classification process. Electronic databases such as PubMed, IEEE Explore, BioMed, and Google Scholar were searched from inception to March 31, 2022, and the reference lists of published papers were also searched. The study followed the preferred reporting items for systematic review and meta-analysis (PRISMA) reporting guidelines. Examination of various deep learning models and their exhibition regarding precision, epochs, their capacity to detect anomalies for less training data, concepts, and challenges that go deep into the applications were analyzed. A total of 53 studies were included that evaluated the performance of deep learning models in a total of 1,414,169°CT volumes, B-scans, patients, and 472,328 fundus images. The overall area under the receiver operating characteristic curve (AUROC) was 0.9727. The overall sensitivity for detecting DME using OCT images was 96% (95% confidence interval [CI]: 0.94–0.98). The overall sensitivity for detecting DME using fundus images was 94% (95% CI: 0.90–0.96).

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Section: Discussionmentioning

confidence: 99%