Deep Learning-Based Optical Coherence Tomography and Optical Coherence Tomography Angiography Image Analysis: An Updated Summary

Ran, Anran; Cheung, Carol Y.

doi:10.1097/apo.0000000000000405

Cited by 28 publications

(22 citation statements)

References 89 publications

(139 reference statements)

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“…Aflibercept was given as treatment with outpatient follow up arranged for further observation and management. In summary, this case helps illustrate that OCTA is useful in evaluating macular vascular abnormalities 1 and anti-VEGF treatment is of some use in treating macular telangiectasia type 1.…”

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confidence: 87%

Multimodal Imaging in the Diagnosis of Macular Telangiectasia Type 1

Chen

Chang

2022

Asia-Pacific Journal of Ophthalmology

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confidence: 87%

Multimodal Imaging in the Diagnosis of Macular Telangiectasia Type 1

Chen

Chang

2022

Asia-Pacific Journal of Ophthalmology

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“…Machine learning approaches to analyze OCT images are already present in the scientific literature [14]. For example, Schmidt-Erfurth et al [15] discuss the comparison of unsupervised and supervised learning in the classification of patients with macular degener-ation (AMD) and diabetic retinopathy (DR).…”

Section: Related Workmentioning

confidence: 99%

Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach

Ara

Matiolański

Dziech

et al. 2022

Sensors

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The use of optical coherence tomography (OCT) in medical diagnostics is now common. The growing amount of data leads us to propose an automated support system for medical staff. The key part of the system is a classification algorithm developed with modern machine learning techniques. The main contribution is to present a new approach for the classification of eye diseases using the convolutional neural network model. The research concerns the classification of patients on the basis of OCT B-scans into one of four categories: Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV), Drusen, and Normal. Those categories are available in a publicly available dataset of above 84,000 images utilized for the research. After several tested architectures, our 5-layer neural network gives us a promising result. We compared them to the other available solutions which proves the high quality of our algorithm. Equally important for the application of the algorithm is the computational time, which is reduced by the limited size of the model. In addition, the article presents a detailed method of image data augmentation and its impact on the classification results. The results of the experiments were also presented for several derived models of convolutional network architectures that were tested during the research. Improving processes in medical treatment is important. The algorithm cannot replace a doctor but, for example, can be a valuable tool for speeding up the process of diagnosis during screening tests.

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“…An artificial intelligence algorithm, especially with standardization of images,10,11 might also be able improve diagnostic accuracy, but the technology remains nascent. Our OCT-based approach, or similar other methodologies, bridge the gap between the present day and the future arrival of artificial intelligence-assisted decision making 12–14…”

Section: How Can This Be Accomplished?mentioning

confidence: 99%

The Glaucoma Suspect Problem: Ways Forward

Leshno

Liebmann

2022

Asia-Pacific Journal of Ophthalmology

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The diagnosis of glaucoma depends upon indentification of characteristic damage to the optic nerve and retinal fiber layer. In many cases, however, clinicians find it difficult to ascertain whether glaucomatous damage is present or absent. These patients are often labeled as "glaucoma suspects," which creates a subpopulation of individuals without clear-cut disease who nonetheless must remain under surveillance. Most will never go on to develop glaucoma, yet the need for ongoing monitoring burdens clinics and health care systems. In this perspective, we illustrate possible directions and novel approaches that can be used to remedy this situation by integrating current technologies into clinical practice. In particular, we suggest that optical coherence tomography be better utilized to methodologically classify these eyes into glaucomatous and healthy categories.

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Deep Learning-Based Optical Coherence Tomography and Optical Coherence Tomography Angiography Image Analysis: An Updated Summary

Abstract: Supplemental Digital Content is available in the text

Cited by 28 publications

References 89 publications

Multimodal Imaging in the Diagnosis of Macular Telangiectasia Type 1

Multimodal Imaging in the Diagnosis of Macular Telangiectasia Type 1

Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach

The Glaucoma Suspect Problem: Ways Forward

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