Deep-learning-based enhanced optic-disc photography

Ha, Ahnul; Sun, Sukkyu; Kim, Young Kook; Lee, Jinho; Jeoung, Jin Wook; Kim, Hee Chan; Park, Ki Ho

doi:10.1371/journal.pone.0239913

Cited by 10 publications

(7 citation statements)

References 28 publications

(32 reference statements)

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“…The ML approach could accurately identify and quantify fleck lesions, a potential outcome measure for future clinical trials [ 170 ]. Similarly, optic disc photography has been used to systematically detect abnormalities of the optic disc [ 171 ]. ML strategies may revolutionise the speed and accuracy at which new patients can be phenotyped in the clinic.…”

Section: Impacts On Ird Diagnosis Outside Of Ngs Testingmentioning

confidence: 99%

Next-Generation Sequencing Applications for Inherited Retinal Diseases

Dockery

Whelan

Humphries

et al. 2021

IJMS

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Inherited retinal diseases (IRDs) represent a collection of phenotypically and genetically diverse conditions. IRDs phenotype(s) can be isolated to the eye or can involve multiple tissues. These conditions are associated with diverse forms of inheritance, and variants within the same gene often can be associated with multiple distinct phenotypes. Such aspects of the IRDs highlight the difficulty met when establishing a genetic diagnosis in patients. Here we provide an overview of cutting-edge next-generation sequencing techniques and strategies currently in use to maximise the effectivity of IRD gene screening. These techniques have helped researchers globally to find elusive causes of IRDs, including copy number variants, structural variants, new IRD genes and deep intronic variants, among others. Resolving a genetic diagnosis with thorough testing enables a more accurate diagnosis and more informed prognosis and should also provide information on inheritance patterns which may be of particular interest to patients of a child-bearing age. Given that IRDs are heritable conditions, genetic counselling may be offered to help inform family planning, carrier testing and prenatal screening. Additionally, a verified genetic diagnosis may enable access to appropriate clinical trials or approved medications that may be available for the condition.

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Section: Impacts On Ird Diagnosis Outside Of Ngs Testingmentioning

confidence: 99%

Next-Generation Sequencing Applications for Inherited Retinal Diseases

Dockery

Whelan

Humphries

et al. 2021

IJMS

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“…b Denoising in fundus photography [ 53 ] and peripapillary optical coherence tomography (OCT) [ 16 ]. c Super-resolution for optic nerve head photography [ 56 ]. d Domain transfer for fundus photography to angiography [ 62 ] and ultra-widefield to classic fundus photography (re-analysis in this work) [ 63 ].…”

Section: Reviewmentioning

confidence: 99%

Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey

et al. 2022

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Background Recent advances in deep learning techniques have led to improved diagnostic abilities in ophthalmology. A generative adversarial network (GAN), which consists of two competing types of deep neural networks, including a generator and a discriminator, has demonstrated remarkable performance in image synthesis and image-to-image translation. The adoption of GAN for medical imaging is increasing for image generation and translation, but it is not familiar to researchers in the field of ophthalmology. In this work, we present a literature review on the application of GAN in ophthalmology image domains to discuss important contributions and to identify potential future research directions. Methods We performed a survey on studies using GAN published before June 2021 only, and we introduced various applications of GAN in ophthalmology image domains. The search identified 48 peer-reviewed papers in the final review. The type of GAN used in the analysis, task, imaging domain, and the outcome were collected to verify the usefulness of the GAN. Results In ophthalmology image domains, GAN can perform segmentation, data augmentation, denoising, domain transfer, super-resolution, post-intervention prediction, and feature extraction. GAN techniques have established an extension of datasets and modalities in ophthalmology. GAN has several limitations, such as mode collapse, spatial deformities, unintended changes, and the generation of high-frequency noises and artifacts of checkerboard patterns. Conclusions The use of GAN has benefited the various tasks in ophthalmology image domains. Based on our observations, the adoption of GAN in ophthalmology is still in a very early stage of clinical validation compared with deep learning classification techniques because several problems need to be overcome for practical use. However, the proper selection of the GAN technique and statistical modeling of ocular imaging will greatly improve the performance of each image analysis. Finally, this survey would enable researchers to access the appropriate GAN technique to maximize the potential of ophthalmology datasets for deep learning research.

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“…Moreover, in clinical practice, poor patient cooperation, small pupil, or refractive medium opacity will limit the performance of optical disc recognition. Ha et al (40) proposed a DL method to improve the resolution and readability of fundus optic disc photos through contrast, color, and brightness compensation. Each high-resolution original fundus photo was converted into two counterparts, a reduced 'low-resolution fundus photo' and a 'compensated high-resolution fundus photo'.…”

Section: Gonmentioning

confidence: 99%