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Diagnostics
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Advances in Retinopathy
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Advances in Retinopathy

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 13806

Special Issue Editor

Dr. Seong Joon Ahn

E-Mail Website
Guest Editor
Department of Ophthalmology, Hanyang University Hospital, Hanyang University College of Medicine, Seoul 04763, Korea
Interests: artificial intelligence; retinal degeneration; retinal imaging; hydroxychloroquine retinopathy; retinopathy of prematurity; uveitis

Special Issue Information

Dear Colleagues,

Retinopathy is a damage to the retina, caused by diverse pathogenic processes such as neovascularization, infections/inflammation, trauma, drug-induced toxicity, and genetic mutation. Diabetic retinopathy and retinopathy of prematurity are the publicly well-known examples of retinopathy that have a significant impact on public health. Retinopathy may lead to significant visual loss, which requires careful diagnosis and appropriate management.

Recently, there have been advances in the field of diagnosis of retinopathy, including modern retinal imaging and artificial intelligence. These have shown promising results for early detection of retinopathy and prediction of its clinical outcomes. In this Special Issue, we welcome original articles and expert reviews about the study on the advances in retinopathy, particularly diagnostic ones. These include, but are not limited to, genetic and molecular mechanisms of retinopathy, retinal imaging, functional tests, artificial intelligence, molecular diagnosis, and preclinical research on diagnostic techniques for diverse types of retinopathy.

Dr. Seong Joon Ahn
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Imaging
  • Molecular diagnosis
  • Functional test
  • Clinical trial
  • Inflammatory/infectious retinopathy
  • Traumatic retinopathy
  • Toxic retinopathy
  • Diabetic retinopathy
  • Retinopathy of prematurity
  • Artificial intelligence

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Published Papers (5 papers)

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Research

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21 pages, 3830 KiB  
Article
GabROP: Gabor Wavelets-Based CAD for Retinopathy of Prematurity Diagnosis via Convolutional Neural Networks
by Omneya Attallah
Diagnostics 2023, 13(2), 171; https://doi.org/10.3390/diagnostics13020171 - 4 Jan 2023
Cited by 22 | Viewed by 3626
Abstract
One of the most serious and dangerous ocular problems in premature infants is retinopathy of prematurity (ROP), a proliferative vascular disease. Ophthalmologists can use automatic computer-assisted diagnostic (CAD) tools to help them make a safe, accurate, and low-cost diagnosis of ROP. All previous [...] Read more.
One of the most serious and dangerous ocular problems in premature infants is retinopathy of prematurity (ROP), a proliferative vascular disease. Ophthalmologists can use automatic computer-assisted diagnostic (CAD) tools to help them make a safe, accurate, and low-cost diagnosis of ROP. All previous CAD tools for ROP diagnosis use the original fundus images. Unfortunately, learning the discriminative representation from ROP-related fundus images is difficult. Textural analysis techniques, such as Gabor wavelets (GW), can demonstrate significant texture information that can help artificial intelligence (AI) based models to improve diagnostic accuracy. In this paper, an effective and automated CAD tool, namely GabROP, based on GW and multiple deep learning (DL) models is proposed. Initially, GabROP analyzes fundus images using GW and generates several sets of GW images. Next, these sets of images are used to train three convolutional neural networks (CNNs) models independently. Additionally, the actual fundus pictures are used to build these networks. Using the discrete wavelet transform (DWT), texture features retrieved from every CNN trained with various sets of GW images are combined to create a textural-spectral-temporal demonstration. Afterward, for each CNN, these features are concatenated with spatial deep features obtained from the original fundus images. Finally, the previous concatenated features of all three CNN are incorporated using the discrete cosine transform (DCT) to lessen the size of features caused by the fusion process. The outcomes of GabROP show that it is accurate and efficient for ophthalmologists. Additionally, the effectiveness of GabROP is compared to recently developed ROP diagnostic techniques. Due to GabROP’s superior performance compared to competing tools, ophthalmologists may be able to identify ROP more reliably and precisely, which could result in a reduction in diagnostic effort and examination time. Full article
(This article belongs to the Special Issue Advances in Retinopathy)
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19 pages, 11459 KiB  
Article
DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity
by Omneya Attallah
Diagnostics 2021, 11(11), 2034; https://doi.org/10.3390/diagnostics11112034 - 3 Nov 2021
Cited by 43 | Viewed by 2867
Abstract
Retinopathy of Prematurity (ROP) affects preterm neonates and could cause blindness. Deep Learning (DL) can assist ophthalmologists in the diagnosis of ROP. This paper proposes an automated and reliable diagnostic tool based on DL techniques called DIAROP to support the ophthalmologic diagnosis of [...] Read more.
Retinopathy of Prematurity (ROP) affects preterm neonates and could cause blindness. Deep Learning (DL) can assist ophthalmologists in the diagnosis of ROP. This paper proposes an automated and reliable diagnostic tool based on DL techniques called DIAROP to support the ophthalmologic diagnosis of ROP. It extracts significant features by first obtaining spatial features from the four Convolution Neural Networks (CNNs) DL techniques using transfer learning and then applying Fast Walsh Hadamard Transform (FWHT) to integrate these features. Moreover, DIAROP explores the best-integrated features extracted from the CNNs that influence its diagnostic capability. The results of DIAROP indicate that DIAROP achieved an accuracy of 93.2% and an area under receiving operating characteristic curve (AUC) of 0.98. Furthermore, DIAROP performance is compared with recent ROP diagnostic tools. Its promising performance shows that DIAROP may assist the ophthalmologic diagnosis of ROP. Full article
(This article belongs to the Special Issue Advances in Retinopathy)
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Review

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14 pages, 1618 KiB  
Review
Modern Approach to Diabetic Retinopathy Diagnostics
by Maria Kąpa, Iga Koryciarz, Natalia Kustosik, Piotr Jurowski and Zofia Pniakowska
Diagnostics 2024, 14(17), 1846; https://doi.org/10.3390/diagnostics14171846 - 24 Aug 2024
Viewed by 1668
Abstract
This article reviews innovative diagnostic approaches for diabetic retinopathy as the prevalence of diabetes mellitus and its complications continue to escalate. Novel techniques focus on early disease detection. Technological innovations, such as teleophthalmology, smartphone-based photography, artificial intelligence with deep learning, or widefield photography, [...] Read more.
This article reviews innovative diagnostic approaches for diabetic retinopathy as the prevalence of diabetes mellitus and its complications continue to escalate. Novel techniques focus on early disease detection. Technological innovations, such as teleophthalmology, smartphone-based photography, artificial intelligence with deep learning, or widefield photography, can enhance diagnostic accuracy and accelerate the treatment. The review highlights teleophthalmology and handheld photography as promising solutions for remote eye care. These methods revolutionize diabetic retinopathy screening, offering cost-effective and accessible solutions. However, the use of these techniques may be limited by insurance coverage in certain world regions. Ultra-widefield photography offers a comprehensive view of up to 80.0% of the retina in a single image, compared to the 34.0% coverage of the traditional seven-field imaging protocol. It allows retinal imaging without pupil dilation, especially for individuals with compromised mydriasis. However, they also have drawbacks, including high costs, artifacts from eyelashes, eyelid margins, and peripheral distortion. Recent advances in artificial intelligence and machine learning, particularly through convolutional neural networks, are revolutionizing diabetic retinopathy diagnostics, enhancing screening efficiency and accuracy. FDA-approved Artificial Intelligence-powered devices such as LumineticsCore™, EyeArt, and AEYE Diagnostic Screening demonstrate high sensitivity and specificity in diabetic retinopathy detection. While Artificial Intelligence offers the potential to improve patient outcomes and reduce treatment costs, challenges such as dataset biases, high initial costs, and cybersecurity risks must be considered to ensure safety and efficiency. Nanotechnology advancements further enhance diagnosis, offering highly branched polyethyleneimine particles with fluorescein sodium (PEI-NHAc-FS) for better fluorescein angiography or vanadium oxide-based metabolic fingerprinting for early detection. Full article
(This article belongs to the Special Issue Advances in Retinopathy)
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16 pages, 1617 KiB  
Review
Clinical Applications and Future Directions of Smartphone Fundus Imaging
by Seong Joon Ahn and Young Hwan Kim
Diagnostics 2024, 14(13), 1395; https://doi.org/10.3390/diagnostics14131395 - 30 Jun 2024
Viewed by 1264
Abstract
The advent of smartphone fundus imaging technology has marked a significant evolution in the field of ophthalmology, offering a novel approach to the diagnosis and management of retinopathy. This review provides an overview of smartphone fundus imaging, including clinical applications, advantages, limitations, clinical [...] Read more.
The advent of smartphone fundus imaging technology has marked a significant evolution in the field of ophthalmology, offering a novel approach to the diagnosis and management of retinopathy. This review provides an overview of smartphone fundus imaging, including clinical applications, advantages, limitations, clinical applications, and future directions. The traditional fundus imaging techniques are limited by their cost, portability, and accessibility, particularly in resource-limited settings. Smartphone fundus imaging emerges as a cost-effective, portable, and accessible alternative. This technology facilitates the early detection and monitoring of various retinal pathologies, including diabetic retinopathy, age-related macular degeneration, and retinal vascular disorders, thereby democratizing access to essential diagnostic services. Despite its advantages, smartphone fundus imaging faces challenges in image quality, standardization, regulatory considerations, and medicolegal issues. By addressing these limitations, this review highlights the areas for future research and development to fully harness the potential of smartphone fundus imaging in enhancing patient care and visual outcomes. The integration of this technology into telemedicine is also discussed, underscoring its role in facilitating remote patient care and collaborative care among physicians. Through this review, we aim to contribute to the understanding and advancement of smartphone fundus imaging as a valuable tool in ophthalmic practice, paving the way for its broader adoption and integration into medical diagnostics. Full article
(This article belongs to the Special Issue Advances in Retinopathy)
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12 pages, 1142 KiB  
Review
Metabolism and Vascular Retinopathies: Current Perspectives and Future Directions
by Charandeep Singh
Diagnostics 2022, 12(4), 903; https://doi.org/10.3390/diagnostics12040903 - 5 Apr 2022
Cited by 8 | Viewed by 2636
Abstract
The retina is one of the most metabolically active organs in the body. Although it is an extension of the brain, the metabolic needs of the retina and metabolic exchanges between the different cell types in the retina are not the same as [...] Read more.
The retina is one of the most metabolically active organs in the body. Although it is an extension of the brain, the metabolic needs of the retina and metabolic exchanges between the different cell types in the retina are not the same as that of the brain. Retinal photoreceptors convert most of the glucose into lactate via aerobic glycolysis which takes place in their cytosol, yet there are immense numbers of mitochondria in photoreceptors. The present article is a focused review of the metabolic dysregulation seen in retinopathies with underlying vascular abnormalities with aberrant mitochondrial metabolism and Hypoxia-inducible factor (HIF) dependent pathogenesis. Special emphasis has been paid to metabolic exchanges between different cell types in retinopathy of prematurity (ROP), age-related macular degeneration (AMD), and diabetic retinopathy (DR). Metabolic similarities between these proliferative retinopathies have been discussed. Full article
(This article belongs to the Special Issue Advances in Retinopathy)
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