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Diagnostics
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Diagnosis and Management of Retinopathy
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Diagnosis and Management of Retinopathy

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 6557

Special Issue Editors

Prof. Dr. Joanna Gołȩbiewska

E-Mail Website
Guest Editor
1. Department of Ophthalmology, Military Institute of Aviation Medicine, 01-755 Warsaw, Poland
2. Faculty of Medicine, Lazarski University, 02-662 Warsaw, Poland
Interests: OCT; OCTA; multimodal imaging; retinal diseases; macular degeneration
Prof. Dr. Dominik C. Odrobina

E-Mail Website
Guest Editor
1. Collegium Medicum, Jan Kochanowski University, 25-317 Kielce, Poland
2. Ophthalmology Clinic Boni Fratres Lodziensis, 93-357 Łódź, Poland
Interests: diabetic retinopathy; retinal vein/artery occlusion; hypertensive retinopathy

Special Issue Information

Dear Colleagues,

Retinopathy is a term used to describe a group of disorders that affect the retina and are usually caused by damage to tiny blood vessels.

Modern ophthalmological imaging methods provide important information on the pathogenesis of diseases such as retinal abnormalities due to diabetic retinopathy or vascular occlusion. Moreover, there is an increasing number of studies on diagnostic methods and treatment patterns for these diseases.

The purpose of the Diagnostics Special Issue on retinopathy is to provide readers with an overview of this ocular disorder—its causes, symptoms, risk factors, diagnostic methods and treatment options. We hope that articles contained in this issue will empower readers with the knowledge and information that can help them effectively prevent and manage retinopathy.

This Special Issue aims to explore recent scientific discoveries in the following research areas:

  • Diabetic retinopathy;
  • Retinopathy of prematurity;
  • Hypertensive retinopathy;
  • Retinal vein/artery occlusion;
  • Inherited retinal diseases, i.e., Coats disease.

Original research articles that focus on multimodal imaging findings in the diagnostics of retinopathy are welcome. In this research topic, we are looking for original papers and review articles focusing on updated diagnostic patterns and innovative multidisciplinary treatment protocols. Interesting case reports will be also considered for publication.

Prof. Dr. Joanna Gołȩbiewska
Prof. Dr. Dominik C. Odrobina
Guest Editors

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.

Published Papers (5 papers)

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Research

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13 pages, 290 KiB  
Article
The Impact of a Single Haemodialysis Session on the Retinal Thickness and Optic Nerve Morphology
by Joanna Roskal-Wałek, Joanna Gołębiewska, Jerzy Mackiewicz, Agnieszka Bociek, Paweł Wałek, Michał Biskup, Kamila Bołtuć-Dziugieł, Katarzyna Starzyk, Dominik Odrobina, Beata Wożakowska-Kapłon and Andrzej Jaroszyński
Diagnostics 2024, 14(3), 331; https://doi.org/10.3390/diagnostics14030331 - 03 Feb 2024
Viewed by 564
Abstract
Background: The aim of the study was to assess the influence of a single haemodialysis (HD) session on the retinal and optic nerve morphology in end-stage kidney disease (ESKD) patients. Methods: It is a prospective study including only the right eye of 35 [...] Read more.
Background: The aim of the study was to assess the influence of a single haemodialysis (HD) session on the retinal and optic nerve morphology in end-stage kidney disease (ESKD) patients. Methods: It is a prospective study including only the right eye of 35 chronic kidney disease (CKD) patients subjected to HD. Each patient underwent a full eye examination 30 min before HD (8 a.m.) and 15 min after HD. Optical coherence tomography (OCT) was used to assess the peripapillary retinal nerve fibre layer (pRNFL) thickness, macular nerve fibre layer (mRNFL) thickness, ganglion cell layer with inner plexiform layer thickness (GCL+), GCL++ (mRNFL and GCL+) thickness, total retinal thickness (RT) and total macular volume (TMV). The correlation was tested between such systemic parameters changes as systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), body weight, plasma osmolarity and ocular perfusion pressure (OPP) and ultrafiltration volume with total RT and pRNFL thickness changes during HD. Results: In the results of a single HD session, we could observe a statistically significant increase in the total RT thickness (pre-HD 270.4 ± 19.94 μm, post-HD 272.14 ± 20.11 μm; p = 0.0014), TMV (pre-HD 7.48 ± 0.53 mm3, post-HD 7.52 ± 0.55 mm3; p = 0.0006), total pRNFL thickness (pre-HD 97.46 ± 15.71 μm, post-HD 100.23 ± 14.7 μm; p = 0.0039), total GCL+ thickness (pre-HD 70.11 ± 9.24 μm, post-HD 70.6 ± 9.7 μm; p = 0.0044), and GCL++ thickness (pre-HD 97.46 ± 12.56 μm, post-HD 97.9 ± 12.94 μm; p = 0.0081). We observed a significant correlation between the change in total RT and DBP change, as well as between body weight change and the change in total pRNFL thickness. There was also a correlation between total pRNFL thickness change and the presence of diabetes mellitus. Conclusion: Even a single HD session affects the retinal and pRNFL thickness, which should be taken into account when interpreting the OCT results in patients subjected to HD. The impact of changes after a single HD session on selected parameters requires further assessment in subsequent studies, including long-term observation. Full article
(This article belongs to the Special Issue Diagnosis and Management of Retinopathy)
30 pages, 7608 KiB  
Article
HDR-EfficientNet: A Classification of Hypertensive and Diabetic Retinopathy Using Optimize EfficientNet Architecture
by Qaisar Abbas, Yassine Daadaa, Umer Rashid, Muhammad Zaheer Sajid and Mostafa E. A. Ibrahim
Diagnostics 2023, 13(20), 3236; https://doi.org/10.3390/diagnostics13203236 - 17 Oct 2023
Viewed by 2119
Abstract
Hypertensive retinopathy (HR) and diabetic retinopathy (DR) are retinal diseases closely associated with high blood pressure. The severity and duration of hypertension directly impact the prevalence of HR. The early identification and assessment of HR are crucial to preventing blindness. Currently, limited computer-aided [...] Read more.
Hypertensive retinopathy (HR) and diabetic retinopathy (DR) are retinal diseases closely associated with high blood pressure. The severity and duration of hypertension directly impact the prevalence of HR. The early identification and assessment of HR are crucial to preventing blindness. Currently, limited computer-aided methods are available for detecting HR and DR. These existing systems rely on traditional machine learning approaches, which require complex image processing techniques and are often limited in their application. To address this challenge, this work introduces a deep learning (DL) method called HDR-EfficientNet, which aims to provide an efficient and accurate approach to identifying various eye-related disorders, including diabetes and hypertensive retinopathy. The proposed method utilizes an EfficientNet-V2 network for end-to-end training focused on disease classification. Additionally, a spatial-channel attention method is incorporated into the approach to enhance its ability to identify specific areas of damage and differentiate between different illnesses. The HDR-EfficientNet model is developed using transfer learning, which helps overcome the challenge of imbalanced sample classes and improves the network’s generalization. Dense layers are added to the model structure to enhance the feature selection capacity. The performance of the implemented system is evaluated using a large dataset of over 36,000 augmented retinal fundus images. The results demonstrate promising accuracy, with an average area under the curve (AUC) of 0.98, a specificity (SP) of 96%, an accuracy (ACC) of 98%, and a sensitivity (SE) of 95%. These findings indicate the effectiveness of the suggested HDR-EfficientNet classifier in diagnosing HR and DR. In summary, the HDR-EfficientNet method presents a DL-based approach that offers improved accuracy and efficiency for the detection and classification of HR and DR, providing valuable support in diagnosing and managing these eye-related conditions. Full article
(This article belongs to the Special Issue Diagnosis and Management of Retinopathy)
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25 pages, 4376 KiB  
Article
Deep-Ocular: Improved Transfer Learning Architecture Using Self-Attention and Dense Layers for Recognition of Ocular Diseases
by Qaisar Abbas, Mubarak Albathan, Abdullah Altameem, Riyad Saleh Almakki and Ayyaz Hussain
Diagnostics 2023, 13(20), 3165; https://doi.org/10.3390/diagnostics13203165 - 10 Oct 2023
Cited by 1 | Viewed by 1307
Abstract
It is difficult for clinicians or less-experienced ophthalmologists to detect early eye-related diseases. By hand, eye disease diagnosis is labor-intensive, prone to mistakes, and challenging because of the variety of ocular diseases such as glaucoma (GA), diabetic retinopathy (DR), cataract (CT), and normal [...] Read more.
It is difficult for clinicians or less-experienced ophthalmologists to detect early eye-related diseases. By hand, eye disease diagnosis is labor-intensive, prone to mistakes, and challenging because of the variety of ocular diseases such as glaucoma (GA), diabetic retinopathy (DR), cataract (CT), and normal eye-related diseases (NL). An automated ocular disease detection system with computer-aided diagnosis (CAD) tools is required to recognize eye-related diseases. Nowadays, deep learning (DL) algorithms enhance the classification results of retinograph images. To address these issues, we developed an intelligent detection system based on retinal fundus images. To create this system, we used ODIR and RFMiD datasets, which included various retinographics of distinct classes of the fundus, using cutting-edge image classification algorithms like ensemble-based transfer learning. In this paper, we suggest a three-step hybrid ensemble model that combines a classifier, a feature extractor, and a feature selector. The original image features are first extracted using a pre-trained AlexNet model with an enhanced structure. The improved AlexNet (iAlexNet) architecture with attention and dense layers offers enhanced feature extraction, task adaptability, interpretability, and potential accuracy benefits compared to other transfer learning architectures, making it particularly suited for tasks like retinograph classification. The extracted features are then selected using the ReliefF method, and then the most crucial elements are chosen to minimize the feature dimension. Finally, an XgBoost classifier offers classification outcomes based on the desired features. These classifications represent different ocular illnesses. We utilized data augmentation techniques to control class imbalance issues. The deep-ocular model, based mainly on the AlexNet-ReliefF-XgBoost model, achieves an accuracy of 95.13%. The results indicate the proposed ensemble model can assist dermatologists in making early decisions for the diagnosing and screening of eye-related diseases. Full article
(This article belongs to the Special Issue Diagnosis and Management of Retinopathy)
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25 pages, 7774 KiB  
Article
RDS-DR: An Improved Deep Learning Model for Classifying Severity Levels of Diabetic Retinopathy
by Ijaz Bashir, Muhammad Zaheer Sajid, Rizwana Kalsoom, Nauman Ali Khan, Imran Qureshi, Fakhar Abbas and Qaisar Abbas
Diagnostics 2023, 13(19), 3116; https://doi.org/10.3390/diagnostics13193116 - 03 Oct 2023
Viewed by 1486
Abstract
A well-known eye disorder called diabetic retinopathy (DR) is linked to elevated blood glucose levels. Cotton wool spots, confined veins in the cranial nerve, AV nicking, and hemorrhages in the optic disc are some of its symptoms, which often appear later. Serious side [...] Read more.
A well-known eye disorder called diabetic retinopathy (DR) is linked to elevated blood glucose levels. Cotton wool spots, confined veins in the cranial nerve, AV nicking, and hemorrhages in the optic disc are some of its symptoms, which often appear later. Serious side effects of DR might include vision loss, damage to the visual nerves, and obstruction of the retinal arteries. Researchers have devised an automated method utilizing AI and deep learning models to enable the early diagnosis of this illness. This research gathered digital fundus images from renowned Pakistani eye hospitals to generate a new “DR-Insight” dataset and known online sources. A novel methodology named the residual-dense system (RDS-DR) was then devised to assess diabetic retinopathy. To develop this model, we have integrated residual and dense blocks, along with a transition layer, into a deep neural network. The RDS-DR system is trained on the collected dataset of 9860 fundus images. The RDS-DR categorization method demonstrated an impressive accuracy of 97.5% on this dataset. These findings show that the model produces beneficial outcomes and may be used by healthcare practitioners as a diagnostic tool. It is important to emphasize that the system’s goal is to augment optometrists’ expertise rather than replace it. In terms of accuracy, the RDS-DR technique fared better than the cutting-edge models VGG19, VGG16, Inception V-3, and Xception. This emphasizes how successful the suggested method is for classifying diabetic retinopathy (DR). Full article
(This article belongs to the Special Issue Diagnosis and Management of Retinopathy)
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Review

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17 pages, 288 KiB  
Review
Regression of Neovascularization after Panretinal Photocoagulation Combined with Anti-VEGF Injection for Proliferative Diabetic Retinopathy—A Review
by Maciej Gawęcki, Krzysztof Kiciński, Lorenzo Bianco and Maurizio Battaglia Parodi
Diagnostics 2024, 14(1), 31; https://doi.org/10.3390/diagnostics14010031 - 22 Dec 2023
Viewed by 732
Abstract
Proliferative diabetic retinopathy (PDR) poses a significant therapeutic problem that often results in severe visual loss. Panretinal photocoagulation (PRP) has long been a mainstay treatment for this condition. Conversely, intravitreal anti-VEGF therapy has served as an alternative treatment for PDR. This review aimed [...] Read more.
Proliferative diabetic retinopathy (PDR) poses a significant therapeutic problem that often results in severe visual loss. Panretinal photocoagulation (PRP) has long been a mainstay treatment for this condition. Conversely, intravitreal anti-VEGF therapy has served as an alternative treatment for PDR. This review aimed to evaluate the effects of PRP combined with anti-VEGF therapy on the regression of neovascularization (NV), including functional outcomes and incidence of complications. The MEDLINE database was searched for articles evaluating regression of NV using a combination of the following terms: “proliferative diabetic retinopathy”, “anti-VEGF”, “panretinal photocoagulation”, and “combined treatment”. The search yielded a total of 22 articles. The analysis of their results indicated PRP combined with ant-VEGF therapy as superior over PRP alone in the management of PDR. Combination treatment yields better and faster regression of NV and a lower incidence of serious complications, such as vitreous hemorrhage and the need for pars plana vitrectomy. Nevertheless, complete regression of NV is not achieved in a significant proportion of patients. Further research is needed to establish the most effective schedule for intravitreal injections as an adjunct to PRP. The current literature shows that in some cases, cessation of anti-VEGF injection in combination treatment for PDR can lead to relapse of NV. Full article
(This article belongs to the Special Issue Diagnosis and Management of Retinopathy)
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