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Applied Sciences
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Diagnosis and Therapy for Retinal Diseases
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Diagnosis and Therapy for Retinal Diseases

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (20 February 2025) | Viewed by 1545

Special Issue Editor

Dr. Alessandro Meduri

E-Mail Website
Guest Editor
Department of Biomedical and Dental Sciences and Morphofunctional Imaging, Section of Ophthalmology, University of Messina, Via Consolare Valeria, 1, 98125 Messina, Italy
Interests: cataract surgery; vitreoretinal surgery; posterior segment pathologies

Special Issue Information

Dear Colleagues,

This Special Issue, titled "Diagnosis and Therapy for Retinal Diseases", focuses on the latest advancements in the identification, assessment, and treatment of retinal disorders. It aims to cover a broad range of topics, including innovative diagnostic techniques, emerging therapeutic approaches, and the underlying pathophysiological mechanisms of retinal diseases. Contributions may include original research, reviews, and clinical studies that provide insights into improving patient outcomes, enhancing diagnostic accuracy, and developing novel treatments for conditions such as diabetic retinopathy, age-related macular degeneration, and other retinal pathologies.

Dr. Alessandro Meduri
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. Applied Sciences 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 2400 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

  • retinal diseases
  • optical coherence tomography
  • intravitreal injection
  • age-related macular degeneration
  • cystoid macular edema
  • vitreous pathologies
  • diabetic retinopathy

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Published Papers (1 paper)

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Research

22 pages, 8375 KiB  
Article
From Pixels to Diagnosis: Early Detection of Diabetic Retinopathy Using Optical Images and Deep Neural Networks
by Amira J. Zaylaa and Sylva Kourtian
Appl. Sci. 2025, 15(5), 2684; https://doi.org/10.3390/app15052684 - 3 Mar 2025
Viewed by 1255
Abstract
The detection of diabetic retinopathy (DR) is challenging, as the current diagnostic methods rely heavily on the expertise of specialists and require the mass screening of diabetic patients. The prevalence of avoidable vision impairment due to DR necessitates the exploration of alternative diagnostic [...] Read more.
The detection of diabetic retinopathy (DR) is challenging, as the current diagnostic methods rely heavily on the expertise of specialists and require the mass screening of diabetic patients. The prevalence of avoidable vision impairment due to DR necessitates the exploration of alternative diagnostic techniques. Specifically, it is necessary to develop reliable automatic methods to enable the early diagnosis and detection of DR from optical images. To address the lack of such methods, this research focused on employing various pre-trained deep neural networks (DNNs) and statistical metrics to provide an automatic framework for detecting DR in optical images. The receiver operating characteristic (ROC) was employed to examine the performance of each network. Ethically obtained real datasets were utilized to validate and enhance the robustness of the proposed detection framework. The experimental results showed that, in terms of the overall performance in DR detection, ResNet-50 was the best, followed by GoogleNet, with 99.44% sensitivity, while they were similar in terms of accuracy (93.56%). ResNet-50 outperformed GoogleNet in terms of the specificity (89.74%) and precision (90.07%) of DR detection. The ROC curves of both ResNet-50 and GoogleNet yielded optimal results, followed by SqueezeNet. MobileNet-v2 showed the weakest performance in terms of the ROC, while all networks showed negligible errors in diagnosis and detection. These results show that the automatic detection and diagnosis framework for DR is a promising tool enabling doctors to diagnose DR early and save time. As future directions, it is necessary to develop a grading algorithm and to explore other strategies to further improve the automatic detection and diagnosis of DR and integrate it into digital slit lamp machines. Full article
(This article belongs to the Special Issue Diagnosis and Therapy for Retinal Diseases)
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