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Diagnostics
Special Issues
Imaging of Macular and Retinal Diseases
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Imaging of Macular and Retinal Diseases

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 2294

Special Issue Editor

Dr. Antonio Ferreras

E-Mail Website
Guest Editor
Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain
Interests: retina; macula; glaucoma; imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Different diseases affecting the retina have typical characteristics that can be identified by assessing the fundus of the eye. Imaging technologies provide accurate images of the retina, as well as quantitative data, and have become common tools to diagnose and monitor macular and retinal disorders in clinical practice.

New techniques, algorithms or programs can optimize the performance of these instruments. Moreover, the combination of different imaging modes can also increase the precision of these devices to detect changes in the retina and evaluate the response to medical or surgical treatments.

High-resolution images of the retina offer valuable information that allows clinicians and researchers to better understand the pathophysiology of retinopathies. Consequently, the possibilities and full applications of imaging technologies for the retina are yet to be discovered.

The objective of this Special Issue is to update the current knowledge about the use of imaging techniques in the field of the retina.

Dr. Antonio Ferreras
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

  • retina
  • macula
  • imaging
  • diagnosis

Published Papers (1 paper)

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Research

13 pages, 5304 KiB  
Article
Automated Identification and Segmentation of Ellipsoid Zone At-Risk Using Deep Learning on SD-OCT for Predicting Progression in Dry AMD
by Gagan Kalra, Hasan Cetin, Jon Whitney, Sari Yordi, Yavuz Cakir, Conor McConville, Victoria Whitmore, Michelle Bonnay, Jamie L. Reese, Sunil K. Srivastava and Justis P. Ehlers
Diagnostics 2023, 13(6), 1178; https://doi.org/10.3390/diagnostics13061178 - 20 Mar 2023
Cited by 2 | Viewed by 1835
Abstract
Background: The development and testing of a deep learning (DL)-based approach for detection and measurement of regions of Ellipsoid Zone (EZ) At-Risk to study progression in nonexudative age-related macular degeneration (AMD). Methods: Used in DL model training and testing were 341 subjects with [...] Read more.
Background: The development and testing of a deep learning (DL)-based approach for detection and measurement of regions of Ellipsoid Zone (EZ) At-Risk to study progression in nonexudative age-related macular degeneration (AMD). Methods: Used in DL model training and testing were 341 subjects with nonexudative AMD with or without geographic atrophy (GA). An independent dataset of 120 subjects were used for testing model performance for prediction of GA progression. Accuracy, specificity, sensitivity, and intraclass correlation coefficient (ICC) for DL-based EZ At-Risk percentage area measurement was calculated. Random forest-based feature ranking of EZ At-Risk was compared to previously validated quantitative OCT-based biomarkers. Results: The model achieved a detection accuracy of 99% (sensitivity = 99%; specificity = 100%) for EZ At-Risk. Automatic EZ At-Risk measurement achieved an accuracy of 90% (sensitivity = 90%; specificity = 84%) and the ICC compared to ground truth was high (0.83). In the independent dataset, higher baseline mean EZ At-Risk correlated with higher progression to GA at year 5 (p < 0.001). EZ At-Risk was a top ranked feature in the random forest assessment for GA prediction. Conclusions: This report describes a novel high performance DL-based model for the detection and measurement of EZ At-Risk. This biomarker showed promising results in predicting progression in nonexudative AMD patients. Full article
(This article belongs to the Special Issue Imaging of Macular and Retinal Diseases)
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