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Applied Sciences
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Technological Advances in Ocular Diseases and Oculomics
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Technological Advances in Ocular Diseases and Oculomics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 30 July 2025 | Viewed by 821

Special Issue Editor

Dr. Zhihong (Jewel) Hu

E-Mail Website
Guest Editor
Doheny Image Analysis Laboratory, Doheny Eye Institute, Pasadena, CA 91103, USA
Interests: artificial intelligence (AI); machine learning, deep learning, and computer vision in the fields of automated screening and classification; novel biomarker discovery; quantitative prediction of progressive growth; image segmentation and registration of 2D and 3D images with various eye diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Applied research into ocular diseases, with a focus on understanding their underlying causes and developing practical approaches for diagnosis, prevention, and treatment, continues to be a critical area of exploration. Recent technological advancements have profoundly transformed ophthalmology, enabling not only the early detection of ocular diseases but also the identification of systemic conditions through the emerging field of oculomics. These innovations are revolutionizing our approach to managing both ocular and systemic health, paving the way for more personalized and precision-driven patient care.

We sincerely invite submissions to this Special Issue, highlighting your impactful applied research on a broad range of topics related to the understanding, diagnosis, screening, and treatment of ocular and systemic diseases. We invite high-quality original applied research articles that present new findings and contribute to our knowledge of the advancements in these fields. Additionally, we are open to considering review articles and case series that offer valuable insights into the current and future directions of ocular and systemic disease management.

Dr. Zhihong (Jewel) Hu
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

  • ocular diseases
  • oculomics
  • early detection, diagnosis, analysis, prevention, treatment, and advancements of ocular and systemic diseases
  • transformed ophthalmology
  • personalized patient care
  • precision-driven patient care

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Published Papers (1 paper)

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Research

13 pages, 7299 KiB  
Article
Automated Detection and Biomarker Identification Associated with the Structural and Functional Progression of Glaucoma on Longitudinal Color Fundus Images
by Iyad Majid, Zubin Mishra, Ziyuan Chris Wang, Vikas Chopra, Dale Heuer and Zhihong Jewel Hu
Appl. Sci. 2025, 15(3), 1627; https://doi.org/10.3390/app15031627 - 6 Feb 2025
Viewed by 645
Abstract
The diagnosis of primary open-angle glaucoma (POAG) progression based on structural imaging such as color fundus photos (CFPs) is challenging due to the limited number of early biomarkers, as commonly determined by clinicians, and the inherent variability in optic nerve heads (ONHs) between [...] Read more.
The diagnosis of primary open-angle glaucoma (POAG) progression based on structural imaging such as color fundus photos (CFPs) is challenging due to the limited number of early biomarkers, as commonly determined by clinicians, and the inherent variability in optic nerve heads (ONHs) between individuals. Moreover, while visual function is the main concern for glaucoma patients, and the ability to infer future visual outcome from imaging will benefit patients by early intervention, there is currently no available tool for this. To detect glaucoma progression from ocular hypertension both structurally and functionally, and identify potential objective early biomarkers associated with progression, we developed and evaluated deep convolutional long short-term memory (CNN-LSTM) neural network models using longitudinal CFPs from the Ocular Hypertension Treatment Study (OHTS). Patients were categorized into four diagnostic groups for model input: healthy, POAG with optic disc changes, POAG with visual field (VF) changes, and POAG with both optic disc and VF changes. Gradient-weighted class activation mapping (Grad-CAM) was employed for the post hoc visualization of image features, which may be associated with the objective POAG biomarkers (rather than the biomarkers determined by clinicians). The CNN-LSTM models for the detection of POAG progression achieved promising performance results both for the structural and functional models, with an area under curve (AUC) performance of 0.894 for the disc-only group, 0.911 for the VF-only group, and 0.939 for the disc and VF group. The model demonstrated high precision (0.984) and F1-score (0.963) in the both-changes group (disc + VF). Our preliminary investigation for early POAG biomarkers with Grad-CAM feature visualization signified that retinal vasculature could serve as an early and objective biomarker for POAG progression, complementing the traditionally used optic disc features and improving clinical workflows. Full article
(This article belongs to the Special Issue Technological Advances in Ocular Diseases and Oculomics)
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