Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.9
3.3
Journals
Diagnostics
Special Issues
Imaging and AI Applications in Glaucoma
share announcement

Imaging and AI Applications in Glaucoma

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 2025) | Viewed by 597

Special Issue Editor

Dr. Henry Shen-Lih Chen

E-Mail Website
Guest Editor
1. College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan
2. Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou 333423, Taiwan
Interests: glaucoma; artificial intelligence; OCT

Special Issue Information

Dear Colleagues,

The field of ophthalmology is undergoing a breakthrough evolution with the advent of advanced imaging technologies and the integration of artificial intelligence (AI). This Special Issue, entitled "Imaging and AI Applications in Glaucoma," will specifically focus on the recent developments in imaging technological advancements and the transformative role of AI in glaucoma diagnosis and healthcare. By bringing together contributions from a diverse range of experts in ophthalmology, medical imaging, and AI, this Special Issue aims to provide a comprehensive overview of current innovations and future directions in the management of glaucoma, ultimately improving patient outcomes.

Scope and Focus:

This Special Issue will delve into the recent advancements in imaging modalities and AI applications that are poised to reshape the landscape of glaucoma management. This Special Issue will cover a broad spectrum of topics, including, but not limited to, the following:

  1. Advanced imaging technologies: early diagnosis, progression detection of OCT, or OCT angiography.
  2. AI in glaucoma diagnosis and prognosis:

   - Machine learning algorithms and deep learning models designed for early detection of glaucoma through automated image analysis of OCT scans, fundus photographs, and visual field tests.

   - Predictive modeling using AI to assess the risk of glaucoma progression, aiding in timely intervention and personalized treatment plans.

  1. Clinical applications and case studies:

   - Real-world applications of AI in clinical settings, including case studies demonstrating improved diagnostic accuracy and treatment outcomes.

We welcome you to submit a research article or review for this Special Issue. For more details, please visit our website.

Dr. Henry Shen-Lih Chen
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

  • glaucoma
  • artificial intelligence
  • OCT

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

14 pages, 785 KiB  
Article
Novel Structure–Function Models for Estimating Retinal Ganglion Cell Count Using Pattern Electroretinography in Glaucoma Suspects
by Andrew Tirsi, Isabella Tello, Timothy Foster, Rushil Kumbhani, Nicholas Leung, Samuel Potash, Derek Orshan and Celso Tello
Diagnostics 2025, 15(14), 1756; https://doi.org/10.3390/diagnostics15141756 - 11 Jul 2025
Abstract
Background/Objectives: The early detection of retinal ganglion cell (RGC) dysfunction is critical for timely intervention in glaucoma suspects (GSs). The combined structure–function index (CSFI), which uses visual field and optical coherence tomography (OCT) data to estimate RGC counts, may be of limited [...] Read more.
Background/Objectives: The early detection of retinal ganglion cell (RGC) dysfunction is critical for timely intervention in glaucoma suspects (GSs). The combined structure–function index (CSFI), which uses visual field and optical coherence tomography (OCT) data to estimate RGC counts, may be of limited utility in GSs. This study evaluates whether steady-state pattern electroretinogram (ssPERG)-derived estimates better predict early structural changes in GSs. Methods: Fifty eyes from 25 glaucoma suspects underwent ssPERG and spectral-domain OCT. Estimated RGC counts (eRGCC) were calculated using three parameters: ssPERG-Magnitude (eRGCCMag), ssPERG-MagnitudeD (eRGCCMagD), and CSFI (eRGCCCSFI). Linear regression and multivariable models were used to assess each model’s ability to predict the average retinal nerve fiber layer thickness (AvRNFLT), average ganglion cell layer–inner plexiform layer thickness (AvGCL-IPLT), and rim area. Results: eRGCCMag and eRGCCMagD were significantly correlated with eRGCCCSFI. Both PERG-derived models outperformed eRGCCCSFI in predicting AvRNFLT and AvGCL-IPLT, with eRGCCMagD showing the strongest association with AvGCL-IPLT. Conversely, the rim area was best predicted by eRGCCMag and eRGCCCSFI. These findings support a linear relationship between ssPERG parameters and early RGC structural changes, while the logarithmic nature of visual field loss may limit eRGCCCSFI’s predictive accuracy in GSs. Conclusions: ssPERG-derived estimates, particularly eRGCCMagD, better predict early structural changes in GSs than eRGCCCSFI. eRGCCMagD’s superior performance in predicting GCL-IPLT highlights its potential utility as an early biomarker of glaucomatous damage. ssPERG-based models offer a simpler and more sensitive tool for early glaucoma risk stratification, and may provide a clinical benchmark for tracking recoverable RGC dysfunction and treatment response. Full article
(This article belongs to the Special Issue Imaging and AI Applications in Glaucoma)
►▼ Show Figures

Figure 1

20 pages, 1534 KiB  
Article
Retinal Vessel Diameter Reductions Are Associated with Retinal Ganglion Cell Dysfunction, Thinning of the Ganglion Cell and Inner Plexiform Layers, and Decreased Visual Field Global Indices in Glaucoma Suspects
by Andrew Tirsi, Nicholas Leung, Rohun Gupta, Sungmin Hong, Derek Orshan, Joby Tsai, Corey Ross Lacher, Isabella Tello, Samuel Potash, Timothy Foster, Rushil Kumbhani and Celso Tello
Diagnostics 2025, 15(13), 1700; https://doi.org/10.3390/diagnostics15131700 - 3 Jul 2025
Viewed by 297
Abstract
Background/Objectives: The aim of this study was to evaluate the associations between optical coherence tomography angiography (OCTA)-based retinal vessel diameter (RVD) measurements, with retinal ganglion cell (RGC) function assessed by means of steady-state pattern electroretinography (ssPERG) using ganglion cell layer-inner plexiform layer [...] Read more.
Background/Objectives: The aim of this study was to evaluate the associations between optical coherence tomography angiography (OCTA)-based retinal vessel diameter (RVD) measurements, with retinal ganglion cell (RGC) function assessed by means of steady-state pattern electroretinography (ssPERG) using ganglion cell layer-inner plexiform layer thickness (GCL-IPLT) measurements and with Humphrey field analyzer (HFA) global indices in glaucoma suspects (GSs). Methods: Thirty-one eyes (20 participants) underwent a comprehensive ophthalmologic examination, ssPERG measurements utilizing the PERGLA paradigm, HFA, optical coherence tomography (OCT), and OCTA. The OCTA scans were processed using ImageJ software, Version 1.53, allowing for measurement of the RVD. Multiple linear regression models were used. Results: After controlling for age, race, central corneal thickness (CCT), and spherical equivalent (SE), a linear regression analysis found that the RVD explained the 4.7% variance in magnitude (Mag) (p = 0.169), 9.2% variance in magnitudeD (MagD) (p = 0.021), and 16.9% variance in magnitudeD/magnitude (p = 0.009). After controlling for age, CCT, and signal strength (SS), a linear regression analysis found that the RVD was significantly associated with the GCL-IPLT measurements (average GCL-IPL, minimum GCL-IPL, superior, superonasal, inferior, and inferonasal sectors) (p ≤ 0.023). An identical regression analysis where the RVD was replaced with the PERG parameters showed a significant association between the MagD and almost all GCI-IPLT measurements. RVD measurements were significantly associated with HFA VFI 24-2 (p = 0.004), MD 24-2 (p < 0.001), and PSD 24-2 (p = 0.009). Conclusions: Decreased RVD measurements were significantly associated with RGC dysfunction, decreased GCL-IPLT, and all HFA global indices in the GSs. Full article
(This article belongs to the Special Issue Imaging and AI Applications in Glaucoma)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop