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Bioengineering
Special Issues
Translational AI and Computational Tools for Ophthalmic Disease
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Translational AI and Computational Tools for Ophthalmic Disease

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 3626

Special Issue Editor

Dr. Mark Christopher

E-Mail Website
Guest Editor
Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA
Interests: AI; deep learning; machine learning; image analysis; glaucoma; OCT; VF; structure-function

Special Issue Information

Dear Colleagues,

Over the past several years, there have been rapid advancements in machine/deep learning and artificial intelligence (AI). These advancements have had a large impact across many fields of study, including medicine. Given the data-rich nature of modern ophthalmic care, which makes use of not only patient information and clinical tests, but also extensive imaging and functional testing, this field is especially amenable to AI and other computational approaches. Indeed, much work has been carried out to apply AI-based techniques to ophthalmic care and has even resulted in the first FDA-approved autonomous AI diagnostic system in any field of medicine (LumineticsCore by Digital Diagnostics, formerly IDx-DR). However, despite the large body of work applying AI techniques to ophthalmology, relatively little has been translated into clinical settings so far. This Special Issue focuses on the application of AI and other computational tools in building translational tools that address pressing needs in ophthalmic care. Topics of interest include (but are not limited to) work in developing and/or evaluating AI tools to address screening and diagnosis of ophthalmic diseases, predicting disease progression, forecasting the need for interventions, and clinical decision support for disease management. Work across all aspects of ophthalmic care is relevant and the focus should be on translation into clinical settings to improve care.

Dr. Mark Christopher
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • AI
  • deep learning
  • ophthalmology
  • translational

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Published Papers (3 papers)

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Research

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17 pages, 1489 KiB  
Article
Interpretable Machine Learning Predictions of Bruch’s Membrane Opening-Minimum Rim Width Using Retinal Nerve Fiber Layer Values and Visual Field Global Indexes
by Sat Byul Seo and Hyun-kyung Cho
Bioengineering 2025, 12(3), 321; https://doi.org/10.3390/bioengineering12030321 - 20 Mar 2025
Viewed by 329
Abstract
The aim of this study was to predict Bruch’s membrane opening-minimum rim Width (BMO-MRW), a relatively new parameter using conventional optical coherence tomography (OCT) parameter, using retinal nerve fibre layer (RNFL) thickness and visual field (VF) global indexes (MD, PSD, and VFI). We [...] Read more.
The aim of this study was to predict Bruch’s membrane opening-minimum rim Width (BMO-MRW), a relatively new parameter using conventional optical coherence tomography (OCT) parameter, using retinal nerve fibre layer (RNFL) thickness and visual field (VF) global indexes (MD, PSD, and VFI). We developed an interpretable machine learning model that integrates structural and functional parameters to predict BMO-MRW. The model achieved the highest predictive accuracy in the inferotemporal sector (R2 = 0.68), followed by the global region (R2 = 0.67) and the superotemporal sector (R2 = 0.64). Through SHAP (SHapley Additive exPlanations) analysis, we demonstrated that RNFL parameters were significant contributing parameters to the prediction of various BMO-MRW parameters, with age and PSD also identified as critical factors. Our machine learning model could provide useful clinical information about the management of glaucoma when BMO-MRW is not available. Our machine learning model has the potential to be highly beneficial in clinical practice for glaucoma diagnosis and the monitoring of disease progression. Full article
(This article belongs to the Special Issue Translational AI and Computational Tools for Ophthalmic Disease)
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17 pages, 14286 KiB  
Article
Anomaly Detection in Optical Coherence Tomography Angiography (OCTA) with a Vector-Quantized Variational Auto-Encoder (VQ-VAE)
by Hana Jebril, Meltem Esengönül and Hrvoje Bogunović
Bioengineering 2024, 11(7), 682; https://doi.org/10.3390/bioengineering11070682 - 5 Jul 2024
Viewed by 1845
Abstract
Optical coherence tomography angiography (OCTA) provides detailed information on retinal blood flow and perfusion. Abnormal retinal perfusion indicates possible ocular or systemic disease. We propose a deep learning-based anomaly detection model to identify such anomalies in OCTA. It utilizes two deep learning approaches. [...] Read more.
Optical coherence tomography angiography (OCTA) provides detailed information on retinal blood flow and perfusion. Abnormal retinal perfusion indicates possible ocular or systemic disease. We propose a deep learning-based anomaly detection model to identify such anomalies in OCTA. It utilizes two deep learning approaches. First, a representation learning with a Vector-Quantized Variational Auto-Encoder (VQ-VAE) followed by Auto-Regressive (AR) modeling. Second, it exploits epistemic uncertainty estimates from Bayesian U-Net employed to segment the vasculature on OCTA en face images. Evaluation on two large public datasets, DRAC and OCTA-500, demonstrates effective anomaly detection (an AUROC of 0.92 for the DRAC and an AUROC of 0.75 for the OCTA-500) and localization (a mean Dice score of 0.61 for the DRAC) on this challenging task. To our knowledge, this is the first work that addresses anomaly detection in OCTA. Full article
(This article belongs to the Special Issue Translational AI and Computational Tools for Ophthalmic Disease)
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Review

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13 pages, 480 KiB  
Review
Applications of Machine Learning-Driven Molecular Models for Advancing Ophthalmic Precision Medicine
by Rahul Kumar, Joshua Ong, Ethan Waisberg, Ryung Lee, Tuan Nguyen, Phani Paladugu, Maria Chiara Rivolta, Chirag Gowda, John Vincent Janin, Jeremy Saintyl, Dylan Amiri, Ansh Gosain and Ram Jagadeesan
Bioengineering 2025, 12(2), 156; https://doi.org/10.3390/bioengineering12020156 - 6 Feb 2025
Viewed by 1083
Abstract
Ophthalmic diseases such as glaucoma, age-related macular degeneration (ARMD), and optic neuritis involve complex molecular and cellular disruptions that challenge current diagnostic and therapeutic approaches. Advanced artificial intelligence (AI) and machine learning (ML) models offer a novel lens to analyze these diseases by [...] Read more.
Ophthalmic diseases such as glaucoma, age-related macular degeneration (ARMD), and optic neuritis involve complex molecular and cellular disruptions that challenge current diagnostic and therapeutic approaches. Advanced artificial intelligence (AI) and machine learning (ML) models offer a novel lens to analyze these diseases by integrating diverse datasets, identifying patterns, and enabling precision medicine strategies. Over the past decade, applications of AI in ophthalmology have expanded from imaging-based diagnostics to molecular-level modeling, bridging critical gaps in understanding disease mechanisms. This paper systematically reviews the application of AI-driven methods, including reinforcement learning (RL), graph neural networks (GNNs), Bayesian inference, and generative adversarial networks (GANs), in the context of these ophthalmic conditions. RL models simulate transcription factor dynamics in hypoxic or inflammatory environments, offering insights into disrupted molecular pathways. GNNs map intricate molecular networks within affected tissues, identifying key inflammatory or degenerative drivers. Bayesian inference provides probabilistic models for predicting disease progression and response to therapies, while GANs generate synthetic datasets to explore therapeutic interventions. By contextualizing these AI tools within the broader framework of ophthalmic disease management, this review highlights their potential to transform diagnostic precision and therapeutic outcomes. Ultimately, this work underscores the need for continued interdisciplinary collaboration to harness AI’s potential in advancing the field of ophthalmology and improving patient care. Full article
(This article belongs to the Special Issue Translational AI and Computational Tools for Ophthalmic Disease)
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