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Bioengineering
Special Issues
Retinal Biomarkers: Seeing Diseases in the Eye
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Retinal Biomarkers: Seeing Diseases in the Eye

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

Deadline for manuscript submissions: 15 December 2025 | Viewed by 1490

Special Issue Editors

Dr. Paul Constable

E-Mail Website
Guest Editor
Caring Futures Institute, College of Nursing and Health Sciences, Flinders University, Adelaide, SA 5000, Australia
Interests: neurodevelopment; visual electrophysiology; biomarkers
Dr. Hugo F. Posada-Quintero

E-Mail Website
Guest Editor
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
Interests: electro-physiological signals; electrodermal activity; heart rate variability; electromyography; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We request papers that use structural and/or functional measures of the retina to enhance diagnosis in retinal and neurological disorders. With a growing interest in applying AI for disease classification, the identification of retinal biomarkers has been used to support clinical diagnoses and improved screening for retinal and neurological disorders. This Special Issue will highlight innovative works in this field by using different tools that have been applied to it, including electrophysiology and imaging methods.

This Special Issue encourages submissions that cover, among others, the following topics:

  • Structural measures of the retina for diagnosing disorders;
  • Functional retinal assessments in neurological diseases;
  • Identification and utilization of retinal biomarkers;
  • Integration of structural and functional retinal data;
  • Application of AI in retinal disease classification;
  • Advanced electrophysiology and imaging techniques;
  • Development of new tools and technologies for retinal assessment;
  • Use of retinal imaging to detect neurological disease biomarkers;
  • AI-supported screening methods for retinal and neurological disorders;
  • Machine learning and deep learning in retinal imaging analysis.

Dr. Paul Constable
Dr. Hugo F. Posada-Quintero
Guest Editors

Dr. Mikhail Kulyabin
Dr. Javier Orlando Pinzón-Arenas
Dr. Aleksei Zhdanov
Guest Editor Assistants

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

  • biomarker
  • retina
  • structure
  • function
  • machine learning
  • deep learning
  • classification
  • imaging
  • electrophysiology

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Published Papers (1 paper)

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Research

26 pages, 2735 KB  
Article
Time Series Classification of Autism Spectrum Disorder Using the Light-Adapted Electroretinogram
by Sergey Chistiakov, Anton Dolganov, Paul A. Constable, Aleksei Zhdanov, Mikhail Kulyabin, Dorothy A. Thompson, Irene O. Lee, Faisal Albasu, Vasilii Borisov and Mikhail Ronkin
Bioengineering 2025, 12(9), 951; https://doi.org/10.3390/bioengineering12090951 - 2 Sep 2025
Viewed by 1037
Abstract
The clinical electroretinogram (ERG) is a non-invasive diagnostic test used to assess the functional state of the retina by recording changes in the bioelectric potential following brief flashes of light. The recorded ERG waveform offers ways for diagnosing both retinal dystrophies and neurological [...] Read more.
The clinical electroretinogram (ERG) is a non-invasive diagnostic test used to assess the functional state of the retina by recording changes in the bioelectric potential following brief flashes of light. The recorded ERG waveform offers ways for diagnosing both retinal dystrophies and neurological disorders such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and Parkinson’s disease. In this study, different time-series-based machine learning methods were used to classify ERG signals from ASD and typically developing individuals with the aim of interpreting the decisions made by the models to understand the classification process made by the models. Among the time-series classification (TSC) algorithms, the Random Convolutional Kernel Transform (ROCKET) algorithm showed the most accurate results with the fewest number of predictive errors. For the interpretation analysis of the model predictions, the SHapley Additive exPlanations (SHAP) algorithm was applied to each of the models’ predictions, with the ROCKET and KNeighborsTimeSeriesClassifier (TS-KNN) algorithms showing more suitability for ASD classification as they provided better-defined explanations by discarding the uninformative non-physiological part of the ERG waveform baseline signal and focused on the time regions incorporating the clinically significant a- and b-waves of the ERG. With the potential broadening scope of practice for visual electrophysiology within neurological disorders, TSC may support the identification of important regions in the ERG time series to support the classification of neurological disorders and potential retinal diseases. Full article
(This article belongs to the Special Issue Retinal Biomarkers: Seeing Diseases in the Eye)
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