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Bioengineering
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Biomedical Imaging and Analysis of the Eye: Second Edition
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Biomedical Imaging and Analysis of the Eye: Second Edition

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

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 10786

Special Issue Editors

Dr. Xuejun Qian

E-Mail Website
Guest Editor
School of Biomedical Engineering, Shanghai Tech University, Shanghai, China
Interests: biomedical imaging; multi-modal imaging; functional imaging; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qifa Zhou

E-Mail Website
Guest Editor
Department of Biomedical Engineering and Ophthalmology, University of Southern California, Los Angeles, CA 90007, USA
Interests: MEMS; biomedical imaging; photoacoustic imaging; ultrasound; elastography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The eye is an important aspect of human health since people rely on their eyes to see and make sense of the world around them. Investigating the condition of the eye is not only important to identify eye diseases but also to predict the risk of non-ocular diseases such as diabetes, anemia, cardiovascular risk, chronic kidney disease, and other systemic parameters.

The aim of this Special Issue is to provide a forum to share insights into medical imaging modalities and machine/deep learning tools to advance the development of algorithms, systems, and clinical applicability for imaging and analyzing the eye. We welcome both original research and review articles. Potential topics include, but are not limited to, the following:

  • Advanced imaging technique and system for the eye;
  • Machine or deep learning-based image analysis using eye imaging;
  • Imaging-guided eye surgery and treatment;
  • Image processing in ocular imaging;
  • Mathematical or statistical modeling of the eye;
  • Multi-dimensional and multi-modality fusion ocular imaging.

You may like to read the papers in our precious issue:

Volume 1 (10 papers): https://www.mdpi.com/journal/bioengineering/special_issues/S2G5Q9113W

Dr. Xuejun Qian
Prof. Dr. Qifa Zhou
Guest Editors

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

  • ultrasound imaging
  • optical coherence tomography
  • functional imaging
  • machine/deep learning
  • ocular tissue

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

Related Special Issue

Published Papers (5 papers)

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Research

16 pages, 2307 KiB  
Article
Deep Learning Approach Predicts Longitudinal Retinal Nerve Fiber Layer Thickness Changes
by Jalil Jalili, Evan Walker, Christopher Bowd, Akram Belghith, Michael H. Goldbaum, Massimo A. Fazio, Christopher A. Girkin, Carlos Gustavo De Moraes, Jeffrey M. Liebmann, Robert N. Weinreb, Linda M. Zangwill and Mark Christopher
Bioengineering 2025, 12(2), 139; https://doi.org/10.3390/bioengineering12020139 - 31 Jan 2025
Viewed by 911
Abstract
This study aims to develop deep learning (DL) models to predict the retinal nerve fiber layer (RNFL) thickness changes in glaucoma, facilitating the early diagnosis and monitoring of disease progression. Using the longitudinal data from two glaucoma studies (Diagnostic Innovations in Glaucoma Study [...] Read more.
This study aims to develop deep learning (DL) models to predict the retinal nerve fiber layer (RNFL) thickness changes in glaucoma, facilitating the early diagnosis and monitoring of disease progression. Using the longitudinal data from two glaucoma studies (Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES)), we constructed models using optical coherence tomography (OCT) scans from 251 participants (437 eyes). The models were trained to predict the RNFL thickness at a future visit based on previous scans. We evaluated four models: linear regression (LR), support vector regression (SVR), gradient boosting regression (GBR), and a custom 1D convolutional neural network (CNN). The GBR model achieved the best performance in predicting pointwise RNFL thickness changes (MAE = 5.2 μm, R2 = 0.91), while the custom 1D CNN excelled in predicting changes to average global and sectoral RNFL thickness, providing greater resolution and outperforming the traditional models (MAEs from 2.0–4.2 μm, R2 from 0.94–0.98). Our custom models used a novel approach that incorporated longitudinal OCT imaging to achieve consistent performance across different demographics and disease severities, offering potential clinical decision support for glaucoma diagnosis. Patient-level data splitting enhances the evaluation robustness, while predicting detailed RNFL thickness provides a comprehensive understanding of the structural changes over time. Full article
(This article belongs to the Special Issue Biomedical Imaging and Analysis of the Eye: Second Edition)
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21 pages, 5523 KiB  
Article
Electroretinogram Analysis Using a Short-Time Fourier Transform and Machine Learning Techniques
by Faisal Albasu, Mikhail Kulyabin, Aleksei Zhdanov, Anton Dolganov, Mikhail Ronkin, Vasilii Borisov, Leonid Dorosinsky, Paul A. Constable, Mohammed A. Al-masni and Andreas Maier
Bioengineering 2024, 11(9), 866; https://doi.org/10.3390/bioengineering11090866 - 26 Aug 2024
Cited by 5 | Viewed by 1771
Abstract
Electroretinography (ERG) is a non-invasive method of assessing retinal function by recording the retina’s response to a brief flash of light. This study focused on optimizing the ERG waveform signal classification by utilizing Short-Time Fourier Transform (STFT) spectrogram preprocessing with a machine learning [...] Read more.
Electroretinography (ERG) is a non-invasive method of assessing retinal function by recording the retina’s response to a brief flash of light. This study focused on optimizing the ERG waveform signal classification by utilizing Short-Time Fourier Transform (STFT) spectrogram preprocessing with a machine learning (ML) decision system. Several window functions of different sizes and window overlaps were compared to enhance feature extraction concerning specific ML algorithms. The obtained spectrograms were employed to train deep learning models alongside manual feature extraction for more classical ML models. Our findings demonstrated the superiority of utilizing the Visual Transformer architecture with a Hamming window function, showcasing its advantage in ERG signal classification. Also, as a result, we recommend the RF algorithm for scenarios necessitating manual feature extraction, particularly with the Boxcar (rectangular) or Bartlett window functions. By elucidating the optimal methodologies for feature extraction and classification, this study contributes to advancing the diagnostic capabilities of ERG analysis in clinical settings. Full article
(This article belongs to the Special Issue Biomedical Imaging and Analysis of the Eye: Second Edition)
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11 pages, 1271 KiB  
Article
Real-World Weekly Efficacy Analysis of Faricimab in Patients with Age-Related Macular Degeneration
by Daniel R. Muth, Katrin F. Fasler, Anders Kvanta, Magdalena Rejdak, Frank Blaser and Sandrine A. Zweifel
Bioengineering 2024, 11(5), 478; https://doi.org/10.3390/bioengineering11050478 - 10 May 2024
Cited by 5 | Viewed by 2058
Abstract
Objectives: This study entailed a weekly analysis of real-world data (RWD) on the safety and efficacy of intravitreal (IVT) faricimab in neovascular age-related macular degeneration (nAMD). Methods: A retrospective, single-centre clinical trial was conducted at the Department of Ophthalmology, University Hospital [...] Read more.
Objectives: This study entailed a weekly analysis of real-world data (RWD) on the safety and efficacy of intravitreal (IVT) faricimab in neovascular age-related macular degeneration (nAMD). Methods: A retrospective, single-centre clinical trial was conducted at the Department of Ophthalmology, University Hospital Zurich, University of Zurich, Switzerland, approved by the Cantonal Ethics Committee of Zurich, Switzerland. Patients with nAMD were included. Data from patient charts and imaging were analysed. The safety and efficacy of the first faricimab injection were evaluated weekly until 4 weeks after injection. Results: Sixty-three eyes with a complete 4-week follow-up were enrolled. Six eyes were treatment-naïve; fifty-seven eyes were switched to faricimab from another treatment. Neither group showed signs of retinal vasculitis during the 4 weeks after injection. Central subfield thickness (CST) and volume (CSV) showed a statistically significant decrease compared to the baseline in the switched group (CST: p = 0.00383; CSV: p = 0.00702) after 4 weeks. The corrected visual acuity returned to the baseline level in both groups. The macular neovascularization area decreased in both groups, but this was not statistically significant. A complete resolution of sub- and intraretinal fluid after 4 weeks was found in 40% (switched) and 75% (naïve) of the treated patients. Conclusions: The weekly follow-ups reflect the structure–function relationship beginning with a fast functional improvement within two weeks after injection followed by a return to near-baseline levels after week 3. The first faricimab injection in our cohort showed a high safety profile and a statistically significant reduction in macular oedema in switched nAMD patients. Full article
(This article belongs to the Special Issue Biomedical Imaging and Analysis of the Eye: Second Edition)
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16 pages, 4769 KiB  
Article
OCT Intensity of the Region between Outer Retina Band 2 and Band 3 as a Biomarker for Retinal Degeneration and Therapy
by Yong Zeng, Shasha Gao, Yichao Li, Dario Marangoni, Tharindu De Silva, Wai T. Wong, Emily Y. Chew, Xun Sun, Tiansen Li, Paul A. Sieving and Haohua Qian
Bioengineering 2024, 11(5), 449; https://doi.org/10.3390/bioengineering11050449 - 1 May 2024
Viewed by 2629
Abstract
Optical coherence tomography (OCT) is widely used to probe retinal structure and function. This study investigated the outer retina band (ORB) pattern and reflective intensity for the region between bands 2 and 3 (Dip) in three mouse models of inherited retinal degeneration (Rs1KO, [...] Read more.
Optical coherence tomography (OCT) is widely used to probe retinal structure and function. This study investigated the outer retina band (ORB) pattern and reflective intensity for the region between bands 2 and 3 (Dip) in three mouse models of inherited retinal degeneration (Rs1KO, TTLL5KO, RPE65KO) and in human AMD patients from the A2A database. OCT images were manually graded, and reflectivity signals were used to calculate the Dip ratio. Qualitative analyses demonstrated the progressive merging band 2 and band 3 in all three mouse models, leading to a reduction in the Dip ratio compared to wildtype (WT) controls. Gene replacement therapy in Rs1KO mice reverted the ORB pattern to one resembling WT and increased the Dip ratio. The degree of anatomical rescue in these mice was highly correlated with level of transgenic RS1 expression and with the restoration of ERG b-wave amplitudes. While the inner retinal cavity was significantly enlarged in dark-adapted Rs1KO mice, the Dip ratio was not altered. A reduction of the Dip ratio was also detected in AMD patients compared with healthy controls and was also positively correlated with AMD severity on the AMD score. We propose that the ORB and Dip ratio can be used as non-invasive early biomarkers for retina health, which can be used to probe therapeutic gene expression and to evaluate the effectiveness of therapy. Full article
(This article belongs to the Special Issue Biomedical Imaging and Analysis of the Eye: Second Edition)
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11 pages, 2285 KiB  
Article
Ultrasound Flow Imaging Study on Rat Brain with Ultrasound and Light Stimulations
by Junhang Zhang, Chen Gong, Zihan Yang, Fan Wei, Xin Sun, Jie Ji, Yushun Zeng, Chi-feng Chang, Xunan Liu, Deepthi S. Rajendran Nair, Biju B. Thomas and Qifa Zhou
Bioengineering 2024, 11(2), 174; https://doi.org/10.3390/bioengineering11020174 - 10 Feb 2024
Cited by 1 | Viewed by 2602
Abstract
Functional ultrasound (fUS) flow imaging provides a non-invasive method for the in vivo study of cerebral blood flow and neural activity. This study used functional flow imaging to investigate rat brain’s response to ultrasound and colored-light stimuli. Male Long-Evan rats were exposed to [...] Read more.
Functional ultrasound (fUS) flow imaging provides a non-invasive method for the in vivo study of cerebral blood flow and neural activity. This study used functional flow imaging to investigate rat brain’s response to ultrasound and colored-light stimuli. Male Long-Evan rats were exposed to direct full-field strobe flashes light and ultrasound stimulation to their retinas, while brain activity was measured using high-frequency ultrasound imaging. Our study found that light stimuli, particularly blue light, elicited strong responses in the visual cortex and lateral geniculate nucleus (LGN), as evidenced by changes in cerebral blood volume (CBV). In contrast, ultrasound stimulation elicited responses undetectable with fUS flow imaging, although these were observable when directly measuring the brain’s electrical signals. These findings suggest that fUS flow imaging can effectively differentiate neural responses to visual stimuli, with potential applications in understanding visual processing and developing new diagnostic tools. Full article
(This article belongs to the Special Issue Biomedical Imaging and Analysis of the Eye: Second Edition)
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