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Bioengineering
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Recent Advances in Optical Imaging and Machine Learning in Biomedicine
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Recent Advances in Optical Imaging and Machine Learning in Biomedicine

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 3520

Special Issue Editor

Dr. Daniel L. Farkas

E-Mail Website
Guest Editor
Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
Interests: optical imaging; machine learning; biomedicine; bioengineering; biophotonics; translational research; interdisciplinary

Special Issue Information

Dear Colleagues,

The power, discrimination ability, and versatility of light as an investigational tool, further potentiated by sophisticated analyses involving machine learning (ML) and artificial intelligence (AI), have helped establish optical imaging as a major field in biomedicine. The best research is high-tech, but also interdisciplinary in nature and translational in its goals, aiming to ultimately save lives and reduce suffering. The discipline that represents the connections, mediation and synergies needed for the best results is bioengineering, and therefore, this journal is bringing together a sampling of important contributions on topics ranging from new enabling technologies to addressing major unmet needs in the clinic.

This Special Issue will showcase research papers, short communications, and review articles on a range of bioengineered solutions for investigating the living state at all levels of biological organization, from molecules to humans, for better understanding and intervention. Emphasis should be on approaches that are realistic, but aim to be disruptive, noninvasive, and quantitative, and which help to better connect, in time and space, diagnosis and treatment. Topics of interest include, but are not limited to:

  • Utilizing multiple properties of light (intensity, wavelength, duration, polarization, and coherence) and new (including intrinsic) biomarkers to create a high-resolution, more comprehensive characterization of living tissues of interest;
  • Exploring how ML and AI can add to the quality and reliability of optical imaging studies, with important applications;
  • Real-time optical biopsy allowing for better intrasurgical navigation, image-based decision-making, and intervention;
  • Early, reliable diagnosis and enhanced treatment assessment in major diseases such as cancer and neurodegeneration.

Dr. Daniel L. Farkas
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

  • optical imaging
  • machine learning
  • biomedicine
  • bioengineering
  • biophotonics
  • translational research
  • interdisciplinary

Published Papers (3 papers)

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Research

15 pages, 4395 KiB  
Article
Self-Guided Algorithm for Fast Image Reconstruction in Photo-Magnetic Imaging: Artificial Intelligence-Assisted Approach
by Maha Algarawi, Janaki S. Saraswatula, Rajas R. Pathare, Yang Zhang, Gyanesh A. Shah, Aydin Eresen, Gultekin Gulsen and Farouk Nouizi
Bioengineering 2024, 11(2), 126; https://doi.org/10.3390/bioengineering11020126 - 28 Jan 2024
Cited by 1 | Viewed by 1043
Abstract
Previously, we introduced photomagnetic imaging (PMI) that synergistically utilizes laser light to slightly elevate the tissue temperature and magnetic resonance thermometry (MRT) to measure the induced temperature. The MRT temperature maps are then converted into absorption maps using a dedicated PMI image reconstruction [...] Read more.
Previously, we introduced photomagnetic imaging (PMI) that synergistically utilizes laser light to slightly elevate the tissue temperature and magnetic resonance thermometry (MRT) to measure the induced temperature. The MRT temperature maps are then converted into absorption maps using a dedicated PMI image reconstruction algorithm. In the MRT maps, the presence of abnormalities such as tumors would create a notable high contrast due to their higher hemoglobin levels. In this study, we present a new artificial intelligence-based image reconstruction algorithm that improves the accuracy and spatial resolution of the recovered absorption maps while reducing the recovery time. Technically, a supervised machine learning approach was used to detect and delineate the boundary of tumors directly from the MRT maps based on their temperature contrast to the background. This information was further utilized as a soft functional a priori in the standard PMI algorithm to enhance the absorption recovery. Our new method was evaluated on a tissue-like phantom with two inclusions representing tumors. The reconstructed absorption map showed that the well-trained neural network not only increased the PMI spatial resolution but also improved the accuracy of the recovered absorption to as low as a 2% percentage error, reduced the artifacts by 15%, and accelerated the image reconstruction process approximately 9-fold. Full article
(This article belongs to the Special Issue Recent Advances in Optical Imaging and Machine Learning in Biomedicine)
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15 pages, 3566 KiB  
Article
Application of a Radiomics Machine Learning Model for Differentiating Aldosterone-Producing Adenoma from Non-Functioning Adrenal Adenoma
by Wenhua Yang, Yonghong Hao, Ketao Mu, Jianjun Li, Zihui Tao, Delin Ma and Anhui Xu
Bioengineering 2023, 10(12), 1423; https://doi.org/10.3390/bioengineering10121423 - 14 Dec 2023
Viewed by 862
Abstract
To evaluate the secretory function of adrenal incidentaloma, this study explored the usefulness of a contrast-enhanced computed tomography (CECT)-based radiomics model for distinguishing aldosterone-producing adenoma (APA) from non-functioning adrenal adenoma (NAA). Overall, 68 APA and 60 NAA patients were randomly assigned (8:2 ratio) [...] Read more.
To evaluate the secretory function of adrenal incidentaloma, this study explored the usefulness of a contrast-enhanced computed tomography (CECT)-based radiomics model for distinguishing aldosterone-producing adenoma (APA) from non-functioning adrenal adenoma (NAA). Overall, 68 APA and 60 NAA patients were randomly assigned (8:2 ratio) to either a training or a test cohort. In the training cohort, univariate and least absolute shrinkage and selection operator regression analyses were conducted to select the significant features. A logistic regression machine learning (ML) model was then constructed based on the radiomics score and clinical features. Model effectiveness was evaluated according to the receiver operating characteristic, accuracy, sensitivity, specificity, F1 score, calibration plots, and decision curve analysis. In the test cohort, the area under the curve (AUC) of the Radscore model was 0.869 [95% confidence interval (CI), 0.734–1.000], and the accuracy, sensitivity, specificity, and F1 score were 0.731, 1.000, 0.583, and 0.900, respectively. The Clinic–Radscore model had an AUC of 0.994 [95% CI, 0.978–1.000], and the accuracy, sensitivity, specificity, and F1 score values were 0.962, 0.929, 1.000, and 0.931, respectively. In conclusion, the CECT-based radiomics and clinical radiomics ML model exhibited good diagnostic efficacy in differentiating APAs from NAAs; this non-invasive, cost-effective, and efficient method is important for the management of adrenal incidentaloma. Full article
(This article belongs to the Special Issue Recent Advances in Optical Imaging and Machine Learning in Biomedicine)
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16 pages, 3491 KiB  
Article
Unilateral Mitochondrial–Hemodynamic Coupling and Bilateral Connectivity in the Prefrontal Cortices of Young and Older Healthy Adults
by Claire Sissons, Fiza Saeed, Caroline Carter, Kathy Lee, Kristen Kerr, Sadra Shahdadian and Hanli Liu
Bioengineering 2023, 10(11), 1336; https://doi.org/10.3390/bioengineering10111336 - 20 Nov 2023
Viewed by 876
Abstract
A recent study demonstrated that noninvasive measurements of cortical hemodynamics and metabolism in the resting human prefrontal cortex can facilitate quantitative metrics of unilateral mitochondrial–hemodynamic coupling and bilateral connectivity in infraslow oscillation frequencies in young adults. The infraslow oscillation includes three distinct vasomotions [...] Read more.
A recent study demonstrated that noninvasive measurements of cortical hemodynamics and metabolism in the resting human prefrontal cortex can facilitate quantitative metrics of unilateral mitochondrial–hemodynamic coupling and bilateral connectivity in infraslow oscillation frequencies in young adults. The infraslow oscillation includes three distinct vasomotions with endogenic (E), neurogenic (N), and myogenic (M) frequency bands. The goal of this study was to prove the hypothesis that there are significant differences between young and older adults in the unilateral coupling (uCOP) and bilateral connectivity (bCON) in the prefrontal cortex. Accordingly, we performed measurements from 24 older adults (67.2 ± 5.9 years of age) using the same two-channel broadband near-infrared spectroscopy (bbNIRS) setup and resting-state experimental protocol as those in the recent study. After quantification of uCOP and bCON in three E/N/M frequencies and statistical analysis, we demonstrated that older adults had significantly weaker bilateral hemodynamic connectivity but significantly stronger bilateral metabolic connectivity than young adults in the M band. Furthermore, older adults exhibited significantly stronger unilateral coupling on both prefrontal sides in all E/N/M bands, particularly with a very large effect size in the M band (>1.9). These age-related results clearly support our hypothesis and were well interpreted following neurophysiological principles. The key finding of this paper is that the neurophysiological metrics of uCOP and bCON are highly associated with age and may have the potential to become meaningful features for human brain health and be translatable for future clinical applications, such as the early detection of Alzheimer’s disease. Full article
(This article belongs to the Special Issue Recent Advances in Optical Imaging and Machine Learning in Biomedicine)
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