Submit to Special Issue Submit Abstract to Special Issue Review for Bioengineering Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Recent Advances in Optical Imaging and Machine Learning in Biomedicine

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 14964

Share This Special Issue

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

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.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (9 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

14 pages, 5462 KiB

Open AccessArticle

AI-Assisted Detection for Early Screening of Acute Myeloid Leukemia Using Infrared Spectra and Clinical Biochemical Reports of Blood

by Chuan Zhang, Jialun Li, Wenda Luo and Sailing He

Bioengineering 2025, 12(4), 340; https://doi.org/10.3390/bioengineering12040340 - 26 Mar 2025

Viewed by 254

Abstract

Early detection and accurate diagnosis of leukemia pose significant challenges due to the disease’s complexity and the need for minimally invasive methods. Acute myeloid leukemia (AML) accounts for most cases of adult leukemia, and our goal is to screen out some AML from adults. In this work, we introduce an AI-enhanced system designed to facilitate early screening and diagnosis of AML among adults. Our approach combines the infrared absorption spectra of serum measured with attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), which identifies distinctive molecular signatures in lyophilized serum, together with standard clinical blood biochemical test results. We developed a multi-modality spectral transformer network (MSTNetwork) to generate latent space feature vectors from these datasets. Subsequently, these vectors were assessed using a linear discriminant analysis (LDA) algorithm to estimate the likelihood of acute myeloid leukemia. By analyzing blood samples from leukemia patients and the negative control (including non-leukemia patients and healthy individuals), we achieved rapid and accurate prediction and identification of acute myeloid leukemia among adults. Compared to conventional methods relying solely on either FTIR spectra or biochemical indicators of blood, our multi-modality classification system demonstrated higher accuracy and sensitivity, ultimately achieving an accuracy of 98% and a sensitivity of 98%, improving the sensitivity by 12% (compared with using only biochemical indicators) or over 6% (compared with using only FTIR spectra). Our multi-modality classification system is also very robust as it gave much smaller standard deviations of the accuracy and sensitivity. Beyond improving early detection, this work also contributes to a more sustainable and intelligent healthcare sector. Full article

(This article belongs to the Special Issue Recent Advances in Optical Imaging and Machine Learning in Biomedicine)

► Show Figures

Figure 1

13 pages, 2404 KiB

Open AccessArticle

Automated Cough Analysis with Convolutional Recurrent Neural Network

by Yiping Wang, Mustafaa Wahab, Tianqi Hong, Kyle Molinari, Gail M. Gauvreau, Ruth P. Cusack, Zhen Gao, Imran Satia and Qiyin Fang

Bioengineering 2024, 11(11), 1105; https://doi.org/10.3390/bioengineering11111105 - 1 Nov 2024

Viewed by 1303

Abstract

Chronic cough is associated with several respiratory diseases and is a significant burden on physical, social, and psychological health. Non-invasive, real-time, continuous, and quantitative monitoring tools are highly desired to assess cough severity, the effectiveness of treatment, and monitor disease progression in clinical practice and research. There are currently limited tools to quantitatively measure spontaneous coughs in daily living settings in clinical trials and in clinical practice. In this study, we developed a machine learning model for the detection and classification of cough sounds. Mel spectrograms are utilized as a key feature representation to capture the temporal and spectral characteristics of coughs. We applied this approach to automate cough analysis using 300 h of audio recordings from cough challenge clinical studies conducted in a clinical lab setting. A number of machine learning algorithms were studied and compared, including decision tree, support vector machine, k-nearest neighbors, logistic regression, random forest, and neural network. We identified that for this dataset, the CRNN approach is the most effective method, reaching 98% accuracy in identifying individual coughs from the audio data. These findings provide insights into the strengths and limitations of various algorithms, highlighting the potential of CRNNs in analyzing complex cough patterns. This research demonstrates the potential of neural network models in fully automated cough monitoring. The approach requires validation in detecting spontaneous coughs in patients with refractory chronic cough in a real-life setting. Full article

(This article belongs to the Special Issue Recent Advances in Optical Imaging and Machine Learning in Biomedicine)

► Show Figures

Figure 1

18 pages, 14036 KiB

Open AccessArticle

Tailoring Plasmonic Nanoheaters Size for Enhanced Theranostic Agent Performance

by Túlio de L. Pedrosa, Gabrielli M. F. de Oliveira, Arthur C. M. V. Pereira, Mariana J. B. da S. Crispim, Luzia A. da Silva, Marcilene S. da Silva, Ivone A. de Souza, Ana M. M. de A. Melo, Anderson S. L. Gomes and Renato E. de Araujo

Bioengineering 2024, 11(9), 934; https://doi.org/10.3390/bioengineering11090934 - 18 Sep 2024

Cited by 1 | Viewed by 1582

Abstract

The introduction of optimized nanoheaters, which function as theranostic agents integrating both diagnostic and therapeutic processes, holds significant promise in the medical field. Therefore, developing strategies for selecting and utilizing optimized plasmonic nanoheaters is crucial for the effective use of nanostructured biomedical agents. [...] Read more.

J_{o}

) as a figure of merit to identify high-performance plasmonic theranostic agents. A framework for optimizing metallic nanoparticles for heat generation was established, uncovering the size dependence of plasmonic nanoparticles optical heating. Gold nanospheres (AuNSs) with a diameter of 50 nm and gold nanorods (AuNRs) with dimensions of

41 \times 10

nm were identified as effective nanoheaters for visible (530 nm) and infrared (808 nm) excitation. Notably, AuNRs achieve higher

J_{o}

values than AuNSs, even when accounting for the possible orientations of the nanorods. Theoretical results estimate that

41 \times 10

nm gold nanorods have an average Joule number of 80, which is significantly higher compared to larger rods. The photothermal performance of optimal and suboptimal nanostructures was evaluated using photoacoustic imaging and photothermal therapy procedures. The photoacoustic images indicate that, despite having larger absorption cross-sections, the large nanoparticle volume of bigger particles leads to less efficient conversion of light into heat, which suggests that the use of optimized nanoparticles promotes higher contrast, benefiting photoacoustic-based procedures in diagnostic applications. The photothermal therapy procedure was performed on S180-bearing mice inoculated with

41 \times 10

nm and

90 \times 25

nm PEGylated AuNRs. Five minutes of laser irradiation of tumor tissue with

41 \times 10

nm produced an approximately 9.5% greater temperature rise than using

90 \times 25

AuNRs in the therapy trials. Optimizing metallic nanoparticles for heat generation may reduce the concentration of the nanoheaters used or decrease the light fluence for bioscience applications, paving the way for the development of more economical theranostic agents. Full article

(This article belongs to the Special Issue Recent Advances in Optical Imaging and Machine Learning in Biomedicine)

► Show Figures

Figure 1

19 pages, 5373 KiB

Open AccessArticle

An Improved Two-Shot Tracking Algorithm for Dynamics Analysis of Natural Killer Cells in Tumor Contexts

by Yanqing Zhou, Yiwen Tang and Zhibing Li

Bioengineering 2024, 11(6), 540; https://doi.org/10.3390/bioengineering11060540 - 24 May 2024

Viewed by 1465

Abstract

Natural killer cells (NKCs) are non-specific immune lymphocytes with diverse morphologies. Their broad killing effect on cancer cells has led to increased attention towards activating NKCs for anticancer immunotherapy. Consequently, understanding the motion characteristics of NKCs under different morphologies and modeling their collective dynamics under cancer cells has become crucial. However, tracking small NKCs in complex backgrounds poses significant challenges, and conventional industrial tracking algorithms often perform poorly on NKC tracking datasets. There remains a scarcity of research on NKC dynamics. In this paper, we utilize deep learning techniques to analyze the morphology of NKCs and their key points. After analyzing the shortcomings of common industrial multi-object tracking algorithms like DeepSORT in tracking natural killer cells, we propose Distance Cascade Matching and the Re-Search method to improve upon existing algorithms, yielding promising results. Through processing and tracking over 5000 frames of images, encompassing approximately 300,000 cells, we preliminarily explore the impact of NKCs’ cell morphology, temperature, and cancer cell environment on NKCs’ motion, along with conducting basic modeling. The main conclusions of this study are as follows: polarized cells are more likely to move along their polarization direction and exhibit stronger activity, and the maintenance of polarization makes them more likely to approach cancer cells; under equilibrium, NK cells display a Boltzmann distribution on the cancer cell surface. Full article

(This article belongs to the Special Issue Recent Advances in Optical Imaging and Machine Learning in Biomedicine)

► Show Figures

Figure 1

15 pages, 4395 KiB

Open AccessArticle

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 2265

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 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)

► Show Figures

Figure 1

15 pages, 3566 KiB

Open AccessArticle

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

Cited by 2 | Viewed by 1674

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) 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)

► Show Figures

Figure 1

16 pages, 3491 KiB

Open AccessArticle

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

Cited by 2 | Viewed by 1674

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 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)

► Show Figures

Figure 1

Review

Jump to: Research

18 pages, 3235 KiB

Open AccessReview

Recent Optical Coherence Tomography (OCT) Innovations for Increased Accessibility and Remote Surveillance

by Brigid C. Devine, Alan B. Dogan and Warren M. Sobol

Bioengineering 2025, 12(5), 441; https://doi.org/10.3390/bioengineering12050441 - 23 Apr 2025

Viewed by 439

Abstract

Optical Coherence Tomography (OCT) has revolutionized the diagnosis and management of retinal diseases, offering high-resolution, cross-sectional imaging that aids in early detection and continuous monitoring. However, traditional OCT devices are limited to clinical settings and require a technician to operate, which poses accessibility challenges such as a lack of appointment availability, patient and family burden of frequent transportation, and heightened healthcare costs, especially when treatable pathology is undetected. With the increasing global burden of retinal conditions such as age-related macular degeneration (AMD) and diabetic retinopathy, there is a critical need for improved accessibility in the detection of retinal diseases. Advances in biomedical engineering have led to innovations such as portable models, community-based systems, and artificial intelligence-enabled image analysis. The SightSync OCT is a community-based, technician-free device designed to enhance accessibility while ensuring secure data transfer and high-quality imaging (6 × 6 mm resolution, 80,000 A-scans/s). With its compact design and potential for remote interpretation, SightSync widens the possibility for community-based screening for vision-threatening retinal diseases. By integrating innovations in OCT imaging, the future of monitoring for retinal disease can be transformed to reduce barriers to care and improve patient outcomes. This article discusses the evolution of OCT technology, its role in the diagnosis and management of retinal diseases, and how novel engineering solutions like SightSync OCT are transforming accessibility in retinal imaging. Full article

(This article belongs to the Special Issue Recent Advances in Optical Imaging and Machine Learning in Biomedicine)

► Show Figures

Figure 1

40 pages, 3495 KiB

Open AccessReview

Optical Image Sensors for Smart Analytical Chemiluminescence Biosensors

by Reza Abbasi, Xinyue Hu, Alain Zhang, Isabelle Dummer and Sebastian Wachsmann-Hogiu

Bioengineering 2024, 11(9), 912; https://doi.org/10.3390/bioengineering11090912 - 12 Sep 2024

Viewed by 2541

Abstract

Optical biosensors have emerged as a powerful tool in analytical biochemistry, offering high sensitivity and specificity in the detection of various biomolecules. This article explores the advancements in the integration of optical biosensors with microfluidic technologies, creating lab-on-a-chip (LOC) platforms that enable rapid, efficient, and miniaturized analysis at the point of need. These LOC platforms leverage optical phenomena such as chemiluminescence and electrochemiluminescence to achieve real-time detection and quantification of analytes, making them ideal for applications in medical diagnostics, environmental monitoring, and food safety. Various optical detectors used for detecting chemiluminescence are reviewed, including single-point detectors such as photomultiplier tubes (PMT) and avalanche photodiodes (APD), and pixelated detectors such as charge-coupled devices (CCD) and complementary metal–oxide–semiconductor (CMOS) sensors. A significant advancement discussed in this review is the integration of optical biosensors with pixelated image sensors, particularly CMOS image sensors. These sensors provide numerous advantages over traditional single-point detectors, including high-resolution imaging, spatially resolved measurements, and the ability to simultaneously detect multiple analytes. Their compact size, low power consumption, and cost-effectiveness further enhance their suitability for portable and point-of-care diagnostic devices. In the future, the integration of machine learning algorithms with these technologies promises to enhance data analysis and interpretation, driving the development of more sophisticated, efficient, and accessible diagnostic tools for diverse applications. Full article

(This article belongs to the Special Issue Recent Advances in Optical Imaging and Machine Learning in Biomedicine)

► Show Figures

Journal Menu

Journal Browser

Recent Advances in Optical Imaging and Machine Learning in Biomedicine

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (9 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI