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Electronics
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Optical Sensing for Biomedical Applications
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Optical Sensing for Biomedical Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 27883

Special Issue Editors

Prof. Dr. Woo June Choi

E-Mail Website
Guest Editor
School of Electrical and Electronics Engineering, College of ICT Engineering, Chung-Ang University, Seoul 06974, Korea
Interests: optical coherence tomography angiography; phase-contrast microscopy; laser speckle contrast imaging; neuroimaging
Prof. Dr. Jun Ki Kim

E-Mail Website
Guest Editor
Asan Institute for Life Science, Asan Medical Center and University of Ulsan, College of Medicine, Seoul 05505, Korea
Interests: intravital imaging; raman spectroscopy; image guided surgery; POC diagnosis

Special Issue Information

Dear Colleagues,

Optical biosensors utilizing optical techniques have been widely used to measure important anatomical and physiological parameters in biological samples, such as morphology, blood pressure, blood flow, heartrate, oximetry, elasticity, and temperature. Recent advances in optoelectronics and the growing demand for biological information in medical diagnostics have boosted the interest in optical biosensors capable of detecting, tracking, monitoring, and imaging health-related biomarkers in clinical, preclinical, and home environments. Furthermore, large efforts in integrating state-of-art artificial intelligence (AI) advances, such as deep learning with sensing databases, are facilitating decision-making regarding medical interventions by promoting diagnosis efficiency and improving access to care and professional knowledge. In consideration of all these points, optical biosensors for biomedical applications is increasingly becoming a very active research area. 

This Special Issue “Optical Sensing for Biomedical Applications” aims to provide a forum for researchers involved in bioengineering and bioelectronics to report their significant results and developments or to provide an overview of research focused on the recent advances and in optical sensing and/or imaging technologies and methodologies for basic research, preclinical/clinical, and biological applications. Both original research articles, brief notes, and reviews describing the current state of the art or providing an up-to-date overview in biosensors are welcome. 

We look forward to receiving your contributions to this Special Issue.

Prof. Dr. Woo June Choi
Prof. Dr. Jun Ki Kim
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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 sensing/imaging modalities including optical fiber sensors, lab-on-a-chip optical platforms, optical probes, micro- and nanooptical technologies, optical interferometry, optical coherence tomography (OCT), OCT angiography, optical nonlinear microscopy, coherent Raman scattering, confocal/multiphoton microscopy, fluorescence lifetime imaging (FLIM), phase contrast microscopy, diffuse optical tomography, optical spectroscopy, laser speckle contrast imaging, digital holographic microscopy (DHM), photoacoustic imaging, acousto-optical imaging, optical endoscopy
  • Novel methods, algorithms, and principles in optical detection and biological diagnosis
  • Applications of optical biosensors and optical probes in medical diagnostics, healthcare, cell biology, agriculture, environmental monitoring
  • Methods to address issues of existing optical biosensors, concerning the fabrication, miniaturization, sensitivity, selectivity, applicability, accuracy, and biocompatibility
  • Computer-aided detection/diagnosis
  • AIs for biomedical image and signal data analysis

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Published Papers (6 papers)

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Research

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13 pages, 3285 KiB  
Article
Double-Clad Optical Fiber-Based Multi-Contrast Noncontact Photoacoustic and Fluorescence Imaging System
by Jun Geun Shin and Jonghyun Eom
Electronics 2021, 10(23), 3008; https://doi.org/10.3390/electronics10233008 - 2 Dec 2021
Viewed by 1839
Abstract
A noncontact photoacoustic and fluorescence dual-modality imaging system is proposed, which integrates a fiber-based fluorescence imaging system with noncontact photoacoustic imaging using a specially fabricated double-cladding fiber (DCF) coupler and a DCF lens. The performance of the DCF coupler and lens was evaluated, [...] Read more.
A noncontact photoacoustic and fluorescence dual-modality imaging system is proposed, which integrates a fiber-based fluorescence imaging system with noncontact photoacoustic imaging using a specially fabricated double-cladding fiber (DCF) coupler and a DCF lens. The performance of the DCF coupler and lens was evaluated, and the feasibility of this new imaging system was demonstrated using simple tubing phantoms with black ink and fluorophore. Our imaging results demonstrated that the multimodal imaging technique can simultaneously acquire photoacoustic and fluorescence images without coming into contact with the sample. Consequently, the developed method is the first noncontact scheme among multimodal imaging systems that is integrated with a photoacoustic imaging system, which can provide varied and complementary information about the sample. Full article
(This article belongs to the Special Issue Optical Sensing for Biomedical Applications)
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16 pages, 2834 KiB  
Article
User State Classification Based on Functional Brain Connectivity Using a Convolutional Neural Network
by Seung-Min Park, Hong-Gi Yeom and Kwee-Bo Sim
Electronics 2021, 10(10), 1158; https://doi.org/10.3390/electronics10101158 - 13 May 2021
Cited by 2 | Viewed by 2855
Abstract
The brain–computer interface (BCI) is a promising technology where a user controls a robot or computer by thinking with no movement. There are several underlying principles to implement BCI, such as sensorimotor rhythms, P300, steady-state visually evoked potentials, and directional tuning. Generally, different [...] Read more.
The brain–computer interface (BCI) is a promising technology where a user controls a robot or computer by thinking with no movement. There are several underlying principles to implement BCI, such as sensorimotor rhythms, P300, steady-state visually evoked potentials, and directional tuning. Generally, different principles are applied to BCI depending on the application, because strengths and weaknesses vary according to each BCI method. Therefore, BCI should be able to predict a user state to apply suitable principles to the system. This study measured electroencephalography signals in four states (resting, speech imagery, leg-motor imagery, and hand-motor imagery) from 10 healthy subjects. Mutual information from 64 channels was calculated as brain connectivity. We used a convolutional neural network to predict a user state, where brain connectivity was the network input. We applied five-fold cross-validation to evaluate the proposed method. Mean accuracy for user state classification was 88.25 ± 2.34%. This implies that the system can change the BCI principle using brain connectivity. Thus, a BCI user can control various applications according to their intentions. Full article
(This article belongs to the Special Issue Optical Sensing for Biomedical Applications)
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13 pages, 2259 KiB  
Article
Algorithm for Automated Foot Detection in Thermal and Optical Images for Temperature Asymmetry Analysis
by Jonas Guzaitis, Agne Kadusauskiene and Renaldas Raisutis
Electronics 2021, 10(5), 571; https://doi.org/10.3390/electronics10050571 - 28 Feb 2021
Cited by 3 | Viewed by 3544
Abstract
Infrared thermography has been proven to be an effective non-invasive method in diabetic foot ulcer prevention, yet current image processing algorithms are inaccurate and impractical for clinical work. The aim of this study was to investigate the accuracy of our automated algorithm for [...] Read more.
Infrared thermography has been proven to be an effective non-invasive method in diabetic foot ulcer prevention, yet current image processing algorithms are inaccurate and impractical for clinical work. The aim of this study was to investigate the accuracy of our automated algorithm for feet outline detection and localization of potential inflammation regions in thermal images. Optical and thermal images were captured by a Flir OnePro camera connected with an Apple iPad Air tablet. Both thermal and optical images were merged into an edge image and used for the estimation of foot template transformations during the localization process. According to the feet template transformations, temperature maps were calculated and compared with each other to detect a set of regions exceeding the defined temperature threshold. Finally, a set of potential inflammation regions were filtered according to the blobs features to obtain the final list of inflammation regions. In this study, 168 thermal images were analyzed. The developed algorithm yielded 95.83% accuracy for foot outline detection and 94.28% accuracy for detection of the inflammation regions. The presented automated algorithm with enhanced detection accuracy can be used for developing a mobile thermal imaging system. Further studies with patients who have diabetes and are at risk of foot ulceration are needed to test the significance of our developed algorithm. Full article
(This article belongs to the Special Issue Optical Sensing for Biomedical Applications)
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12 pages, 2545 KiB  
Article
Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19
by Sampa Misra, Seungwan Jeon, Seiyon Lee, Ravi Managuli, In-Su Jang and Chulhong Kim
Electronics 2020, 9(9), 1388; https://doi.org/10.3390/electronics9091388 - 27 Aug 2020
Cited by 69 | Viewed by 6126
Abstract
The 2019 novel coronavirus (COVID-19) has spread rapidly all over the world. The standard test for screening COVID-19 patients is the polymerase chain reaction test. As this method is time consuming, as an alternative, chest X-rays may be considered for quick screening. However, [...] Read more.
The 2019 novel coronavirus (COVID-19) has spread rapidly all over the world. The standard test for screening COVID-19 patients is the polymerase chain reaction test. As this method is time consuming, as an alternative, chest X-rays may be considered for quick screening. However, specialization is required to read COVID-19 chest X-ray images as they vary in features. To address this, we present a multi-channel pre-trained ResNet architecture to facilitate the diagnosis of COVID-19 chest X-ray. Three ResNet-based models were retrained to classify X-rays in a one-against-all basis from (a) normal or diseased, (b) pneumonia or non-pneumonia, and (c) COVID-19 or non-COVID19 individuals. Finally, these three models were ensembled and fine-tuned using X-rays from 1579 normal, 4245 pneumonia, and 184 COVID-19 individuals to classify normal, pneumonia, and COVID-19 cases in a one-against-one framework. Our results show that the ensemble model is more accurate than the single model as it extracts more relevant semantic features for each class. The method provides a precision of 94% and a recall of 100%. It could potentially help clinicians in screening patients for COVID-19, thus facilitating immediate triaging and treatment for better outcomes. Full article
(This article belongs to the Special Issue Optical Sensing for Biomedical Applications)
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12 pages, 2010 KiB  
Article
Intercellular Bioimaging and Biodistribution of Gold Nanoparticle-Loaded Macrophages for Targeted Drug Delivery
by Sehwan Kim, Sung Hun Kang, Soo Hwan Byun, Hye-Jin Kim, In-Kyu Park, Henry Hirschberg and Seok Jin Hong
Electronics 2020, 9(7), 1105; https://doi.org/10.3390/electronics9071105 - 7 Jul 2020
Cited by 13 | Viewed by 4328
Abstract
In order to effectively apply nanoparticles to clinical use, macrophages have been used as vehicles to deliver genes, drugs or nanomaterials into tumors. In this study, the effectiveness of macrophage as a drug delivery system was validated by biodistribution imaging modalities at intercellular [...] Read more.
In order to effectively apply nanoparticles to clinical use, macrophages have been used as vehicles to deliver genes, drugs or nanomaterials into tumors. In this study, the effectiveness of macrophage as a drug delivery system was validated by biodistribution imaging modalities at intercellular and ex vivo levels. We focused on biodistribution imaging, namely, the characterization of the gold nanoparticle-loaded macrophages using intracellular holotomography and target delivery efficiency analysis using ex vivo fluorescence imaging techniques. In more detail, gold nanoparticles (AuNPs) were prepared with trisodium citrate method and loaded into macrophage cells (RAW 264.7). First, AuNPs loading into macrophages was confirmed using the conventional ultraviolet-visible (UV-VIS) spectroscopy and inductively coupled plasma-mass spectrometry (ICP-MS). Then, the holotomographic imaging was employed to characterize the intracellular biodistribution of the AuNPs-loaded macrophages. The efficacy of target delivery of the well AuNPs uptake macrophages was studied in a mouse model, established via lipopolysaccharide (LPS)-induced inflammation. The fluorescent images and the ex vivo ICP-MS evaluated the delivery efficiency of the AuNPs-loaded macrophages. Results revealed that the holotomographic imaging techniques can be promising modalities to understand intracellular biodistribution and ex vivo fluorescence imaging can be useful to validate the target delivery efficacy of the AuNPs-loaded macrophages. Full article
(This article belongs to the Special Issue Optical Sensing for Biomedical Applications)
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Review

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29 pages, 6020 KiB  
Review
State-of-the-Art Optical Devices for Biomedical Sensing Applications—A Review
by N. L. Kazanskiy, S. N. Khonina, M. A. Butt, A. Kaźmierczak and R. Piramidowicz
Electronics 2021, 10(8), 973; https://doi.org/10.3390/electronics10080973 - 19 Apr 2021
Cited by 45 | Viewed by 8153
Abstract
Optical sensors for biomedical applications have gained prominence in recent decades due to their compact size, high sensitivity, reliability, portability, and low cost. In this review, we summarized and discussed a few selected techniques and corresponding technological platforms enabling the manufacturing of optical [...] Read more.
Optical sensors for biomedical applications have gained prominence in recent decades due to their compact size, high sensitivity, reliability, portability, and low cost. In this review, we summarized and discussed a few selected techniques and corresponding technological platforms enabling the manufacturing of optical biomedical sensors of different types. We discussed integrated optical biosensors, vertical grating couplers, plasmonic sensors, surface plasmon resonance optical fiber biosensors, and metasurface biosensors, Photonic crystal-based biosensors, thin metal films biosensors, and fiber Bragg grating biosensors as the most representative cases. All of these might enable the identification of symptoms of deadly illnesses in their early stages; thus, potentially saving a patient’s life. The aim of this paper was not to render a definitive judgment in favor of one sensor technology over another. We presented the pros and cons of all the major sensor systems enabling the readers to choose the solution tailored to their needs and demands. Full article
(This article belongs to the Special Issue Optical Sensing for Biomedical Applications)
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