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Contribution of Smart Biosensors to the Future of the Health...
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Contribution of Smart Biosensors to the Future of the Health Care Services

A special issue of Bioengineering (ISSN 2306-5354).

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 29563

Special Issue Editor

Dr. Pau Redón Lurbe

E-Mail Website
Guest Editor
Smart Sensors Laboratory-NUIG, Galway, Ireland
Interests: biosensors; biomarkers; medical devices; cardiovascular; machine learning; artificial inteligence

Special Issue Information

Dear Colleagues,

Recently, the design and development of sensors with new or improved capabilities has attracted interest and funding from academia and industrial partners. In the healthcare field, some of the key developments have related to the reduction of the noise-to-signal ratio for more accurate readings, the employment of new wearable or implantable biocompatible materials and the downsizing of these materials to ensure minimally invasivity.

The coupling of these new technologies with machine learning or artificial intelligence algorithms have contributed to the enhancement of the sensor’s capabilities, making them “smart”. Despite the challenges to test them in a real clinical context, major contributions have already been reported. Among the reported staistics were improved diagnosis, improved patient safety, and improved patient management, as well as the facilitation of the prescription of personalized treatments. Aware of the rapid proliferation of these technologies, special regulations have been developed by the FDA and CE authorities.

We are designing a Special Issue that aims to give the reader an updated understanding of how “smart” biosensors will improve the future of healthcare services. Papers presenting relevant scientific, clinical, or technological advances in areas like clinical diagnosis, patient safety, patient management or reduction of health care expenditures are welcome.

Dr. Pau Redón Lurbe
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.

Published Papers (6 papers)

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Research

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11 pages, 1141 KiB  
Article
Development of a New Detection Algorithm to Identify Acute Coronary Syndrome Using Electrochemical Biosensors for Real-World Long-Term Monitoring
by Pau Redon, Atif Shahzad, Talha Iqbal and William Wijns
Bioengineering 2021, 8(2), 28; https://doi.org/10.3390/bioengineering8020028 - 20 Feb 2021
Cited by 7 | Viewed by 2963
Abstract
Electrochemically based technologies are rapidly moving from the laboratory to bedside applications and wearable devices, like in the field of cardiovascular disease. Major efforts have focused on the biosensor component in contrast with those employed in creating more suitable detection algorithms for long-term [...] Read more.
Electrochemically based technologies are rapidly moving from the laboratory to bedside applications and wearable devices, like in the field of cardiovascular disease. Major efforts have focused on the biosensor component in contrast with those employed in creating more suitable detection algorithms for long-term real-world monitoring solutions. The calibration curve procedure presents major limitations in this context. The objective is to propose a new algorithm, compliant with current clinical guidelines, which can overcome these limitations and contribute to the development of trustworthy wearable or telemonitoring solutions for home-based care. A total of 123 samples of phosphate buffer solution were spiked with different concentrations of troponin, the gold standard method for the diagnosis of the acute coronary syndrome. These were classified as normal or abnormal according to established clinical cut-off values. Off-the-shelf screen-printed electrochemical sensors and cyclic voltammetry measurements (sweep between −1 and 1 V in a 5 mV step) was performed to characterize the changes on the surface of the biosensor and to measure the concentration of troponin in each sample. A logistic regression model was developed to accurately classify these samples as normal or abnormal. The model presents high predictive performance according to specificity (94%), sensitivity (92%), precision (92%), recall (92%), negative predictive value (94%) and F-score (92%). The area under the curve of the precision-recall curve is 97% and the positive and negative likelihood ratios are 16.38 and 0.082, respectively. Moreover, high discriminative power is observed from the discriminate odd ratio (201) and the Youden index (0.866) values. The promising performance of the proposed algorithm suggests its capability to overcome the limitations of the calibration curve procedure and therefore its suitability for the development of trustworthy home-based care solutions. Full article
(This article belongs to the Special Issue Contribution of Smart Biosensors to the Future of the Health Care Services)
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16 pages, 5493 KiB  
Article
Theoretical Simulation of the Near-Field Probe for Non-Invasive Measurements on Planar Layers with Biological Characteristics
by Aleksandr Gorst, Kseniya Zavyalova, Vladimir Yakubov, Aleksandr Mironchev and Andrey Zapasnoy
Bioengineering 2020, 7(4), 149; https://doi.org/10.3390/bioengineering7040149 - 19 Nov 2020
Cited by 7 | Viewed by 2563
Abstract
The article presents the design of the near-field probe, which is a combined emitter (a combination of a symmetric dipole and an annular frame). The design of the probe allows forming a prolonged zone of the near-field. This effect can be used for [...] Read more.
The article presents the design of the near-field probe, which is a combined emitter (a combination of a symmetric dipole and an annular frame). The design of the probe allows forming a prolonged zone of the near-field. This effect can be used for in-depth penetration of the field in media with high absorption, without loss of information. Particular attention in this article is given to a detailed study of the interaction of the field created by this probe on plane-layered biological media. A theoretical analysis of the interaction of the electromagnetic field was carried out in a wide frequency band with a model plane-layer biological medium containing blood vessels of shallow depth using the proposed probe design. Conclusions are drawn about the depth of penetration of a useful signal into different media-analogs of biological tissue. This study is necessary to consider the possibility of using this probe for non-invasive measurements of blood glucose concentration. The studies were carried out using numerical simulation in the CST (Computer Simulation Technology) Microwave Studio environment. All biological tissues were simulated over a wide frequency range from 10 MHz to 10 GHz. Full article
(This article belongs to the Special Issue Contribution of Smart Biosensors to the Future of the Health Care Services)
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13 pages, 1570 KiB  
Article
Development of a Minimally Invasive Screening Tool to Identify Obese Pediatric Population at Risk of Obstructive Sleep Apnea/Hypopnea Syndrome
by José Miguel Calderón, Julio Álvarez-Pitti, Irene Cuenca, Francisco Ponce and Pau Redon
Bioengineering 2020, 7(4), 131; https://doi.org/10.3390/bioengineering7040131 - 19 Oct 2020
Cited by 10 | Viewed by 2752
Abstract
Obstructive sleep apnea syndrome is a reduction of the airflow during sleep which not only produces a reduction in sleep quality but also has major health consequences. The prevalence in the obese pediatric population can surpass 50%, and polysomnography is the current gold [...] Read more.
Obstructive sleep apnea syndrome is a reduction of the airflow during sleep which not only produces a reduction in sleep quality but also has major health consequences. The prevalence in the obese pediatric population can surpass 50%, and polysomnography is the current gold standard method for its diagnosis. Unfortunately, it is expensive, disturbing and time-consuming for experienced professionals. The objective is to develop a patient-friendly screening tool for the obese pediatric population to identify those children at higher risk of suffering from this syndrome. Three supervised learning classifier algorithms (i.e., logistic regression, support vector machine and AdaBoost) common in the field of machine learning were trained and tested on two very different datasets where oxygen saturation raw signal was recorded. The first dataset was the Childhood Adenotonsillectomy Trial (CHAT) consisting of 453 individuals, with ages between 5 and 9 years old and one-third of the patients being obese. Cross-validation was performed on the second dataset from an obesity assessment consult at the Pediatric Department of the Hospital General Universitario of Valencia. A total of 27 patients were recruited between 5 and 17 years old; 42% were girls and 63% were obese. The performance of each algorithm was evaluated based on key performance indicators (e.g., area under the curve, accuracy, recall, specificity and positive predicted value). The logistic regression algorithm outperformed (accuracy = 0.79, specificity = 0.96, area under the curve = 0.9, recall = 0.62 and positive predictive value = 0.94) the support vector machine and the AdaBoost algorithm when trained with the CHAT datasets. Cross-validation tests, using the Hospital General de Valencia (HG) dataset, confirmed the higher performance of the logistic regression algorithm in comparison with the others. In addition, only a minor loss of performance (accuracy = 0.75, specificity = 0.88, area under the curve = 0.85, recall = 0.62 and positive predictive value = 0.83) was observed despite the differences between the datasets. The proposed minimally invasive screening tool has shown promising performance when it comes to identifying children at risk of suffering obstructive sleep apnea syndrome. Moreover, it is ideal to be implemented in an outpatient consult in primary and secondary care. Full article
(This article belongs to the Special Issue Contribution of Smart Biosensors to the Future of the Health Care Services)
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10 pages, 1083 KiB  
Article
SlicerArduino: A Bridge between Medical Imaging Platform and Microcontroller
by Paolo Zaffino, Alessio Merola, Domenico Leuzzi, Virgilio Sabatino, Carlo Cosentino and Maria Francesca Spadea
Bioengineering 2020, 7(3), 109; https://doi.org/10.3390/bioengineering7030109 - 11 Sep 2020
Cited by 2 | Viewed by 3482
Abstract
Interaction between medical image platform and external environment is a desirable feature in several clinical, research, and educational scenarios. In this work, the integration between 3D Slicer package and Arduino board is introduced, enabling a simple and useful communication between the two software/hardware [...] Read more.
Interaction between medical image platform and external environment is a desirable feature in several clinical, research, and educational scenarios. In this work, the integration between 3D Slicer package and Arduino board is introduced, enabling a simple and useful communication between the two software/hardware platforms. The open source extension, programmed in Python language, manages the connection process and offers a communication layer accessible from any point of the medical image suite infrastructure. Deep integration with 3D Slicer code environment is provided and a basic input–output mechanism accessible via GUI is also made available. To test the proposed extension, two exemplary use cases were implemented: (1) INPUT data to 3D Slicer, to navigate on basis of data detected by a distance sensor connected to the board, and (2) OUTPUT data from 3D Slicer, to control a servomotor on the basis of data computed through image process procedures. Both goals were achieved and quasi-real-time control was obtained without any lag or freeze, thus boosting the integration between 3D Slicer and Arduino. This integration can be easily obtained through the execution of few lines of Python code. In conclusion, SlicerArduino proved to be suitable for fast prototyping, basic input–output interaction, and educational purposes. The extension is not intended for mission-critical clinical tasks. Full article
(This article belongs to the Special Issue Contribution of Smart Biosensors to the Future of the Health Care Services)
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14 pages, 2182 KiB  
Article
Application of Fibre Bragg Grating Sensors in Strain Monitoring and Fracture Recovery of Human Femur Bone
by Ali Najafzadeh, Dinusha Serandi Gunawardena, Zhengyong Liu, Ton Tran, Hwa-Yaw Tam, Jing Fu and Bernard K. Chen
Bioengineering 2020, 7(3), 98; https://doi.org/10.3390/bioengineering7030098 - 19 Aug 2020
Cited by 10 | Viewed by 4179
Abstract
Fibre Bragg Grating (FBG) sensors are gaining popularity in biomedical engineering. However, specific standards for in vivo testing for their use are absolutely limited. In this study, in vitro experimental tests were performed to investigate the behaviors and applications of gratings attached to [...] Read more.
Fibre Bragg Grating (FBG) sensors are gaining popularity in biomedical engineering. However, specific standards for in vivo testing for their use are absolutely limited. In this study, in vitro experimental tests were performed to investigate the behaviors and applications of gratings attached to intact and fractured thighbone for a range of compression loading (<300 N) based around some usual daily activities. The wavelength shifts and the corresponding strain sensitivities of the FBG sensors were measured to determine their effectiveness in monitoring the femoral fracture healing process. Four different arrangements of FBG sensors were selected to measure strains at different critical locations on the femoral sawbones surface. Data obtained for intact and plated sawbones were compared using both embedded longitudinal and coiled FBG arrays. Strains were measured close to the fracture, posterior linea aspera and popliteal surface areas, as well as at the proximal and distal ends of the synthetic femur; their responses are discussed herein. The gratings on the longitudinally secured FBG arrays were found to provide high levels of sensitivity and precise measurements, even for relatively small loads (<100 N). Nevertheless, embedding angled FBG sensors is essential to measure the strain generated by applied torque on the femur bone. The maximum recorded strain of the plated femur was 503.97 µε for longitudinal and −274.97 µε for coiled FBG arrays, respectively. These project results are important to configure effective arrangements and orientations of FBG sensors with respect to fracture position and fixation implant for future in vivo experiments. Full article
(This article belongs to the Special Issue Contribution of Smart Biosensors to the Future of the Health Care Services)
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Review

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14 pages, 1061 KiB  
Review
Spinal Deformities and Advancement in Corrective Orthoses
by Athar Ali, Vigilio Fontanari, Marco Fontana and Werner Schmölz
Bioengineering 2021, 8(1), 2; https://doi.org/10.3390/bioengineering8010002 - 25 Dec 2020
Cited by 15 | Viewed by 12940
Abstract
Spinal deformity is an abnormality in the spinal curves and can seriously affect the activities of daily life. The conventional way to treat spinal deformities, such as scoliosis, kyphosis, and spondylolisthesis, is to use spinal orthoses (braces). Braces have been used for centuries [...] Read more.
Spinal deformity is an abnormality in the spinal curves and can seriously affect the activities of daily life. The conventional way to treat spinal deformities, such as scoliosis, kyphosis, and spondylolisthesis, is to use spinal orthoses (braces). Braces have been used for centuries to apply corrective forces to the spine to treat spinal deformities or to stabilize the spine during postoperative rehabilitation. Braces have not modernized with advancements in technology, and very few braces are equipped with smart sensory design and active actuation. There is a need to enable the orthotists, ergonomics practitioners, and developers to incorporate new technologies into the passive field of bracing. This article presents a review of the conventional passive braces and highlights the advancements in spinal orthoses in terms of improved sensory designs, active actuation mechanisms, and new construction methods (CAD/CAM, three-dimensional (3D) printing). This review includes 26 spinal orthoses, comprised of passive rigid/soft braces, active dynamics braces, and torso training devices for the rehabilitation of the spine. Full article
(This article belongs to the Special Issue Contribution of Smart Biosensors to the Future of the Health Care Services)
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