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Novel Implantable Sensors and Biomedical Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 20 October 2024 | Viewed by 7815

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Dr. Laura Cercenelli

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Guest Editor

eDIMES Lab—Laboratory of Bioengineering, Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, 40138 Bologna, Italy
Interests: implantable sensors for monitoring cardio-pulmonary mechanics; prosthetic heart valves; 3D modeling; 3D printing for medical applications; virtual reality; augmented reality; patient-specific simulation for medical education; eye tracking systems

Special Issue Information

Dear Colleagues,

With current technological advancements, the adoption of smart implants and non-invasive solutions is regaining momentum in managing chronic conditions, such as cardiovascular diseases, cancer, cognitive impairment, and managing and remotely monitoring infectious diseases, such as the novel coronavirus disease 2019 (COVID-19).

Various biomedical sensors have been successfully implemented to measure some important physiological parameters, such as heart rate and blood pressure, using both implantable sensor systems and noninvasive devices. Further, connecting these devices to a portable/wireless power supply and data transmission unit has the potential to support remote patient-tailored treatment and diagnostics of many diseases.

The development of implantable and noninvasive sensor systems which can be deployed at point of care, used at home, or integrated into wearable devices is a key factor to enable effective disease prevention, real-time health data collection and monitoring of chronic diseases, and early detection of diseases even before symptoms occur. This could reduce costs and time of hospitalization while improving the quality of life and mobility of patients.

The purpose of this Special Issue is to bring together state-of-the-art applications of both implantable and noninvasive sensors in biomedical field, with particular focus on cardiovascular applications.

We encourage authors to submit original research articles, reviews, case studies on the following topics, but not limited to:

New sensor materials and technologies for cardiovascular applications;
Implantable sensors for biomedical applications;
Noninvasive monitoring systems;
Sensors and systems for remote patient monitoring;
Multi-sensory-based devices;
Sensor integration with artificial intelligence approach.

Dr. Laura Cercenelli
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. Sensors 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 2600 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 (5 papers)

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Research

12 pages, 6133 KiB

Open AccessArticle

Passive Biotelemetric Detection of Tibial Debonding in Wireless Battery-Free Smart Knee Implants

by Thomas A. G. Hall, Frederic Cegla and Richard J. van Arkel

Sensors 2024, 24(5), 1696; https://doi.org/10.3390/s24051696 - 06 Mar 2024

Viewed by 587

Abstract

Aseptic loosening is the dominant failure mechanism in contemporary knee replacement surgery, but diagnostic techniques are poorly sensitive to the early stages of loosening and poorly specific in delineating aseptic cases from infections. Smart implants have been proposed as a solution, but incorporating components for sensing, powering, processing, and communication increases device cost, size, and risk; hence, minimising onboard instrumentation is desirable. In this study, two wireless, battery-free smart implants were developed that used passive biotelemetry to measure fixation at the implant–cement interface of the tibial components. The sensing system comprised of a piezoelectric transducer and coil, with the transducer affixed to the superior surface of the tibial trays of both partial (PKR) and total knee replacement (TKR) systems. Fixation was measured via pulse-echo responses elicited via a three-coil inductive link. The instrumented systems could detect loss of fixation when the implants were partially debonded (+7.1% PKA, +32.6% TKA, both p < 0.001) and fully debonded in situ (+6.3% PKA, +32.5% TKA, both p < 0.001). Measurements were robust to variations in positioning of the external reader, soft tissue, and the femoral component. With low cost and small form factor, the smart implant concept could be adopted for clinical use, particularly for generating an understanding of uncertain aseptic loosening mechanisms. Full article

(This article belongs to the Special Issue Novel Implantable Sensors and Biomedical Applications)

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15 pages, 8298 KiB

Open AccessArticle

First Ex Vivo Animal Study of a Biological Heart Valve Prosthesis Sensorized with Intravalvular Impedance

by Laura Cercenelli, Camilla Gironi, Barbara Bortolani and Emanuela Marcelli

Sensors 2023, 23(8), 3829; https://doi.org/10.3390/s23083829 - 08 Apr 2023

Viewed by 1388

Abstract

IntraValvular Impedance (IVI) sensing is an innovative concept for monitoring heart valve prostheses after implant. We recently demonstrated IVI sensing feasible in vitro for biological heart valves (BHVs). In this study, for the first time, we investigate ex vivo the IVI sensing applied to a BHV when it is surrounded by biological tissue, similar to a real implant condition. A commercial model of BHV was sensorized with three miniaturized electrodes embedded in the commissures of the valve leaflets and connected to an external impedance measurement unit. To perform ex vivo animal tests, the sensorized BHV was implanted in the aortic position of an explanted porcine heart, which was connected to a cardiac BioSimulator platform. The IVI signal was recorded in different dynamic cardiac conditions reproduced with the BioSimulator, varying the cardiac cycle rate and the stroke volume. For each condition, the maximum percent variation in the IVI signal was evaluated and compared. The IVI signal was also processed to calculate its first derivative (dIVI/dt), which should reflect the rate of the valve leaflets opening/closing. The results demonstrated that the IVI signal is well detectable when the sensorized BHV is surrounded by biological tissue, maintaining the similar increasing/decreasing trend that was found during in vitro experiments. The signal can also be informative on the rate of valve opening/closing, as indicated by the changes in dIVI/dt in different dynamic cardiac conditions. Full article

(This article belongs to the Special Issue Novel Implantable Sensors and Biomedical Applications)

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19 pages, 2366 KiB

Open AccessArticle

Constrained IoT-Based Machine Learning for Accurate Glycemia Forecasting in Type 1 Diabetes Patients

by Ignacio Rodríguez-Rodríguez, María Campo-Valera, José-Víctor Rodríguez and Alberto Frisa-Rubio

Sensors 2023, 23(7), 3665; https://doi.org/10.3390/s23073665 - 31 Mar 2023

Cited by 2 | Viewed by 1819

Abstract

Individuals with diabetes mellitus type 1 (DM1) tend to check their blood sugar levels multiple times daily and utilize this information to predict their future glycemic levels. Based on these predictions, patients decide on the best approach to regulate their glucose levels with considerations such as insulin dosage and other related factors. Nevertheless, modern developments in Internet of Things (IoT) technology and innovative biomedical sensors have enabled the constant gathering of glucose level data using continuous glucose monitoring (CGM) in addition to other biomedical signals. With the use of machine learning (ML) algorithms, glycemic level patterns can be modeled, enabling accurate forecasting of this variable. Constrained devices have limited computational power, making it challenging to run complex machine learning algorithms directly on these devices. However, by leveraging edge computing, using lightweight machine learning algorithms, and performing preprocessing and feature extraction, it is possible to run machine learning algorithms on constrained devices despite these limitations. In this paper we test the burdens of some constrained IoT devices, probing that it is feasible to locally predict glycemia using a smartphone, up to 45 min in advance and with acceptable accuracy using random forest. Full article

(This article belongs to the Special Issue Novel Implantable Sensors and Biomedical Applications)

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16 pages, 4266 KiB

Open AccessArticle

An Ex Vivo Study of Wireless Linkage Distance between Implantable LC Resonance Sensor and External Readout Coil

by Muhammad Farooq, Bilal Amin, Marcin J. Kraśny, Adnan Elahi, Muhammad Riaz ur Rehman, William Wijns and Atif Shahzad

Sensors 2022, 22(21), 8402; https://doi.org/10.3390/s22218402 - 01 Nov 2022

Cited by 2 | Viewed by 1851

Abstract

The wireless monitoring of key physiological parameters such as heart rate, respiratory rate, temperature, and pressure can aid in preventive healthcare, early diagnosis, and patient-tailored treatment. In wireless implantable sensors, the distance between the sensor and the reader device is prone to be influenced by the operating frequency, as well as by the medium between the sensor and the reader. This manuscript presents an ex vivo investigation of the wireless linkage between an implantable sensor and an external reader for medical applications. The sensor was designed and fabricated using a cost-effective and accessible fabrication process. The sensor is composed of a circular planar inductor (L) and a circular planar capacitor (C) to form an inductor–capacitor (LC) resonance tank circuit. The reader system comprises a readout coil and data acquisition instrumentation. To investigate the effect of biological medium on wireless linkage, the readout distance between the sensor and the readout coil was examined independently for porcine and ovine tissues. In the bench model, to mimic the bio-environment for the investigation, skin, muscle, and fat tissues were used. The relative magnitude of the reflection coefficient (

S_{11}

) at the readout coil was used as a metric to benchmark wireless linkage. A readable linkage signal was observed on the readout coil when the sensor was held up to 2.5 cm under layers of skin, muscle, and fat tissue. To increase the remote readout distance of the LC sensor, the effect of the repeater coil was also investigated. The experimental results showed that the magnitude of the reflection coefficient signal was increased 3–3.5 times in the presence of the repeater coil, thereby increasing the signal-to-noise ratio of the detected signal. Therefore, the repeater coil between the sensor and the readout coil allows a larger sensing range for a variety of applications in implanted or sealed fields. Full article

(This article belongs to the Special Issue Novel Implantable Sensors and Biomedical Applications)

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17 pages, 7097 KiB

Open AccessArticle

Innovative IntraValvular Impedance Sensing Applied to Biological Heart Valve Prostheses: Design and In Vitro Evaluation

by Camilla Gironi, Laura Cercenelli, Barbara Bortolani, Nicolas Emiliani, Lorenzo Tartarini and Emanuela Marcelli

Sensors 2022, 22(21), 8297; https://doi.org/10.3390/s22218297 - 29 Oct 2022

Cited by 2 | Viewed by 1410

Abstract

Subclinical valve thrombosis in heart valve prostheses is characterized by the progressive reduction in leaflet motion detectable with advanced imaging diagnostics. However, without routine imaging surveillance, this subclinical thrombosis may be underdiagnosed. We recently proposed the novel concept of a sensorized heart valve prosthesis based on electrical impedance measurement (IntraValvular Impedance, IVI) using miniaturized electrodes embedded in the valve structure to generate a local electric field that is altered by the cyclic movement of the leaflets. In this study, we investigated the feasibility of the novel IVI-sensing concept applied to biological heart valves (BHVs). Three proof-of-concept prototypes of sensorized BHVs were assembled with different size, geometry and positioning of the electrodes to identify the optimal IVI-measurement configuration. Each prototype was tested in vitro on a hydrodynamic heart valve assessment platform. IVI signal was closely related to the electrodes’ positioning in the valve structure and showed greater sensitivity in the prototype with small electrodes embedded in the valve commissures. The novel concept of IVI sensing is feasible on BHVs and has great potential for monitoring the valve condition after implant, allowing for early detection of subclinical valve thrombosis and timely selection of an appropriate anticoagulation therapy. Full article

(This article belongs to the Special Issue Novel Implantable Sensors and Biomedical Applications)

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