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The Interface between Human Physiology and Medical Device Development
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The Interface between Human Physiology and Medical Device Development

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 46011

Special Issue Editor

Dr. Leo Quinlan

E-Mail Website
Guest Editor
Physiology, School of Medicine, National University of Ireland Galway, H91 W5P7 Galway, Ireland
Interests: interface of human physiology and medical device development with particular emphasis on electrophysiology; neuromodulation and human movement
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The evolution in current medical practice has been built on a foundation of a growing improved understanding of the fundamental physiological principles that control the human body function. In parallel, over the last 30 years, the discipline of biomedical engineering has reported on a vast wealth of knowledge of how the human body interacts with foreign materials, and has grown our understanding of the forces and dynamics of these interactions. These two disciplines have merged to great effect in modern medicine, where clinicians can now treat human disease with a level of specificity and sensitivity unimaginable 30 years ago. There has been an explosion in the development of medical device technologies with the arrival of novel physiological sensors (both wearable and implantable), actuators (both mechanical and electrical), improved signal processing tools, and machine learning algorithms. All of this allows, for example, for heart disease in individual patients to be not only managed better, but managed remotely. Essential organ systems like the lungs, because of their large surface area, have become a novel route for medical devices in drug delivery. Using the interface, we can modulate the brain control of the motor function by stimulating the peripheral nerves. This Special Issue of the Journal of Personalized Medicine aims to highlight the current state of the art in medical device design, and application to human health through the sensing or manipulation of human physiology. The advances in the field of medical device design will continue to pave the way towards personalized care for optimal health.  

Dr. Leo Quinlan
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. Journal of Personalized Medicine 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 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.

Keywords

  • Physiological sensing
  • Neuromodulation
  • Personalized medicine
  • Medical device design
  • Human factors
  • Human machine interface
  • Connected health

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Published Papers (7 papers)

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Research

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25 pages, 3210 KiB  
Article
Identifying Barriers and Facilitators to Diet and Physical Activity Behaviour Change in Type 2 Diabetes Using a Design Probe Methodology
by Kevin A. Cradock, Leo R. Quinlan, Francis M. Finucane, Heather L. Gainforth, Kathleen A. Martin Ginis, Ana Correia de Barros, Elizabeth B. N. Sanders and Gearóid ÓLaighin
J. Pers. Med. 2021, 11(2), 72; https://doi.org/10.3390/jpm11020072 - 26 Jan 2021
Cited by 13 | Viewed by 5755
Abstract
Treatment of Type 2 Diabetes (T2D) typically involves pharmacological methods and adjunct behavioural modifications, focused on changing diet and physical activity (PA) behaviours. Changing diet and physical activity behaviours is complex and any behavioural intervention in T2D, to be successful, must use an [...] Read more.
Treatment of Type 2 Diabetes (T2D) typically involves pharmacological methods and adjunct behavioural modifications, focused on changing diet and physical activity (PA) behaviours. Changing diet and physical activity behaviours is complex and any behavioural intervention in T2D, to be successful, must use an appropriate suite of behaviour change techniques (BCTs). In this study, we sought to understand the perceived barriers and facilitators to diet and PA behaviour change in persons with T2D, with a view to creating artefacts to facilitate the required behaviour changes. The Design Probe was chosen as the most appropriate design research instrument to capture the required data, as it enabled participants to reflect and self-document, over an extended period of time, on their daily lived experiences and, following this reflection, to identify their barriers and facilitators to diet and PA behaviour change. Design Probes were sent to 21 participants and 13 were fully completed. A reflective thematic analysis was carried out on the data, which identified themes of food environment, mental health, work schedule, planning, social support, cravings, economic circumstances and energy associated with diet behaviour. Similar themes were identified for PA as well as themes of physical health, weather, motivation and the physical environment. Full article
(This article belongs to the Special Issue The Interface between Human Physiology and Medical Device Development)
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12 pages, 1065 KiB  
Article
Complex Machine-Learning Algorithms and Multivariable Logistic Regression on Par in the Prediction of Insufficient Clinical Response to Methotrexate in Rheumatoid Arthritis
by Helen R. Gosselt, Maxime M. A. Verhoeven, Maja Bulatović-Ćalasan, Paco M. Welsing, Maurits C. F. J. de Rotte, Johanna M. W. Hazes, Floris P. J. G. Lafeber, Mark Hoogendoorn and Robert de Jonge
J. Pers. Med. 2021, 11(1), 44; https://doi.org/10.3390/jpm11010044 - 14 Jan 2021
Cited by 23 | Viewed by 4634
Abstract
The goals of this study were to examine whether machine-learning algorithms outperform multivariable logistic regression in the prediction of insufficient response to methotrexate (MTX); secondly, to examine which features are essential for correct prediction; and finally, to investigate whether the best performing model [...] Read more.
The goals of this study were to examine whether machine-learning algorithms outperform multivariable logistic regression in the prediction of insufficient response to methotrexate (MTX); secondly, to examine which features are essential for correct prediction; and finally, to investigate whether the best performing model specifically identifies insufficient responders to MTX (combination) therapy. The prediction of insufficient response (3-month Disease Activity Score 28-Erythrocyte-sedimentation rate (DAS28-ESR) > 3.2) was assessed using logistic regression, least absolute shrinkage and selection operator (LASSO), random forest, and extreme gradient boosting (XGBoost). The baseline features of 355 rheumatoid arthritis (RA) patients from the “treatment in the Rotterdam Early Arthritis CoHort” (tREACH) and the U-Act-Early trial were combined for analyses. The model performances were compared using area under the curve (AUC) of receiver operating characteristic (ROC) curves, 95% confidence intervals (95% CI), and sensitivity and specificity. Finally, the best performing model following feature selection was tested on 101 RA patients starting tocilizumab (TCZ)-monotherapy. Logistic regression (AUC = 0.77 95% CI: 0.68–0.86) performed as well as LASSO (AUC = 0.76, 95% CI: 0.67–0.85), random forest (AUC = 0.71, 95% CI: 0.61 = 0.81), and XGBoost (AUC = 0.70, 95% CI: 0.61–0.81), yet logistic regression reached the highest sensitivity (81%). The most important features were baseline DAS28 (components). For all algorithms, models with six features performed similarly to those with 16. When applied to the TCZ-monotherapy group, logistic regression’s sensitivity significantly dropped from 83% to 69% (p = 0.03). In the current dataset, logistic regression performed equally well compared to machine-learning algorithms in the prediction of insufficient response to MTX. Models could be reduced to six features, which are more conducive for clinical implementation. Interestingly, the prediction model was specific to MTX (combination) therapy response. Full article
(This article belongs to the Special Issue The Interface between Human Physiology and Medical Device Development)
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10 pages, 2648 KiB  
Article
An Ingestible Electronics for Continuous and Real-Time Intraabdominal Pressure Monitoring
by Chien-Hung Liao, Chi-Tung Cheng, Chih-Chi Chen, Uei-Ming Jow, Chun-Hung Chen, Yen-Liang Lai, Ya-Chuan Chen and Dong-Ru Ho
J. Pers. Med. 2021, 11(1), 12; https://doi.org/10.3390/jpm11010012 - 24 Dec 2020
Cited by 20 | Viewed by 3716
Abstract
Abdominal compartment syndrome can be treated through decompressive surgery if intraabdominal hypertension (IAH) can be detected in time. Treatment delays due to manual, conventional intravesical pressure (IVP) monitoring using a Foley catheter have been reported. In this work, we present an innovative gastrointestinal [...] Read more.
Abdominal compartment syndrome can be treated through decompressive surgery if intraabdominal hypertension (IAH) can be detected in time. Treatment delays due to manual, conventional intravesical pressure (IVP) monitoring using a Foley catheter have been reported. In this work, we present an innovative gastrointestinal intraluminal pressure (GIP) measurement-based method to monitor and improve pressure-guided relief of intraabdominal pressure (IAP). A novel algorithm for detecting IAH in the gastrointestinal tract of a live porcine model is reported. A wireless pressure-sensing capsule (10 × 13 mm) was developed for absolute measurement. The IAP was estimated during artificial pneumoperitoneum. The pressure waveform-based measurements indicated that the wireless pressure sensor could be used to predict IAP. To enhance GIP monitoring for predicting IAH, the proposed continuous ingestible wireless electronics-based pressure waveform measurement device can be used as a complement to existing modalities. The use of the proposed pressure measurement and communication technology can help provide valuable data for digital health platforms. Full article
(This article belongs to the Special Issue The Interface between Human Physiology and Medical Device Development)
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11 pages, 2961 KiB  
Article
Comparison of the Hemodynamic Performance of Two Neuromuscular Electrical Stimulation Devices Applied to the Lower Limb
by Sahar Avazzadeh, Andrea O’Farrell, Kate Flaherty, Sandra O’Connell, Gearóid ÓLaighin and Leo R. Quinlan
J. Pers. Med. 2020, 10(2), 36; https://doi.org/10.3390/jpm10020036 - 7 May 2020
Cited by 4 | Viewed by 4452
Abstract
Currently, 1% of the population of the Western world suffers from venous leg ulcers as a result of chronic venous insufficiency. Current treatment involves the use of moist wound healing, compression bandages, and intermittent pneumatic compression. Neuromuscular electrical stimulation is a novel potential [...] Read more.
Currently, 1% of the population of the Western world suffers from venous leg ulcers as a result of chronic venous insufficiency. Current treatment involves the use of moist wound healing, compression bandages, and intermittent pneumatic compression. Neuromuscular electrical stimulation is a novel potential new therapeutic method for the promotion of increased lower limb hemodynamics. The aim of this study was to measure the hemodynamic changes in the lower limb with the use of two neuromuscular electrical stimulation devices. Twelve healthy volunteers received two neuromuscular stimulation device interventions. The GekoTM and National University of Ireland (NUI) Galway neuromuscular electrical stimulation devices were randomized between dominant and non-dominant legs. Hemodynamic measurements of peak venous velocity (cm/s), the time average mean velocity (TAMEAN) (cm/s), and ejected volume (mL) of blood were recorded. Peak venous velocity was significantly increased by the GekoTM and the NUI Galway device compared to baseline blood flow (p < 0.0001), while only the voluntary contraction produced significant increases in TAMEAN and ejected volume (both p < 0.05). Neuromuscular muscular electrical stimulation can produce adequate increases in lower limb hemodynamics sufficient to prevent venous stasis. Greater use of neuromuscular stimulation devices could be considered in the treatment of conditions related to chronic venous insufficiency but requires further research. Full article
(This article belongs to the Special Issue The Interface between Human Physiology and Medical Device Development)
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Review

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22 pages, 2842 KiB  
Review
How Do Machines Learn? Artificial Intelligence as a New Era in Medicine
by Oliwia Koteluk, Adrian Wartecki, Sylwia Mazurek, Iga Kołodziejczak and Andrzej Mackiewicz
J. Pers. Med. 2021, 11(1), 32; https://doi.org/10.3390/jpm11010032 - 7 Jan 2021
Cited by 64 | Viewed by 12543
Abstract
With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct [...] Read more.
With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool. Full article
(This article belongs to the Special Issue The Interface between Human Physiology and Medical Device Development)
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22 pages, 323 KiB  
Review
Future Trends in Nebulized Therapies for Pulmonary Disease
by Sean D. McCarthy, Héctor E. González and Brendan D. Higgins
J. Pers. Med. 2020, 10(2), 37; https://doi.org/10.3390/jpm10020037 - 10 May 2020
Cited by 40 | Viewed by 8869
Abstract
Aerosol therapy is a key modality for drug delivery to the lungs of respiratory disease patients. Aerosol therapy improves therapeutic effects by directly targeting diseased lung regions for rapid onset of action, requiring smaller doses than oral or intravenous delivery and minimizing systemic [...] Read more.
Aerosol therapy is a key modality for drug delivery to the lungs of respiratory disease patients. Aerosol therapy improves therapeutic effects by directly targeting diseased lung regions for rapid onset of action, requiring smaller doses than oral or intravenous delivery and minimizing systemic side effects. In order to optimize treatment of critically ill patients, the efficacy of aerosol therapy depends on lung morphology, breathing patterns, aerosol droplet characteristics, disease, mechanical ventilation, pharmacokinetics, and the pharmacodynamics of cell-drug interactions. While aerosol characteristics are influenced by drug formulations and device mechanisms, most other factors are reliant on individual patient variables. This has led to increased efforts towards more personalized therapeutic approaches to optimize pulmonary drug delivery and improve selection of effective drug types for individual patients. Vibrating mesh nebulizers (VMN) are the dominant device in clinical trials involving mechanical ventilation and emerging drugs. In this review, we consider the use of VMN during mechanical ventilation in intensive care units. We aim to link VMN fundamentals to applications in mechanically ventilated patients and look to the future use of VMN in emerging personalized therapeutic drugs. Full article
(This article belongs to the Special Issue The Interface between Human Physiology and Medical Device Development)

Other

Jump to: Research, Review

4 pages, 373 KiB  
Commentary
Dysmorphology in the Era of Genomic Diagnosis
by Anna C. E. Hurst and Nathaniel H. Robin
J. Pers. Med. 2020, 10(1), 18; https://doi.org/10.3390/jpm10010018 - 17 Mar 2020
Cited by 7 | Viewed by 4980
Abstract
Genetic and genomic testing technologies have expanded beyond levels of diagnostic capability that were unimaginable even a few years ago. While this has significantly benefited clinicians in their care of patients and families, it has also altered how geneticists evaluate patients. One immediate [...] Read more.
Genetic and genomic testing technologies have expanded beyond levels of diagnostic capability that were unimaginable even a few years ago. While this has significantly benefited clinicians in their care of patients and families, it has also altered how geneticists evaluate patients. One immediate example is the role of the dysmorphologic physical exam in the patient evaluation. While some have suggested that it is no longer necessary, we argue that the dysmorphologic physical exam is still essential, albeit in a different role. Full article
(This article belongs to the Special Issue The Interface between Human Physiology and Medical Device Development)
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