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Bioelectronic Technologies and Artificial Intelligence for Medical Diagnosis and Healthcare

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

Deadline for manuscript submissions: closed (30 January 2021) | Viewed by 48552

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Special Issue Editors

Prof. Dr. Giovanni Dimauro

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Guest Editor

Department of Computer Science, University of Bari, Bari, Italy
Interests: machine learning; deep learning; pattern recognition; image processing; computer vision
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Vitoantonio Bevilacqua

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Co-Guest Editor

Dipartimento di Ingegneria Elettrica e dell’Informazione, Politecnico di Bari, 70126 Bari, Italy
Interests: intelligent decision support systems for medicine; biomedical image processing and understanding; radiomics for precision medicine; bioinformatics; optimized diagnosis; rehabilitation and therapy

Assoc. Prof. Dr. Leandro Pecchia

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Co-Guest Editor

School of Engineering, University of Warwick, Coventry, UK
Interests: biomedical engineering; health technology assessment; machine learning; clinical engineering; regulatory science

Special Issue Information

Dear Colleagues,

The application of electronic findings to biology and medicine has significantly impacted health and wellbeing. Recent technology advances have allowed the development of new systems that can provide diagnostic information on portable point-of-devices or smartphones.

The size decrease of electronics technologies down to the atomic scale and the advances in system, cell, and molecular biology have the potential to increase the quality and reduce the costs of healthcare.

Clinicians will have access to data from complex sensors, imaging tools, and a multitude of other sources, including personal health e-records. The volume of available data far exceeds the ability of humans to process and use without advanced tools. Artificial intelligence can help draw meaning from this huge amount of data to make better choices for patients.

This Special Issue aims to solicit original research papers as well as review articles focusing on recent advances. We are inviting original research work covering novel theories, innovative methods, and meaningful applications that can potentially lead to significant advances.

Assoc. Prof. Dr. Giovanni Dimauro
Assoc. Prof. Dr. Vitoantonio Bevilacqua
Assoc. Prof. Dr. Leandro Pecchia
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

Chronic and acute pathologies;
Cognitive and neurodegenerative disorders;
Noninvasive physical sensing, e.g., vital functions;
Medical imaging, including cellular;
Machine learning and deep learning in biomedical sciences;
Clinical decision support and reliability of decisions;
Medical datasets and health technologies assessment;
Health monitoring and compliance, also personalized;
Rehabilitation, including home healthcare and independent living;
Electronic health record systems, including personal;
Data collection technologies and devices;
Diagnostic speed, accuracy and reliability; and
Regulatory science.

Published Papers (11 papers)

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Editorial

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9 pages, 201 KiB

Open AccessEditorial

Bioelectronic Technologies and Artificial Intelligence for Medical Diagnosis and Healthcare

by Giovanni Dimauro, Vitoantonio Bevilacqua and Leandro Pecchia

Electronics 2021, 10(11), 1242; https://doi.org/10.3390/electronics10111242 - 24 May 2021

Cited by 4 | Viewed by 2317

Abstract

The application of electronic findings to biology and medicine has significantly impacted health and wellbeing [...] Full article

(This article belongs to the Special Issue Bioelectronic Technologies and Artificial Intelligence for Medical Diagnosis and Healthcare)

Research

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20 pages, 4541 KiB

Open AccessEditor’s ChoiceArticle

Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

by Imayanmosha Wahlang, Arnab Kumar Maji, Goutam Saha, Prasun Chakrabarti, Michal Jasinski, Zbigniew Leonowicz and Elzbieta Jasinska

Electronics 2021, 10(4), 495; https://doi.org/10.3390/electronics10040495 - 20 Feb 2021

Cited by 21 | Viewed by 4121

Abstract

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images. Full article

(This article belongs to the Special Issue Bioelectronic Technologies and Artificial Intelligence for Medical Diagnosis and Healthcare)

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21 pages, 12694 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

A Deep Learning Instance Segmentation Approach for Global Glomerulosclerosis Assessment in Donor Kidney Biopsies

by Nicola Altini, Giacomo Donato Cascarano, Antonio Brunetti, Irio De Feudis, Domenico Buongiorno, Michele Rossini, Francesco Pesce, Loreto Gesualdo and Vitoantonio Bevilacqua

Electronics 2020, 9(11), 1768; https://doi.org/10.3390/electronics9111768 - 25 Oct 2020

Cited by 27 | Viewed by 3270

Abstract

The histological assessment of glomeruli is fundamental for determining if a kidney is suitable for transplantation. The Karpinski score is essential to evaluate the need for a single or dual kidney transplant and includes the ratio between the number of sclerotic glomeruli and the overall number of glomeruli in a kidney section. The manual evaluation of kidney biopsies performed by pathologists is time-consuming and error-prone, so an automatic framework to delineate all the glomeruli present in a kidney section can be very useful. Our experiments have been conducted on a dataset provided by the Department of Emergency and Organ Transplantations (DETO) of Bari University Hospital. This dataset is composed of 26 kidney biopsies coming from 19 donors. The rise of Convolutional Neural Networks (CNNs) has led to a realm of methods which are widely applied in Medical Imaging. Deep learning techniques are also very promising for the segmentation of glomeruli, with a variety of existing approaches. Many methods only focus on semantic segmentation—which consists in segmentation of individual pixels—or ignore the problem of discriminating between non-sclerotic and sclerotic glomeruli, so these approaches are not optimal or inadequate for transplantation assessment. In this work, we employed an end-to-end fully automatic approach based on Mask R-CNN for instance segmentation and classification of glomeruli. We also compared the results obtained with a baseline based on Faster R-CNN, which only allows detection at bounding boxes level. With respect to the existing literature, we improved the Mask R-CNN approach in sliding window contexts, by employing a variant of the Non-Maximum Suppression (NMS) algorithm, which we called Non-Maximum-Area Suppression (NMAS). The obtained results are very promising, leading to improvements over existing literature. The baseline Faster R-CNN-based approach obtained an F-Measure of 0.904 and 0.667 for non-sclerotic and sclerotic glomeruli, respectively. The Mask R-CNN approach has a significant improvement over the baseline, obtaining an F-Measure of 0.925 and 0.777 for non-sclerotic and sclerotic glomeruli, respectively. The proposed method is very promising for the instance segmentation and classification of glomeruli, and allows to make a robust evaluation of global glomerulosclerosis. We also compared Karpinski score obtained with our algorithm to that obtained with pathologists’ annotations to show the soundness of the proposed workflow from a clinical point of view. Full article

(This article belongs to the Special Issue Bioelectronic Technologies and Artificial Intelligence for Medical Diagnosis and Healthcare)

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13 pages, 2906 KiB

Open AccessArticle

Semantic Segmentation of Conjunctiva Region for Non-Invasive Anemia Detection Applications

by Sivachandar Kasiviswanathan, Thulasi Bai Vijayan, Lorenzo Simone and Giovanni Dimauro

Electronics 2020, 9(8), 1309; https://doi.org/10.3390/electronics9081309 - 14 Aug 2020

Cited by 26 | Viewed by 5041

Abstract

Technology is changing the future of healthcare, technology-supported non-invasive medical procedures are more preferable in the medical diagnosis. Anemia is one of the widespread diseases affecting the wellbeing of individuals around the world especially childbearing age women and children and addressing this issue with the advanced technology will reduce the prevalence in large numbers. The objective of this work is to perform segmentation of the conjunctiva region for non-invasive anemia detection applications using deep learning. The proposed U-Net Based Conjunctiva Segmentation Model (UNBCSM) uses fine-tuned U-Net architecture for effective semantic segmentation of conjunctiva from the digital eye images captured by consumer-grade cameras in an uncontrolled environment. The ground truth for this supervised learning was given as Pascal masks obtained by manual selection of conjunctiva pixels. Image augmentation and pre-processing was performed to increase the data size and the performance of the model. UNBCSM showed good segmentation results and exhibited a comparable value of Intersection over Union (IoU) score between the ground truth and the segmented mask of 96% and 85.7% for training and validation, respectively. Full article

(This article belongs to the Special Issue Bioelectronic Technologies and Artificial Intelligence for Medical Diagnosis and Healthcare)

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15 pages, 3542 KiB

Open AccessArticle

A Novel Approach for the Automatic Estimation of the Ciliated Cell Beating Frequency

by Vito Renò, Mauro Sciancalepore, Giovanni Dimauro, Rosalia Maglietta, Michele Cassano and Matteo Gelardi

Electronics 2020, 9(6), 1002; https://doi.org/10.3390/electronics9061002 - 15 Jun 2020

Cited by 4 | Viewed by 2964

Abstract

The qualitative and quantitative evaluation of nasal epithelial cells is interesting in chronic infectious and inflammatory pathologies of the nose and sinuses. Among the cells of the population of the nasal mucosa, ciliated cells are particularly important. In fact, the observation of these cells is essential to investigate primary ciliary dyskinesia, a rare and severe disease associated with other serious diseases such as respiratory diseases, situs inversus, heart disease, and male infertility. Biopsy or brushing of the ciliary mucosa and assessment of ciliary function through measurements of the Ciliary Beating Frequency (CBF) are usually required to facilitate diagnosis. Therefore, low-cost and easy-to-use technologies devoted to measuring the ciliary beating frequency are desirable. We have considered related works in this field and noticed that up to date an actually usable system is not available to measure and monitor CBF. Moreover, performing this operation manually is practically unfeasible or demanding. For this reason, we designed BeatCilia, a low cost and easy-to-use system, based on image processing techniques, with the aim of automatically measuring CBF. This system performs cell Region of Interest (RoI) detection basing on dense optical flow computation of cell body masking, focusing on the cilia movement and taking advantage of the structural characteristics of the ciliated cell and CBF estimation by applying a fast Fourier transform to extract the frequency with the peak amplitude. The experimental results show that it offers a reliable and fast CBF estimation method and can efficiently run on a consumer-grade smartphone. It can support rhinocytologists during cell observation, significantly reducing their efforts. Full article

(This article belongs to the Special Issue Bioelectronic Technologies and Artificial Intelligence for Medical Diagnosis and Healthcare)

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19 pages, 3752 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Comparative Analysis of Rhino-Cytological Specimens with Image Analysis and Deep Learning Techniques

by Giovanni Dimauro, Vitoantonio Bevilacqua, Pio Raffaele Fina, Domenico Buongiorno, Antonio Brunetti, Sergio Latrofa, Michele Cassano and Matteo Gelardi

Electronics 2020, 9(6), 952; https://doi.org/10.3390/electronics9060952 - 8 Jun 2020

Cited by 7 | Viewed by 2995

Abstract

Cytological study of the nasal mucosa (also known as rhino-cytology) represents an important diagnostic aid that allows highlighting of the presence of some types of rhinitis through the analysis of cellular features visible under a microscope. Nowadays, the automated detection and classification of cells benefit from the capacity of deep learning techniques in processing digital images of the cytological preparation. Even though the results of such automatic systems need to be validated by a specialized rhino-cytologist, this technology represents a valid support that aims at increasing the accuracy of the analysis while reducing the required time and effort. The quality of the rhino-cytological preparation, which is clearly important for the microscope observation phase, is also fundamental for the automatic classification process. In fact, the slide-preparing technique turns out to be a crucial factor among the multiple ones that may modify the morphological and chromatic characteristics of the cells. This paper aims to investigate the possible differences between direct smear (SM) and cytological centrifugation (CYT) slide-preparation techniques, in order to preserve image quality during the observation and cell classification phases in rhino-cytology. Firstly, a comparative study based on image analysis techniques has been put forward. The extraction of densitometric and morphometric features has made it possible to quantify and describe the spatial distribution of the cells in the field images observed under the microscope. Statistical analysis of the distribution of these features has been used to evaluate the degree of similarity between images acquired from SM and CYT slides. The results prove an important difference in the observation process of the cells prepared with the above-mentioned techniques, with reference to cell density and spatial distribution: the analysis of CYT slides has been more difficult than of the SM ones due to the spatial distribution of the cells, which results in a lower cell density than the SM slides. As a marginal part of this study, a performance assessment of the computer-aided diagnosis (CAD) system called Rhino-cyt has also been carried out on both groups of image slide types. Full article

(This article belongs to the Special Issue Bioelectronic Technologies and Artificial Intelligence for Medical Diagnosis and Healthcare)

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18 pages, 2027 KiB

Open AccessFeature PaperArticle

A Novel Approach for Biofilm Detection Based on a Convolutional Neural Network

by Giovanni Dimauro, Francesca Deperte, Rosalia Maglietta, Mario Bove, Fabio La Gioia, Vito Renò, Lorenzo Simone and Matteo Gelardi

Electronics 2020, 9(6), 881; https://doi.org/10.3390/electronics9060881 - 26 May 2020

Cited by 15 | Viewed by 3963

Abstract

Rhinology studies anatomy, physiology and diseases affecting the nasal region: one of the most modern techniques to diagnose these diseases is nasal cytology or rhinocytology, which involves analyzing the cells contained in the nasal mucosa under a microscope and researching of other elements such as bacteria, to suspect a pathology. During the microscopic observation, bacteria can be detected in the form of biofilm, that is, a bacterial colony surrounded by an organic extracellular matrix, with a protective function, made of polysaccharides. In the field of nasal cytology, the presence of biofilm in microscopic samples denotes the presence of an infection. In this paper, we describe the design and testing of interesting diagnostic support, for the automatic detection of biofilm, based on a convolutional neural network (CNN). To demonstrate the reliability of the system, alternative solutions based on isolation forest and deep random forest techniques were also tested. Texture analysis is used, with Haralick feature extraction and dominant color. The CNN-based biofilm detection system shows an accuracy of about 98%, an average accuracy of about 100% on the test set and about 99% on the validation set. The CNN-based system designed in this study is confirmed as the most reliable among the best automatic image recognition technologies, in the specific context of this study. The developed system allows the specialist to obtain a rapid and accurate identification of the biofilm in the slide images. Full article

(This article belongs to the Special Issue Bioelectronic Technologies and Artificial Intelligence for Medical Diagnosis and Healthcare)

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14 pages, 6977 KiB

Open AccessArticle

A Development Study of a New Bi-directional Solenoid Actuator for Active Locomotion Capsule Robots

by Linlin Wu and Kaiyuan Lu

Electronics 2020, 9(5), 736; https://doi.org/10.3390/electronics9050736 - 29 Apr 2020

Cited by 2 | Viewed by 2923

Abstract

A new bi-directional, simple-structured solenoid actuator for active locomotion capsule robots (CRs) is investigated in this paper. This active actuator consists of two permanent magnets (PMs) attached to the two ends of the capsule body and a vibration inner mass formed by a solenoidal coil with an iron core. The proposed CR, designed as a sealed structure without external legs, wheels, or caterpillars, can achieve both forward and backward motions driven by the internal collision force. This new design concept has been successfully confirmed on a capsule prototype. The measured displacements show that its movement can be easily controlled by changing the supplied current amplitude and frequency of the solenoid actuator. To validate the new bi-directional CR prototype, various experimental as well as finite element analysis results are presented in this paper. Full article

(This article belongs to the Special Issue Bioelectronic Technologies and Artificial Intelligence for Medical Diagnosis and Healthcare)

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Review

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36 pages, 1005 KiB

Open AccessReview

Advances of ECG Sensors from Hardware, Software and Format Interoperability Perspectives

by Khaleel Husain, Mohd Soperi Mohd Zahid, Shahab Ul Hassan, Sumayyah Hasbullah and Satria Mandala

Electronics 2021, 10(2), 105; https://doi.org/10.3390/electronics10020105 - 6 Jan 2021

Cited by 28 | Viewed by 8015

Abstract

It is well-known that cardiovascular disease is one of the major causes of death worldwide nowadays. Electrocardiogram (ECG) sensor is one of the tools commonly used by cardiologists to diagnose and detect signs of heart disease with their patients. Since fast, prompt and accurate interpretation and decision is important in saving the life of patients from sudden heart attack or cardiac arrest, many innovations have been made to ECG sensors. However, the use of traditional ECG sensors is still prevalent in the clinical settings of many medical institutions. This article provides a comprehensive survey on ECG sensors from hardware, software and data format interoperability perspectives. The hardware perspective outlines a general hardware architecture of an ECG sensor along with the description of its hardware components. The software perspective describes various techniques (denoising, machine learning, deep learning, and privacy preservation) and other computer paradigms used in the software development and deployment for ECG sensors. Finally, the format interoperability perspective offers a detailed taxonomy of current ECG formats and the relationship among these formats. The intention is to help researchers towards the development of modern ECG sensors that are suitable and approved for adoption in real clinical settings. Full article

(This article belongs to the Special Issue Bioelectronic Technologies and Artificial Intelligence for Medical Diagnosis and Healthcare)

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16 pages, 2226 KiB

Open AccessReview

Improving the Healthcare Effectiveness: The Possible Role of EHR, IoMT and Blockchain

by Francesco Girardi, Gaetano De Gennaro, Lucio Colizzi and Nicola Convertini

Electronics 2020, 9(6), 884; https://doi.org/10.3390/electronics9060884 - 26 May 2020

Cited by 47 | Viewed by 6403

Abstract

New types of patient health records aim to help physicians shift from a medical practice, often based on their personal experience, towards one of evidence based medicine, thus improving the communication among patients and care providers and increasing the availability of personal medical information. These new records, allowing patients and care providers to share medical data and clinical information, and access them whenever they need, can be considered enabling Ambient Assisted Living technologies. Furthermore, new personal disease monitoring tools support specialists in their tasks, as an example allowing acquisition, transmission and analysis of medical images. The growing interest around these new technologies poses serious questions regarding data integrity and transaction security. The huge amount of sensitive data stored in these new records surely attracts the interest of malicious hackers, therefore it is necessary to guarantee the integrity and the maximum security of servers and transactions. Blockchain technology can be an important turning point in the development of personal health records. This paper discusses some issues regarding the management and protection of health data exchanged through new medical or diagnostic devices. Full article

(This article belongs to the Special Issue Bioelectronic Technologies and Artificial Intelligence for Medical Diagnosis and Healthcare)

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16 pages, 1757 KiB

Open AccessReview

Estimate of Anemia with New Non-Invasive Systems—A Moment of Reflection

by Giovanni Dimauro, Serena De Ruvo, Federica Di Terlizzi, Angelo Ruggieri, Vincenzo Volpe, Lucio Colizzi and Francesco Girardi

Electronics 2020, 9(5), 780; https://doi.org/10.3390/electronics9050780 - 9 May 2020

Cited by 18 | Viewed by 5182

Abstract

Anemia is a global public health problem with major consequences for human health. About a quarter of the world population shows a hemoglobin concentration that is below the recommended thresholds. Non-invasive methods for monitoring and identifying potential risk of anemia and smartphone-based devices to perform this task are promising in addressing this pathology. We have considered some well-known studies carried out on this topic since the main purpose of this work was not to produce a review. The first group of papers describes the approaches for the clinical evaluation of anemia focused on different human exposed tissues, while we used a second group to overview some technologies, basic methods, and principles of operation of some devices and highlight some technical problems. Results extracted from the second group of papers examined were aggregated in two comparison tables. A growing interest in this topic is demonstrated by the increasing number of papers published recently. We believe we have identified several critical issues in the published studies, including those published by us. Just as an example, in many papers the dataset used is not described. With this paper we wish to open a discussion on these issues. Few papers have been sufficient to highlight differences in the experimental conditions and this makes the comparison of the results difficult. Differences are also found in the identification of the regions of interest in the tissue, descriptions of the datasets, and other boundary conditions. These critical issues are discussed together with open problems and common mistakes that probably we are making. We propose the definition of a road-map and a common agenda for research on this topic. In this sense, we want to highlight here some issues that seem worthy of common discussion and the subject of synergistic agreements. This paper, and in particular, the discussion could be the starting point for an open debate about the dissemination of our experiments and pave the way for further updates and improvements of what we have outlined. Full article

(This article belongs to the Special Issue Bioelectronic Technologies and Artificial Intelligence for Medical Diagnosis and Healthcare)

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