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Digital Healthcare Leveraging Edge Computing and the Internet of Things
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Digital Healthcare Leveraging Edge Computing and the Internet of Things

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 13439

Special Issue Editors

Dr. Antonio Celesti

E-Mail Website
Guest Editor
Department of Mathematics, Computer Science, Physics and Hearth Sciences (MIFT), University of Messina, 98166 Messina, Italy
Interests: distributed systems; cloud computing; edge computing; Internet of Things (IoT); machine learning; assistive technology; eHealth
Special Issues, Collections and Topics in MDPI journals
Dr. Ivanoe De Falco

E-Mail Website
Guest Editor
Institute of High Performance Computing and Networking of National Research Council (ICAR-CNR), 80131 Naples, Italy
Interests: artificial intelligence; machine learning; soft computing; computational intelligence; parallel and distributed computing; explainable artificial intelligence; AI/ML applications to eHealth and mobile health; pattern recognition; signal processing; optimization; classification; regression; time series forecasting
Special Issues, Collections and Topics in MDPI journals
Dr. Giovanna Sannino

E-Mail Website
Guest Editor
Institute of High Performance Computing and Networking – National Research Council of Italy (ICAR-CNR), 80131 Naples, Italy
Interests: eHealth; mobile health; signal processing; pattern recognition; biomechanical and physiological parameter extraction and analysis; statistical analysis; machine learning/artificial intelligence techniques for eHealth applications; ICT-based intelligent solutions for chronic disease (cardiovascular diseases); wearable devices (ECG sensors, accelerometer sensors, etc.)
Special Issues, Collections and Topics in MDPI journals
Dr. Lorenzo Carnevale

E-Mail Website
Guest Editor
Department of Mathematics, Computer Science, Physics and Hearth Sciences (MIFT), University of Messina, 98166 Messina, Italy
Interests: artificial intelligence; cloud computing; edge computing; big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In 2025, the global market for Internet of Things (IoT) medical devices is expected to exceed USD 500 billion. This means that the paradigm of digital healthcare will still change. Most computing will happen at the edge, namely, data are analyzed and acted closer to the point of collection, or on a nearby system situated between the connected device and the Cloud.

Edge computing combined with the IoT offers several advantages, such as greater data transmission speed, less dependence on limited bandwidth, greater privacy and security, and lower costs, because as more sensor-derived data are used locally, fewer data need to be transmitted remotely. This not only decreases cost but increases efficiency and improves the patient’s experience and quality of life, bringing us one step closer to autonomous instead of merely automated care.

The smart use of edge computing can strongly impact the management of chronic diseases and also emergency medical scenarios on ambulances where real time is crucial to saving time and lives. The combination of IoT and fast 5G cellular connections will also enhance the delivery of at-home care and allow for the continuous monitoring of patients for diseases such as diabetes and congestive heart failure.

This Special Issue will address all novelties related to edge, IoT, and 5G applications, sensor and actuator systems consisting of wearable medical smart devices, signal processing techniques, artificial intelligence techniques, and decision support systems for healthcare.

Dr. Antonio Celesti
Dr. Ivanoe De Falco
Dr. Giovanna Sannino
Dr. Lorenzo Carnevale
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. 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.

Keywords

  • Digital healthcare
  • Edge computing
  • IoT
  • 5G
  • Sensors and actuators
  • Signal processing
  • Artificial intelligence
  • AI accelerators
  • Security
  • Decision support systems

Published Papers (5 papers)

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Research

17 pages, 2028 KiB  
Article
A Hybrid Generic Framework for Heart Problem Diagnosis Based on a Machine Learning Paradigm
by Alaa Menshawi, Mohammad Mehedi Hassan, Nasser Allheeib and Giancarlo Fortino
Sensors 2023, 23(3), 1392; https://doi.org/10.3390/s23031392 - 26 Jan 2023
Cited by 7 | Viewed by 2516
Abstract
The early, valid prediction of heart problems would minimize life threats and save lives, while lack of prediction and false diagnosis can be fatal. Addressing a single dataset alone to build a machine learning model for the identification of heart problems is not [...] Read more.
The early, valid prediction of heart problems would minimize life threats and save lives, while lack of prediction and false diagnosis can be fatal. Addressing a single dataset alone to build a machine learning model for the identification of heart problems is not practical because each country and hospital has its own data schema, structure, and quality. On this basis, a generic framework has been built for heart problem diagnosis. This framework is a hybrid framework that employs multiple machine learning and deep learning techniques and votes for the best outcome based on a novel voting technique with the intention to remove bias from the model. The framework contains two consequent layers. The first layer contains simultaneous machine learning models running over a given dataset. The second layer consolidates the outputs of the first layer and classifies them as a second classification layer based on novel voting techniques. Prior to the classification process, the framework selects the top features using a proposed feature selection framework. It starts by filtering the columns using multiple feature selection methods and considers the top common features selected. Results from the proposed framework, with 95.6% accuracy, show its superiority over the single machine learning model, classical stacking technique, and traditional voting technique. The main contribution of this work is to demonstrate how the prediction probabilities of multiple models can be exploited for the purpose of creating another layer for final output; this step neutralizes any model bias. Another experimental contribution is proving the complete pipeline’s ability to be retrained and used for other datasets collected using different measurements and with different distributions. Full article
(This article belongs to the Special Issue Digital Healthcare Leveraging Edge Computing and the Internet of Things)
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20 pages, 11400 KiB  
Article
Mammogram Image Enhancement Techniques for Online Breast Cancer Detection and Diagnosis
by Daniel S. da Silva, Caio S. Nascimento, Senthil K. Jagatheesaperumal and Victor Hugo C. de Albuquerque
Sensors 2022, 22(22), 8818; https://doi.org/10.3390/s22228818 - 15 Nov 2022
Cited by 7 | Viewed by 2191
Abstract
Breast cancer is the type of cancer with the highest incidence and global mortality of female cancers. Thus, the adaptation of modern technologies that assist in medical diagnosis in order to accelerate, automate and reduce the subjectivity of this process are of paramount [...] Read more.
Breast cancer is the type of cancer with the highest incidence and global mortality of female cancers. Thus, the adaptation of modern technologies that assist in medical diagnosis in order to accelerate, automate and reduce the subjectivity of this process are of paramount importance for an efficient treatment. Therefore, this work aims to propose a robust platform to compare and evaluate the proposed strategies for improving breast ultrasound images and compare them with state-of-the-art techniques by classifying them as benign, malignant and normal. Investigations were performed on a dataset containing a total of 780 images of tumor-affected persons, divided into benign, malignant and normal. A data augmentation technique was used to scale up the corpus of images available in the chosen dataset. For this, novel image enhancement techniques were used and the Multilayer Perceptrons, k-Nearest Neighbor and Support Vector Machines algorithms were used for classification. From the promising outcomes of the conducted experiments, it was observed that the bilateral algorithm together with the SVM classifier achieved the best result for the classification of breast cancer, with an overall accuracy of 96.69% and an accuracy for the detection of malignant nodules of 95.11%. Therefore, it was found that the application of image enhancement methods can help in the detection of breast cancer at a much earlier stage with better accuracy in detection. Full article
(This article belongs to the Special Issue Digital Healthcare Leveraging Edge Computing and the Internet of Things)
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20 pages, 1432 KiB  
Article
Hypertension Diagnosis with Backpropagation Neural Networks for Sustainability in Public Health
by Jorge Antonio Orozco Torres, Alejandro Medina Santiago, José Manuel Villegas Izaguirre, Monica Amador García and Alberto Delgado Hernández
Sensors 2022, 22(14), 5272; https://doi.org/10.3390/s22145272 - 14 Jul 2022
Cited by 2 | Viewed by 1740
Abstract
This paper presents the development of a multilayer feed-forward neural network for the diagnosis of hypertension, based on a population-based study. For the development of this architecture, several physiological factors have been considered, which are vital to determining the risk of being hypertensive; [...] Read more.
This paper presents the development of a multilayer feed-forward neural network for the diagnosis of hypertension, based on a population-based study. For the development of this architecture, several physiological factors have been considered, which are vital to determining the risk of being hypertensive; a diagnostic system can offer a solution which is not easy to determine by conventional means. The results obtained demonstrate the sustainability of health conditions affecting humanity today as a consequence of the social environment in which we live, e.g., economics, stress, smoking, alcoholism, drug addiction, obesity, diabetes, physical inactivity, etc., which leads to hypertension. The results of the neural network-based diagnostic system show an effectiveness of 90%, thus generating a high expectation in diagnosing the risk of hypertension from the analyzed physiological data. Full article
(This article belongs to the Special Issue Digital Healthcare Leveraging Edge Computing and the Internet of Things)
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24 pages, 1889 KiB  
Article
FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information
by Trang-Thi Ho, Khoa-Dang Tran and Yennun Huang
Sensors 2022, 22(10), 3728; https://doi.org/10.3390/s22103728 - 13 May 2022
Cited by 20 | Viewed by 2357
Abstract
Coronavirus (COVID-19) has created an unprecedented global crisis because of its detrimental effect on the global economy and health. COVID-19 cases have been rapidly increasing, with no sign of stopping. As a result, test kits and accurate detection models are in short supply. [...] Read more.
Coronavirus (COVID-19) has created an unprecedented global crisis because of its detrimental effect on the global economy and health. COVID-19 cases have been rapidly increasing, with no sign of stopping. As a result, test kits and accurate detection models are in short supply. Early identification of COVID-19 patients will help decrease the infection rate. Thus, developing an automatic algorithm that enables the early detection of COVID-19 is essential. Moreover, patient data are sensitive, and they must be protected to prevent malicious attackers from revealing information through model updates and reconstruction. In this study, we presented a higher privacy-preserving federated learning system for COVID-19 detection without sharing data among data owners. First, we constructed a federated learning system using chest X-ray images and symptom information. The purpose is to develop a decentralized model across multiple hospitals without sharing data. We found that adding the spatial pyramid pooling to a 2D convolutional neural network improves the accuracy of chest X-ray images. Second, we explored that the accuracy of federated learning for COVID-19 identification reduces significantly for non-independent and identically distributed (Non-IID) data. We then proposed a strategy to improve the model’s accuracy on Non-IID data by increasing the total number of clients, parallelism (client-fraction), and computation per client. Finally, for our federated learning model, we applied a differential privacy stochastic gradient descent (DP-SGD) to improve the privacy of patient data. We also proposed a strategy to maintain the robustness of federated learning to ensure the security and accuracy of the model. Full article
(This article belongs to the Special Issue Digital Healthcare Leveraging Edge Computing and the Internet of Things)
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17 pages, 3744 KiB  
Article
DICOMization of Proprietary Files Obtained from Confocal, Whole-Slide, and FIB-SEM Microscope Scanners
by Yubraj Gupta, Carlos Costa, Eduardo Pinho and Luís Bastião Silva
Sensors 2022, 22(6), 2322; https://doi.org/10.3390/s22062322 - 17 Mar 2022
Cited by 8 | Viewed by 3253
Abstract
The evolution of biomedical imaging technology is allowing the digitization of hundreds of glass slides at once. There are multiple microscope scanners available in the market including low-cost solutions that can serve small centers. Moreover, new technology is being researched to acquire images [...] Read more.
The evolution of biomedical imaging technology is allowing the digitization of hundreds of glass slides at once. There are multiple microscope scanners available in the market including low-cost solutions that can serve small centers. Moreover, new technology is being researched to acquire images and new modalities are appearing in the market such as electron microscopy. This reality offers new diagnostics tools to clinical practice but emphasizes also the lack of multivendor system’s interoperability. Without the adoption of standard data formats and communications methods, it will be impossible to build this industry through the installation of vendor-neutral archives and the establishment of telepathology services in the cloud. The DICOM protocol is a feasible solution to the aforementioned problem because it already provides an interface for visible light and whole slide microscope imaging modalities. While some scanners currently have DICOM interfaces, the vast majority of manufacturers continue to use proprietary solutions. This article proposes an automated DICOMization pipeline that can efficiently transform distinct proprietary microscope images from CLSM, FIB-SEM, and WSI scanners into standard DICOM with their biological information maintained within their metadata. The system feasibility and performance were evaluated with fifteen distinct proprietary modalities, including stacked WSI samples. The results demonstrated that the proposed methodology is accurate and can be used in production. The normalized objects were stored through the standard communications in the Dicoogle open-source archive. Full article
(This article belongs to the Special Issue Digital Healthcare Leveraging Edge Computing and the Internet of Things)
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