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Applied Sciences
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New Applications of Deep Learning in Health Monitoring Systems
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New Applications of Deep Learning in Health Monitoring Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 15434

Special Issue Editor

Dr. Lei Zhang

E-Mail Website
Guest Editor
Department of Precision Instrument Engineering, Tianjin University, Tianjin, China
Interests: wearable health monitoring sensors; self-powered sensors; flexible energy harvesting and storage devices; human-interactive devices

Special Issue Information

Dear Colleagues,

With a massive influx of multimodality data, data analytics play an increasingly important role in health informatics in the last decade. This also prompts rapidly growing research in the analytical method and data-driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning. Considering the capability of addressing large-scale data and learning multi-scale/multi-level/hierarchical representation, deep learning can be a powerful and effective solution for health monitoring systems. Therefore, this Special Issue aims to present researchers and engineers with a global view of this hot and active topic on novel deep learning-based health monitoring system applications.

Subjects that will be discussed in this Special Issue will focus not only on modern methods and data-driven approaches based on deep learning, but also on the applications and their properties in health monitoring systems.

Dr. Lei Zhang
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. Applied Sciences 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

  • artificial neural networks
  • deep learning
  • artificial intelligence algorithm
  • health monitoring
  • flexible sensors

Published Papers (6 papers)

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Research

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13 pages, 2809 KiB  
Article
Non-Invasive Estimation of Gleason Score by Semantic Segmentation and Regression Tasks Using a Three-Dimensional Convolutional Neural Network
by Takaaki Yoshimura, Keisuke Manabe and Hiroyuki Sugimori
Appl. Sci. 2023, 13(14), 8028; https://doi.org/10.3390/app13148028 - 09 Jul 2023
Cited by 2 | Viewed by 1208
Abstract
The Gleason score (GS) is essential in categorizing prostate cancer risk using biopsy. The aim of this study was to propose a two-class GS classification (< and ≥GS 7) methodology using a three-dimensional convolutional neural network with semantic segmentation to predict GS non-invasively [...] Read more.
The Gleason score (GS) is essential in categorizing prostate cancer risk using biopsy. The aim of this study was to propose a two-class GS classification (< and ≥GS 7) methodology using a three-dimensional convolutional neural network with semantic segmentation to predict GS non-invasively using multiparametric magnetic resonance images (MRIs). Four training datasets of T2-weighted images and apparent diffusion coefficient maps with and without semantic segmentation were used as test images. All images and lesion information were selected from a training cohort of the Society of Photographic Instrumentation Engineers, the American Association of Physicists in Medicine, and the National Cancer Institute (SPIE–AAPM–NCI) PROSTATEx Challenge dataset. Precision, recall, overall accuracy and area under the receiver operating characteristics curve (AUROC) were calculated from this dataset, which comprises publicly available prostate MRIs. Our data revealed that the GS ≥ 7 precision (0.73 ± 0.13) and GS < 7 recall (0.82 ± 0.06) were significantly higher using semantic segmentation (p < 0.05). Moreover, the AUROC in segmentation volume was higher than that in normal volume (ADCmap: 0.70 ± 0.05 and 0.69 ± 0.08, and T2WI: 0.71 ± 0.07 and 0.63 ± 0.08, respectively). However, there were no significant differences in overall accuracy between the segmentation and normal volume. This study generated a diagnostic method for non-invasive GS estimation from MRIs. Full article
(This article belongs to the Special Issue New Applications of Deep Learning in Health Monitoring Systems)
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18 pages, 677 KiB  
Article
Novel Methods of AI-Based Gait Analysis in Post-Stroke Patients
by Izabela Rojek, Piotr Prokopowicz, Janusz Dorożyński and Dariusz Mikołajewski
Appl. Sci. 2023, 13(10), 6258; https://doi.org/10.3390/app13106258 - 20 May 2023
Cited by 1 | Viewed by 1537
Abstract
Research on gait function assessment is important not only in terms of the patient’s mobility, but also in terms of the patient’s current and future quality of life, ability to achieve health goals, family life, study and/or work, and participation in society. The [...] Read more.
Research on gait function assessment is important not only in terms of the patient’s mobility, but also in terms of the patient’s current and future quality of life, ability to achieve health goals, family life, study and/or work, and participation in society. The main methods used herein include a literature review and an analysis of our own original research and concepts. This study used the historical data of 92 ischemic stroke patients (convenience trial) undergoing two kinds of rehabilitation. An artificial neural network, fractal analysis, and fuzzy analysis were used to analyze the results. Our findings suggest that artificial neural networks, fuzzy logic, and multifractal analysis are useful for building simple, low-cost, and efficient computational tools for gait analysis, especially in post-stroke patients. The novelty lies in the simultaneous application of the three aforementioned technologies to develop a computational model for the analysis of a patient’s post-stroke gait. The contribution of this work consists not only in its proposal of a new and useful clinical tool for gait assessment, even in the most severe post-stroke cases, but also in its attempt to offer a comprehensive computational explanation of observed gait phenomena and mechanisms. We conclude by anticipating more advanced and broader future applications of artificial intelligence (AI) in gait analysis, especially in post-stroke patients. Full article
(This article belongs to the Special Issue New Applications of Deep Learning in Health Monitoring Systems)
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11 pages, 16403 KiB  
Article
A Machine-Learning-Algorithm-Assisted Intelligent System for Real-Time Wireless Respiratory Monitoring
by Chi Zhang, Lei Zhang, Yu Tian, Bo Bao and Dachao Li
Appl. Sci. 2023, 13(6), 3885; https://doi.org/10.3390/app13063885 - 18 Mar 2023
Cited by 8 | Viewed by 7189
Abstract
Respiratory signals are basic indicators of human life and health that are used as effective biomarkers to detect respiratory diseases in clinics, including cardiopulmonary function, breathing disorders, and breathing system infections. Therefore, it is necessary to continuously measure respiratory signals. However, there is [...] Read more.
Respiratory signals are basic indicators of human life and health that are used as effective biomarkers to detect respiratory diseases in clinics, including cardiopulmonary function, breathing disorders, and breathing system infections. Therefore, it is necessary to continuously measure respiratory signals. However, there is still a lack of effective portable electronic devices designed to meet the needs of daily respiratory monitoring. This study presents an intelligent, portable, and wireless respiratory monitoring system for real-time evaluation of human respiratory behaviors. The system consists of a triboelectric respiratory sensor; circuit board hardware for data acquisition, preprocessing, and wireless transmission; a machine learning algorithm for enhancing recognition accuracy; and a mobile terminal app. The triboelectric sensor—fabricated by the screen-printing method—is lightweight, non-invasive, and biocompatible. It provides a clear response to the frequency and intensity of respiratory airflow. The portable circuit board is reusable and cost-effective. The decision tree model algorithm is used to identify the respiratory signals with an average accuracy of 97.2%. The real-time signal and statistical results can be uploaded to a server network and displayed on various mobile terminals for body health warnings and advice. This work promotes the development of wearable health monitoring systems. Full article
(This article belongs to the Special Issue New Applications of Deep Learning in Health Monitoring Systems)
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14 pages, 797 KiB  
Article
Characterization of Pupillary Light Response Features for the Classification of Patients with Optic Neuritis
by Stefano Polizzi, Nico Curti, Lorenzo Dall’Olio, Laura Cercenelli, Luigi Fontana, Nicola Valsecchi, Emanuela Marcelli, Gastone Castellani and Piera Versura
Appl. Sci. 2023, 13(3), 1520; https://doi.org/10.3390/app13031520 - 24 Jan 2023
Cited by 1 | Viewed by 1559
Abstract
Pupillometry is a promising technique for the potential diagnosis of several neurological pathologies. However, its potential is not fully explored yet, especially for prediction purposes and results interpretation. In this work, we analyzed 100 pupillometric curves obtained by 12 subjects, applying both advanced [...] Read more.
Pupillometry is a promising technique for the potential diagnosis of several neurological pathologies. However, its potential is not fully explored yet, especially for prediction purposes and results interpretation. In this work, we analyzed 100 pupillometric curves obtained by 12 subjects, applying both advanced signal processing techniques and physics methods to extract typically collected features and newly proposed ones. We used machine learning techniques for the classification of Optic Neuritis (ON) vs. Healthy subjects, controlling for overfitting and ranking the features by random permutation, following their importance in prediction. All the extracted features, except one, turned out to have significant importance for prediction, with an average accuracy of 76%, showing the complexity of the processes involved in the pupillary light response. Furthermore, we provided a possible neurological interpretation of this new set of pupillometry features in relation to ON vs. Healthy classification. Full article
(This article belongs to the Special Issue New Applications of Deep Learning in Health Monitoring Systems)
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11 pages, 1052 KiB  
Article
Prostate Gleason Score Detection by Calibrated Machine Learning Classification through Radiomic Features
by Francesco Mercaldo, Maria Chiara Brunese, Francesco Merolla, Aldo Rocca, Marcello Zappia and Antonella Santone
Appl. Sci. 2022, 12(23), 11900; https://doi.org/10.3390/app122311900 - 22 Nov 2022
Cited by 3 | Viewed by 1521
Abstract
The Gleason score was originally formulated to represent the heterogeneity of prostate cancer and helps to stratify the risk of patients affected by this tumor. The Gleason score assigning represents an on H&E stain task performed by pathologists upon histopathological examination of needle [...] Read more.
The Gleason score was originally formulated to represent the heterogeneity of prostate cancer and helps to stratify the risk of patients affected by this tumor. The Gleason score assigning represents an on H&E stain task performed by pathologists upon histopathological examination of needle biopsies or surgical specimens. In this paper, we propose an approach focused on the automatic Gleason score classification. We exploit a set of 18 radiomic features. The radiomic feature set is directly obtainable from segmented magnetic resonance images. We build several models considering supervised machine learning techniques, obtaining with the RandomForest classification algorithm a precision ranging from 0.803 to 0.888 and a recall from to 0.873 to 0.899. Moreover, with the aim to increase the never seen instance detection, we exploit the sigmoid calibration to better tune the built model. Full article
(This article belongs to the Special Issue New Applications of Deep Learning in Health Monitoring Systems)
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10 pages, 1596 KiB  
Brief Report
Pan-Cancer Classification of Gene Expression Data Based on Artificial Neural Network Model
by Claudia Cava, Christian Salvatore and Isabella Castiglioni
Appl. Sci. 2023, 13(13), 7355; https://doi.org/10.3390/app13137355 - 21 Jun 2023
Cited by 2 | Viewed by 1251
Abstract
Although precision classification is a vital issue for therapy, cancer diagnosis has been shown to have serious constraints. In this paper, we proposed a deep learning model based on gene expression data to perform a pan-cancer classification on 16 cancer types. We used [...] Read more.
Although precision classification is a vital issue for therapy, cancer diagnosis has been shown to have serious constraints. In this paper, we proposed a deep learning model based on gene expression data to perform a pan-cancer classification on 16 cancer types. We used principal component analysis (PCA) to decrease data dimensionality before building a neural network model for pan-cancer prediction. The performance of accuracy was monitored and optimized using the Adam algorithm. We compared the results of the model with a random forest classifier and XGBoost. The results show that the neural network model and random forest achieve high and similar classification performance (neural network mean accuracy: 0.84; random forest mean accuracy: 0.86; XGBoost mean accuracy: 0.90). Thus, we suggest future studies of neural network, random forest and XGBoost models for the detection of cancer in order to identify early treatment approaches to enhance cancer survival. Full article
(This article belongs to the Special Issue New Applications of Deep Learning in Health Monitoring Systems)
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