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AI and IoT Enabled Solutions for Healthcare

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

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 47192

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Special Issue Editors

Dr. Samaneh Kouchaki

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Guest Editor

Centre for Vision, Speech, and Signal Processing (CVSSP), Department of Electrical and Electronic Engineering, University of Surrey, Guildford, Surrey GU2 7XH, UK
Interests: machine learning; health informatics; biomedical signal processing; computational biology

Dr. Xiao-Rong Ding

E-Mail Website
Guest Editor

School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: wearable health monitoring; biomedical signal processing; artificial intelligence in healthcare

Prof. Dr. Saeid Sanei

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Guest Editor

School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
Interests: signal processing; biomedical signal processing; machine learning; body sensor networking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ever-increasing quantities of clinical data are routinely collected concerning all aspects of patient care throughout the patient’s life. Internet of Things (IoT) and smart home technologies provide an opportunity to monitor people in their homes without any disruption in their daily activities. Digital health records offer a rich source of clinical information. Innovations in AI and machine learning can facilitate faster patient monitoring, management, and treatment, and convert a hospital-only treatment pathway into cost-effective combined home-hospital or even outpatient alternatives, which improve the overall quality of healthcare and pave the path for personalised medicine. However, analysing the real-time collected data poses several challenges as the data have significant artefacts due to transmission and recording limitation, are highly imbalanced and incomplete due to subject variabilities and resource limitation, and involve various modalities. Moreover, data labelling is cumbersome and often involves uncertainty. This Special Issue aims to attract innovative and novel machine learning developments and IoT solutions around the challenges in healthcare applications. The Special Issue topics include but are not limited to the following:

- Assistive AI;

- Augmentation techniques including adversarial networks;

- Data imputation;

- Dealing with noisy labels;

- Deep learning;

- Ensemble learning;

- Independent living;

- Interpretable machine learning;

- Learning under uncertainty, noise, and imbalanced data;

- More efficient delivery of healthcare and healthcare applications;

- Multi-modal and heterogeneous data analysis;

- Reinforcement learning;

- Remote/smart monitoring of patients;

- Semi-supervised learning;

- Unsupervised learning;

- Weakly supervised/self-supervised learning.

Dr. Samaneh Kouchaki
Dr. Xiaorong Ding
Prof. Dr. Saeid Sanei

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.

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Keywords

Deep learning
Health informatics
Machine learning
Multi-modal data analysis
Reinforcement learning
Remote/smart monitoring

Published Papers (11 papers)

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Editorial

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4 pages, 132 KiB

Open AccessEditorial

AI- and IoT-Enabled Solutions for Healthcare

by Samaneh Kouchaki, Xiaorong Ding and Saeid Sanei

Sensors 2024, 24(8), 2607; https://0-doi-org.brum.beds.ac.uk/10.3390/s24082607 - 19 Apr 2024

Viewed by 225

Abstract

Patient care and management have entered a new arena, where intelligent technology can assist clinicians in both diagnosis and treatment [...] Full article

(This article belongs to the Special Issue AI and IoT Enabled Solutions for Healthcare)

Research

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12 pages, 1339 KiB

Open AccessArticle

A Stacked Long Short-Term Memory Approach for Predictive Blood Glucose Monitoring in Women with Gestational Diabetes Mellitus

by Huiqi Y. Lu, Ping Lu, Jane E. Hirst, Lucy Mackillop and David A. Clifton

Sensors 2023, 23(18), 7990; https://0-doi-org.brum.beds.ac.uk/10.3390/s23187990 - 20 Sep 2023

Cited by 1 | Viewed by 1049

Abstract

Gestational diabetes mellitus (GDM) is a subtype of diabetes that develops during pregnancy. Managing blood glucose (BG) within the healthy physiological range can reduce clinical complications for women with gestational diabetes. The objectives of this study are to (1) develop benchmark glucose prediction models with long short-term memory (LSTM) recurrent neural network models using time-series data collected from the GDm-Health platform, (2) compare the prediction accuracy with published results, and (3) suggest an optimized clinical review schedule with the potential to reduce the overall number of blood tests for mothers with stable and within-range glucose measurements. A total of 190,396 BG readings from 1110 patients were used for model development, validation and testing under three different prediction schemes: 7 days of BG readings to predict the next 7 or 14 days and 14 days to predict 14 days. Our results show that the optimized BG schedule based on a 7-day observational window to predict the BG of the next 14 days achieved the accuracies of the root mean square error (RMSE) = 0.958 ± 0.007, 0.876 ± 0.003, 0.898 ± 0.003, 0.622 ± 0.003, 0.814 ± 0.009 and 0.845 ± 0.005 for the after-breakfast, after-lunch, after-dinner, before-breakfast, before-lunch and before-dinner predictions, respectively. This is the first machine learning study that suggested an optimized blood glucose monitoring frequency, which is 7 days to monitor the next 14 days based on the accuracy of blood glucose prediction. Moreover, the accuracy of our proposed model based on the fingerstick blood glucose test is on par with the prediction accuracies compared with the benchmark performance of one-hour prediction models using continuous glucose monitoring (CGM) readings. In conclusion, the stacked LSTM model is a promising approach for capturing the patterns in time-series data, resulting in accurate predictions of BG levels. Using a deep learning model with routine fingerstick glucose collection is a promising, predictable and low-cost solution for BG monitoring for women with gestational diabetes. Full article

(This article belongs to the Special Issue AI and IoT Enabled Solutions for Healthcare)

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17 pages, 16937 KiB

Open AccessArticle

Classification of Tetanus Severity in Intensive-Care Settings for Low-Income Countries Using Wearable Sensing

by Ping Lu, Shadi Ghiasi, Jannis Hagenah, Ho Bich Hai, Nguyen Van Hao, Phan Nguyen Quoc Khanh, Le Dinh Van Khoa, VITAL Consortium, Louise Thwaites, David A. Clifton and Tingting Zhu

Sensors 2022, 22(17), 6554; https://0-doi-org.brum.beds.ac.uk/10.3390/s22176554 - 30 Aug 2022

Cited by 4 | Viewed by 3304

Abstract

Infectious diseases remain a common problem in low- and middle-income countries, including in Vietnam. Tetanus is a severe infectious disease characterized by muscle spasms and complicated by autonomic nervous system dysfunction in severe cases. Patients require careful monitoring using electrocardiograms (ECGs) to detect deterioration and the onset of autonomic nervous system dysfunction as early as possible. Machine learning analysis of ECG has been shown of extra value in predicting tetanus severity, however any additional ECG signal analysis places a high demand on time-limited hospital staff and requires specialist equipment. Therefore, we present a novel approach to tetanus monitoring from low-cost wearable sensors combined with a deep-learning-based automatic severity detection. This approach can automatically triage tetanus patients and reduce the burden on hospital staff. In this study, we propose a two-dimensional (2D) convolutional neural network with a channel-wise attention mechanism for the binary classification of ECG signals. According to the Ablett classification of tetanus severity, we define grades 1 and 2 as mild tetanus and grades 3 and 4 as severe tetanus. The one-dimensional ECG time series signals are transformed into 2D spectrograms. The 2D attention-based network is designed to extract the features from the input spectrograms. Experiments demonstrate a promising performance for the proposed method in tetanus classification with an F1 score of 0.79 ± 0.03, precision of 0.78 ± 0.08, recall of 0.82 ± 0.05, specificity of 0.85 ± 0.08, accuracy of 0.84 ± 0.04 and AUC of 0.84 ± 0.03. Full article

(This article belongs to the Special Issue AI and IoT Enabled Solutions for Healthcare)

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18 pages, 3314 KiB

Open AccessArticle

Automated Chicago Classification for Esophageal Motility Disorder Diagnosis Using Machine Learning

by Teodora Surdea-Blaga, Gheorghe Sebestyen, Zoltan Czako, Anca Hangan, Dan Lucian Dumitrascu, Abdulrahman Ismaiel, Liliana David, Imre Zsigmond, Giuseppe Chiarioni, Edoardo Savarino, Daniel Corneliu Leucuta and Stefan Lucian Popa

Sensors 2022, 22(14), 5227; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145227 - 13 Jul 2022

Cited by 2 | Viewed by 2339

Abstract

The goal of this paper is to provide a Machine Learning-based solution that can be utilized to automate the Chicago Classification algorithm, the state-of-the-art scheme for esophageal motility disease identification. First, the photos were preprocessed by locating the area of interest—the precise instant of swallowing. After resizing and rescaling the photos, they were utilized as input for the Deep Learning models. The InceptionV3 Deep Learning model was used to identify the precise class of the IRP. We used the DenseNet201 CNN architecture to classify the images into 5 different classes of swallowing disorders. Finally, we combined the results of the two trained ML models to automate the Chicago Classification algorithm. With this solution we obtained a top-1 accuracy and f1-score of 86% with no human intervention, automating the whole flow, from image preprocessing until Chicago classification and diagnosis. Full article

(This article belongs to the Special Issue AI and IoT Enabled Solutions for Healthcare)

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18 pages, 4699 KiB

Open AccessArticle

Machine Learning-Based Risk Stratification for Gestational Diabetes Management

by Jenny Yang, David Clifton, Jane E. Hirst, Foteini K. Kavvoura, George Farah, Lucy Mackillop and Huiqi Lu

Sensors 2022, 22(13), 4805; https://0-doi-org.brum.beds.ac.uk/10.3390/s22134805 - 25 Jun 2022

Cited by 5 | Viewed by 2230

Abstract

Gestational diabetes mellitus (GDM) is often diagnosed during the last trimester of pregnancy, leaving only a short timeframe for intervention. However, appropriate assessment, management, and treatment have been shown to reduce the complications of GDM. This study introduces a machine learning-based stratification system for identifying patients at risk of exhibiting high blood glucose levels, based on daily blood glucose measurements and electronic health record (EHR) data from GDM patients. We internally trained and validated our model on a cohort of 1148 pregnancies at Oxford University Hospitals NHS Foundation Trust (OUH), and performed external validation on 709 patients from Royal Berkshire Hospital NHS Foundation Trust (RBH). We trained linear and non-linear tree-based regression models to predict the proportion of high-readings (readings above the UK’s National Institute for Health and Care Excellence [NICE] guideline) a patient may exhibit in upcoming days, and found that XGBoost achieved the highest performance during internal validation (0.021 [CI 0.019–0.023], 0.482 [0.442–0.516], and 0.112 [0.109–0.116], for MSE, R2, MAE, respectively). The model also performed similarly during external validation, suggesting that our method is generalizable across different cohorts of GDM patients. Full article

(This article belongs to the Special Issue AI and IoT Enabled Solutions for Healthcare)

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18 pages, 2729 KiB

Open AccessArticle

An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples

by Olusola O. Abayomi-Alli, Robertas Damaševičius, Rytis Maskeliūnas and Sanjay Misra

Sensors 2022, 22(6), 2224; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062224 - 13 Mar 2022

Cited by 21 | Viewed by 3328

Abstract

Current research endeavors in the application of artificial intelligence (AI) methods in the diagnosis of the COVID-19 disease has proven indispensable with very promising results. Despite these promising results, there are still limitations in real-time detection of COVID-19 using reverse transcription polymerase chain reaction (RT-PCR) test data, such as limited datasets, imbalance classes, a high misclassification rate of models, and the need for specialized research in identifying the best features and thus improving prediction rates. This study aims to investigate and apply the ensemble learning approach to develop prediction models for effective detection of COVID-19 using routine laboratory blood test results. Hence, an ensemble machine learning-based COVID-19 detection system is presented, aiming to aid clinicians to diagnose this virus effectively. The experiment was conducted using custom convolutional neural network (CNN) models as a first-stage classifier and 15 supervised machine learning algorithms as a second-stage classifier: K-Nearest Neighbors, Support Vector Machine (Linear and RBF), Naive Bayes, Decision Tree, Random Forest, MultiLayer Perceptron, AdaBoost, ExtraTrees, Logistic Regression, Linear and Quadratic Discriminant Analysis (LDA/QDA), Passive, Ridge, and Stochastic Gradient Descent Classifier. Our findings show that an ensemble learning model based on DNN and ExtraTrees achieved a mean accuracy of 99.28% and area under curve (AUC) of 99.4%, while AdaBoost gave a mean accuracy of 99.28% and AUC of 98.8% on the San Raffaele Hospital dataset, respectively. The comparison of the proposed COVID-19 detection approach with other state-of-the-art approaches using the same dataset shows that the proposed method outperforms several other COVID-19 diagnostics methods. Full article

(This article belongs to the Special Issue AI and IoT Enabled Solutions for Healthcare)

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15 pages, 702 KiB

Open AccessArticle

Examination of Potential of Thermopile-Based Contactless Respiratory Gating

by Qi Zhan, Wenjin Wang and Xiaorong Ding

Sensors 2021, 21(16), 5525; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165525 - 17 Aug 2021

Cited by 3 | Viewed by 1724

Abstract

To control the spread of coronavirus disease 2019 (COVID-19), it is effective to perform a fast screening of the respiratory rate of the subject at the gate before entering a space to assess the potential risks. In this paper, we examine the potential of a novel yet cost-effective solution, called thermopile-based respiratory gating, to contactlessly screen a subject by measuring their respiratory rate in the scenario with an entrance gate. Based on a customized thermopile array system, we investigate different image and signal processing methods that measure respiratory rate from low-resolution thermal videos, where an automatic region-of-interest selection-based approach obtains a mean absolute error (MAE) of 0.8 breaths per minute. We show the feasibility of thermopile-based respiratory gating and quantify its limitations and boundary conditions in a benchmark (e.g., appearance of face mask, measurement distance and screening time). The technical validation provided by this study is helpful for designing and implementing a respiratory gating solution toward the prevention of the spread of COVID-19 during the pandemic. Full article

(This article belongs to the Special Issue AI and IoT Enabled Solutions for Healthcare)

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13 pages, 1951 KiB

Open AccessCommunication

A Lightweight Attention-Based CNN Model for Efficient Gait Recognition with Wearable IMU Sensors

by Haohua Huang, Pan Zhou, Ye Li and Fangmin Sun

Sensors 2021, 21(8), 2866; https://0-doi-org.brum.beds.ac.uk/10.3390/s21082866 - 19 Apr 2021

Cited by 29 | Viewed by 5159

Abstract

Wearable sensors-based gait recognition is an effective method to recognize people’s identity by recognizing the unique way they walk. Recently, the adoption of deep learning networks for gait recognition has achieved significant performance improvement and become a new promising trend. However, most of the existing studies mainly focused on improving the gait recognition accuracy while ignored model complexity, which make them unsuitable for wearable devices. In this study, we proposed a lightweight attention-based Convolutional Neural Networks (CNN) model for wearable gait recognition. Specifically, a four-layer lightweight CNN was first employed to extract gait features. Then, a novel attention module based on contextual encoding information and depthwise separable convolution was designed and integrated into the lightweight CNN to enhance the extracted gait features and simplify the complexity of the model. Finally, the Softmax classifier was used for classification to realize gait recognition. We conducted comprehensive experiments to evaluate the performance of the proposed model on whuGait and OU-ISIR datasets. The effect of the proposed attention mechanisms, different data segmentation methods, and different attention mechanisms on gait recognition performance were studied and analyzed. The comparison results with the existing similar researches in terms of recognition accuracy and number of model parameters shown that our proposed model not only achieved a higher recognition performance but also reduced the model complexity by 86.5% on average. Full article

(This article belongs to the Special Issue AI and IoT Enabled Solutions for Healthcare)

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Review

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19 pages, 2042 KiB

Open AccessReview

Survey of Explainable AI Techniques in Healthcare

by Ahmad Chaddad, Jihao Peng, Jian Xu and Ahmed Bouridane

Sensors 2023, 23(2), 634; https://0-doi-org.brum.beds.ac.uk/10.3390/s23020634 - 05 Jan 2023

Cited by 74 | Viewed by 16080

Abstract

Artificial intelligence (AI) with deep learning models has been widely applied in numerous domains, including medical imaging and healthcare tasks. In the medical field, any judgment or decision is fraught with risk. A doctor will carefully judge whether a patient is sick before forming a reasonable explanation based on the patient’s symptoms and/or an examination. Therefore, to be a viable and accepted tool, AI needs to mimic human judgment and interpretation skills. Specifically, explainable AI (XAI) aims to explain the information behind the black-box model of deep learning that reveals how the decisions are made. This paper provides a survey of the most recent XAI techniques used in healthcare and related medical imaging applications. We summarize and categorize the XAI types, and highlight the algorithms used to increase interpretability in medical imaging topics. In addition, we focus on the challenging XAI problems in medical applications and provide guidelines to develop better interpretations of deep learning models using XAI concepts in medical image and text analysis. Furthermore, this survey provides future directions to guide developers and researchers for future prospective investigations on clinical topics, particularly on applications with medical imaging. Full article

(This article belongs to the Special Issue AI and IoT Enabled Solutions for Healthcare)

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Other

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35 pages, 3946 KiB

Open AccessSystematic Review

A Systematic Review of Internet of Things in Clinical Laboratories: Opportunities, Advantages, and Challenges

by Tahir Munir, Muhammad Soomair Akbar, Sadia Ahmed, Azza Sarfraz, Zouina Sarfraz, Muzna Sarfraz, Miguel Felix and Ivan Cherrez-Ojeda

Sensors 2022, 22(20), 8051; https://0-doi-org.brum.beds.ac.uk/10.3390/s22208051 - 21 Oct 2022

Cited by 5 | Viewed by 2912

Abstract

The Internet of Things (IoT) is the network of physical objects embedded with sensors, software, electronics, and online connectivity systems. This study explores the role of IoT in clinical laboratory processes; this systematic review was conducted adhering to the PRISMA Statement 2020 guidelines. We included IoT models and applications across preanalytical, analytical, and postanalytical laboratory processes. PubMed, Cochrane Central, CINAHL Plus, Scopus, IEEE, and A.C.M. Digital library were searched between August 2015 to August 2022; the data were tabulated. Cohen’s coefficient of agreement was calculated to quantify inter-reviewer agreements; a total of 18 studies were included with Cohen’s coefficient computed to be 0.91. The included studies were divided into three classifications based on availability, including preanalytical, analytical, and postanalytical. The majority (77.8%) of the studies were real-tested. Communication-based approaches were the most common (83.3%), followed by application-based approaches (44.4%) and sensor-based approaches (33.3%) among the included studies. Open issues and challenges across the included studies included scalability, costs and energy consumption, interoperability, privacy and security, and performance issues. In this study, we identified, classified, and evaluated IoT applicability in clinical laboratory systems. This study presents pertinent findings for IoT development across clinical laboratory systems, for which it is essential that more rigorous and efficient testing and studies be conducted in the future. Full article

(This article belongs to the Special Issue AI and IoT Enabled Solutions for Healthcare)

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56 pages, 17422 KiB

Open AccessSystematic Review

Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review

by Konstantina-Maria Giannakopoulou, Ioanna Roussaki and Konstantinos Demestichas

Sensors 2022, 22(5), 1799; https://0-doi-org.brum.beds.ac.uk/10.3390/s22051799 - 24 Feb 2022

Cited by 31 | Viewed by 6197

Abstract

Parkinson’s disease is a chronic neurodegenerative disease that affects a large portion of the population, especially the elderly. It manifests with motor, cognitive and other types of symptoms, decreasing significantly the patients’ quality of life. The recent advances in the Internet of Things and Artificial Intelligence fields, including the subdomains of machine learning and deep learning, can support Parkinson’s disease patients, their caregivers and clinicians at every stage of the disease, maximizing the treatment effectiveness and minimizing the respective healthcare costs at the same time. In this review, the considered studies propose machine learning models, trained on data acquired via smart devices, wearable or non-wearable sensors and other Internet of Things technologies, to provide predictions or estimations regarding Parkinson’s disease aspects. Seven hundred and seventy studies have been retrieved from three dominant academic literature databases. Finally, one hundred and twelve of them have been selected in a systematic way and have been considered in the state-of-the-art systematic review presented in this paper. These studies propose various methods, applied on various sensory data to address different Parkinson’s disease-related problems. The most widely deployed sensors, the most commonly addressed problems and the best performing algorithms are highlighted. Finally, some challenges are summarized along with some future considerations and opportunities that arise. Full article

(This article belongs to the Special Issue AI and IoT Enabled Solutions for Healthcare)

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