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The Convergence of Artificial Intelligence, Sensor and System for Sustainable E-healthcare Services and Biomedical Applications

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

Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 34932

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Special Issue Editors

Dr. Jungsuk Kim

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Guest Editor

Department of Biomedical Engineering, Gachon University, Incheon 21936, Korea
Interests: implantable prosthetic system; neural–electronic interfaces; nanopore sensors; point-of-care devices for nanopore sequencing; integrated circuit; VLSI; deep learning technologies for biomedical applications
Special Issues, Collections and Topics in MDPI journals

Dr. Imran Imran

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Guest Editor

1.Interdisciplinary Research Center for Intelligent Manufacturing & Robotics, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
2.Department of Research and Innovation, SCIEKORE Institute of Scientific Entrepreneurship and Technology, Batkhela 23020, Pakistan
Interests: IoT; AI; blockchain; interdisciplinary research; IT convergence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In contemporary research, AI, including machine learning and deep learning, has been widely applied to automate the time and resources constraint applications in diagnosis or pathology detection. This Special Issue is created with an interdisciplinary approach, involving topics that cover (i) the e-health applications of artificial intelligence and related technologies and (ii) biomedical engineering applications of IT and rapidly growing communication technologies, and (iii) the convergence of health sciences practices, artificial intelligence, mathematical optimization, and related technologies for medical image analysis, biomedical discovery, and healthcare services delivery. We expect high-quality research in these important domains that will impact the future of personalized and enterprise-level services management. Therefore, we invite research that brings together a collection of original research and review papers that cover modern technologies in all aspects of the topics of this Special Issue. Scientific contributions to the sustainability of the existing biomedical applications will contribute to the rapidly growing field of novel and innovative healthcare systems. Furthermore, we expect articles that will positively impact the domain knowledge and practices to improve healthcare practices.

This Special Issue is open to multidisciplinary research on the convergence of health sciences and artificial intelligence for sustainable e-healthcare services and biomedical applications. It covers original research articles, reviews, and communication surveys in the described domain that include but are not limited to the following topics:

Artificial-intelligence-based biomedical applications;
Sustainable e-healthcare services;
Toward the convergence of health sciences and artificial intelligence;
Smart health monitoring and diagnosis;
Big data intelligence for enterprise e-healthcare services;
Artificial-intelligence-based trust and privacy for personal healthcare;
Deep learning in biomedical image diagnosis;
Mathematical optimization in diagnosis, decision, and policymaking;
Smart-camera-based real health monitoring;
Sustainability in biomedical discovery and healthcare services;
Research trends in AI, such as federated learning, on Healthcare 4.0.

Dr. Jungsuk Kim
Dr. Imran
Guest Editors

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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

Artificial Intelligence
biomedicine
sustainability
deep learning
e-healthcare services
convergence
medical images
smart health monitoring
mathematical optimization
Healthcare 4.0
biomedical discovery

Published Papers (13 papers)

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Research

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18 pages, 2924 KiB

Open AccessArticle

Enhancing Medical Image Denoising with Innovative Teacher–Student Model-Based Approaches for Precision Diagnostics

by Shakhnoza Muksimova, Sabina Umirzakova, Sevara Mardieva and Young-Im Cho

Sensors 2023, 23(23), 9502; https://doi.org/10.3390/s23239502 - 29 Nov 2023

Cited by 1 | Viewed by 1355

Abstract

The realm of medical imaging is a critical frontier in precision diagnostics, where the clarity of the image is paramount. Despite advancements in imaging technology, noise remains a pervasive challenge that can obscure crucial details and impede accurate diagnoses. Addressing this, we introduce a novel teacher–student network model that leverages the potency of our bespoke NoiseContextNet Block to discern and mitigate noise with unprecedented precision. This innovation is coupled with an iterative pruning technique aimed at refining the model for heightened computational efficiency without compromising the fidelity of denoising. We substantiate the superiority and effectiveness of our approach through a comprehensive suite of experiments, showcasing significant qualitative enhancements across a multitude of medical imaging modalities. The visual results from a vast array of tests firmly establish our method’s dominance in producing clearer, more reliable images for diagnostic purposes, thereby setting a new benchmark in medical image denoising. Full article

(This article belongs to the Special Issue The Convergence of Artificial Intelligence, Sensor and System for Sustainable E-healthcare Services and Biomedical Applications)

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42 pages, 8975 KiB

Open AccessArticle

Enhancing Data Protection in Dynamic Consent Management Systems: Formalizing Privacy and Security Definitions with Differential Privacy, Decentralization, and Zero-Knowledge Proofs

by Muhammad Irfan Khalid, Mansoor Ahmed and Jungsuk Kim

Sensors 2023, 23(17), 7604; https://doi.org/10.3390/s23177604 - 1 Sep 2023

Cited by 2 | Viewed by 2902

Abstract

Dynamic consent management allows a data subject to dynamically govern her consent to access her data. Clearly, security and privacy guarantees are vital for the adoption of dynamic consent management systems. In particular, specific data protection guarantees can be required to comply with rules and laws (e.g., the General Data Protection Regulation (GDPR)). Since the primary instantiation of the dynamic consent management systems in the existing literature is towards developing sustainable e-healthcare services, in this paper, we study data protection issues in dynamic consent management systems, identifying crucial security and privacy properties and discussing severe limitations of systems described in the state of the art. We have presented the precise definitions of security and privacy properties that are essential to confirm the robustness of the dynamic consent management systems against diverse adversaries. Finally, under those precise formal definitions of security and privacy, we have proposed the implications of state-of-the-art tools and technologies such as differential privacy, blockchain technologies, zero-knowledge proofs, and cryptographic procedures that can be used to build dynamic consent management systems that are secure and private by design. Full article

(This article belongs to the Special Issue The Convergence of Artificial Intelligence, Sensor and System for Sustainable E-healthcare Services and Biomedical Applications)

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10 pages, 5631 KiB

Open AccessCommunication

Integrated High-Temporal-Resolution and High-Density Subretinal Prosthesis Using a Correlated Double-Sampling Technique

by Hosung Kang, Jungyeon Kim and Jungsuk Kim

Sensors 2023, 23(14), 6501; https://doi.org/10.3390/s23146501 - 18 Jul 2023

Cited by 1 | Viewed by 989

Abstract

This paper presents a 1600-pixel integrated neural stimulator with a correlated double-sampling readout (DSR) circuit for a subretinal prosthesis. The retinal stimulation chip inserted beneath the photoreceptor layer comprises an array of an active pixel sensor (APS) and biphasic pulse shaper. The DSR circuit achieves a high signal-to-noise ratio (SNR) of the APS with a short integration time to simultaneously improve the temporal and spatial resolutions of restored vision. This DSR circuit is adopted along with a 5 × 5-pixel tile, which reduces pixel size and improves the SNR by increasing the area occupied by storage capacitors. Moreover, a low-mismatch reference generator enables a low standard deviation between individual pulse shapers. The 1600-pixel retinal chip, fabricated using the 0.18 μm 1P6M CMOS process, occupies a total area of 4.3 mm × 3.3 mm and dissipates an average power of 3.4 mW; this was demonstrated by determining the stimulus current patterns corresponding to the illuminations of an LCD projector. Experimental results show that the proposed high-density stimulation array chip can achieve a high temporal resolution owing to its short integration time. Full article

(This article belongs to the Special Issue The Convergence of Artificial Intelligence, Sensor and System for Sustainable E-healthcare Services and Biomedical Applications)

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22 pages, 8959 KiB

Open AccessArticle

Decision Method of Optimal Needle Insertion Angle for Dorsal Hand Intravenous Robot

by Zihan Zhu, Kefeng Li, Guangyuan Zhang, Hualei Jin, Zhenfang Zhu and Peng Wang

Sensors 2023, 23(2), 848; https://doi.org/10.3390/s23020848 - 11 Jan 2023

Cited by 1 | Viewed by 1917

Abstract

In the context of COVID-19, the research on various aspects of the venipuncture robot field has become increasingly hot, but there has been little research on robotic needle insertion angles, primarily performed at a rough angle. This will increase the rate of puncture failure. Furthermore, there is sometimes significant pain due to the patients’ differences. This paper investigates the optimal needle entry angle decision for a dorsal hand intravenous injection robot. The dorsal plane of the hand was obtained by a linear structured light scan, which was used as a basis for calculating the needle entry angle. Simulation experiments were also designed to determine the optimal needle entry angle. Firstly, the linear structured optical system was calibrated and optimized, and the error function was constructed and solved iteratively by the optimization method to eliminate measurement error. Besides, the dorsal hand was scanned to obtain the spatial point clouds of the needle entry area, and the least squares method was used to fit it to obtain the dorsal hand plane. Then, the needle entry angle was calculated based on the needle entry area plane. Finally, the changes in the penetration force under different needle entry angles were analyzed to determine the optimal needle insertion angle. According to the experimental results, the average error of the optimized structured light plane position was about 0.1 mm, which meets the needs of the project, and a large angle should be properly selected for needle insertion during the intravenous injection. Full article

(This article belongs to the Special Issue The Convergence of Artificial Intelligence, Sensor and System for Sustainable E-healthcare Services and Biomedical Applications)

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12 pages, 2523 KiB

Open AccessArticle

Remote Photoplethysmography with a High-Speed Camera Reveals Temporal and Amplitude Differences between Glabrous and Non-Glabrous Skin

by Meiyun Cao, Timothy Burton, Gennadi Saiko and Alexandre Douplik

Sensors 2023, 23(2), 615; https://doi.org/10.3390/s23020615 - 5 Jan 2023

Cited by 2 | Viewed by 1952

Abstract

Photoplethysmography (PPG) is a noninvasive optical technology with applications including vital sign extraction and patient monitoring. However, its current use is primarily limited to heart rate and oxygenation monitoring. This study aims to demonstrate the utility of PPG for physiological investigations. In particular, we sought to demonstrate the utility of simultaneous data acquisition from several regions of tissue using remote/contactless PPG (rPPG). Specifically, using a high-speed scientific-grade camera, we collected rPPG from the hands (palmar/dorsal) of 22 healthy volunteers. Data collected through the red and green channels of the RGB CMOS sensor were analyzed. We found a statistically significant difference in the amplitude of the glabrous skin signal over the non-glabrous skin signal (1.41 ± 0.85 in the red channel and 2.27 ± 0.88 in the green channel). In addition, we found a statistically significant lead of the red channel over the green channel, which is consistent between glabrous (17.13 ± 10.69 ms) and non-glabrous (19.31 ± 12.66 ms) skin. We also found a statistically significant lead time (32.69 ± 55.26 ms in the red channel and 40.56 ± 26.97 ms in the green channel) of the glabrous PPG signal over the non-glabrous, which cannot be explained by bilateral variability. These results demonstrate the utility of rPPG imaging as a tool for fundamental physiological studies and can be used to inform the development of PPG-based devices. Full article

(This article belongs to the Special Issue The Convergence of Artificial Intelligence, Sensor and System for Sustainable E-healthcare Services and Biomedical Applications)

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28 pages, 2681 KiB

Open AccessArticle

An Integrated Methodology for Bibliometric Analysis: A Case Study of Internet of Things in Healthcare Applications

by Rahmat Ullah, Ikram Asghar and Mark G. Griffiths

Sensors 2023, 23(1), 67; https://doi.org/10.3390/s23010067 - 21 Dec 2022

Cited by 28 | Viewed by 5149

Abstract

This paper presents an integrated and easy methodology for bibliometric analysis. The proposed methodology is evaluated on recent research activities to highlight the role of the Internet of Things in healthcare applications. Different tools are used for bibliometric studies to explore the breadth and depth of different research areas. However, these Methods consider only the Web of Science or Scopus data for bibliometric analysis. Furthermore, bibliometric analysis has not been fully utilised to examine the capabilities of the Internet of Things for medical devices and their applications. There is a need for an easy methodology to use for a single integrated analysis of data from many sources rather than just the Web of Science or Scopus. A few bibliometric studies merge the Web of Science and Scopus to conduct a single integrated piece of research. This paper presents a methodology that could be used for a single bibliometric analysis across multiple databases. Three freely available tools, Excel, Perish or Publish and the R package Bibliometrix, are used for the purpose. The proposed bibliometric methodology is evaluated for studies related to the Internet of Medical Things (IoMT) and its applications in healthcare settings. An inclusion/exclusion criterion is developed to explore relevant studies from the seven largest databases, including Scopus, Web of Science, IEEE, ACM digital library, PubMed, Science Direct and Google Scholar. The study focuses on factors such as the number of publications, citations per paper, collaborative research output, h-Index, primary research and healthcare application areas. Data for this study are collected from the seven largest academic databases for 2012 to 2022 related to IoMT and their applications in healthcare. The bibliometric data analysis generated different research themes within IoMT technologies and their applications in healthcare research. The study has also identified significant research areas in this field. The leading research countries and their contributions are another output from the data analysis. Finally, future research directions are proposed for researchers to explore this area in further detail. Full article

(This article belongs to the Special Issue The Convergence of Artificial Intelligence, Sensor and System for Sustainable E-healthcare Services and Biomedical Applications)

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26 pages, 5654 KiB

Open AccessArticle

IGWO-IVNet3: DL-Based Automatic Diagnosis of Lung Nodules Using an Improved Gray Wolf Optimization and InceptionNet-V3

by Anas Bilal, Muhammad Shafiq, Fang Fang, Muhammad Waqar, Inam Ullah, Yazeed Yasin Ghadi, Haixia Long and Rao Zeng

Sensors 2022, 22(24), 9603; https://doi.org/10.3390/s22249603 - 7 Dec 2022

Cited by 28 | Viewed by 2443

Abstract

Artificial intelligence plays an essential role in diagnosing lung cancer. Lung cancer is notoriously difficult to diagnose until it has progressed to a late stage, making it a leading cause of cancer-related mortality. Lung cancer is fatal if not treated early, making this a significant issue. Initial diagnosis of malignant nodules is often made using chest radiography (X-ray) and computed tomography (CT) scans; nevertheless, the possibility of benign nodules leads to wrong choices. In their first phases, benign and malignant nodules seem very similar. Additionally, radiologists have a hard time viewing and categorizing lung abnormalities. Lung cancer screenings performed by radiologists are often performed with the use of computer-aided diagnostic technologies. Computer scientists have presented many methods for identifying lung cancer in recent years. Low-quality images compromise the segmentation process, rendering traditional lung cancer prediction algorithms inaccurate. This article suggests a highly effective strategy for identifying and categorizing lung cancer. Noise in the pictures was reduced using a weighted filter, and the improved Gray Wolf Optimization method was performed before segmentation with watershed modification and dilation operations. We used InceptionNet-V3 to classify lung cancer into three groups, and it performed well compared to prior studies: 98.96% accuracy, 94.74% specificity, as well as 100% sensitivity. Full article

(This article belongs to the Special Issue The Convergence of Artificial Intelligence, Sensor and System for Sustainable E-healthcare Services and Biomedical Applications)

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11 pages, 3849 KiB

Open AccessArticle

Wearable Healthcare Monitoring Based on a Microfluidic Electrochemical Integrated Device for Sensing Glucose in Natural Sweat

by Zouaghi Noura, Imran Shah, Shahid Aziz, Aamouche Ahmed, Dong-Won Jung, Lakssir Brahim and Ressami ElMostafa

Sensors 2022, 22(22), 8971; https://doi.org/10.3390/s22228971 - 19 Nov 2022

Cited by 12 | Viewed by 3000

Abstract

Wearable sweat sensors offer the possibility of continuous real-time health monitoring of an individual at a low cost without invasion. A variety of sweat glucose sensors have been developed thus far to help diabetes patients frequently monitor blood glucose levels through sweat glucose as a surrogate marker. The present study demonstrates the development and characterization of a three-dimensional paper-based microfluidic electrochemical integrated device (3D PMED) for measuring glucose concentration in sweat in real-time via simple, non-invasive, capillary-action-based sample collection. The device was selective for glucose, and it detected glucose accurately in the clinically relevant range (0~2 mM) in an off-body setup. To the best of our knowledge, this is the first time NEXAR™ has been used for biosensing applications. Further, the developed glucose sensor has acceptable sensitivity of 16.8 µA/mM/cm². Importantly, in an on-body setup, the device achieved a significant amperometric response to sweat glucose in a very short amount of time (a few seconds). With detailed investigations, this proof-of-concept study could help further the development of sensitive and selective sweat-based glucose sensing devices for real-time glucose monitoring in diabetes patients. Full article

(This article belongs to the Special Issue The Convergence of Artificial Intelligence, Sensor and System for Sustainable E-healthcare Services and Biomedical Applications)

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9 pages, 3211 KiB

Open AccessCommunication

Reverse Offset Printed, Biocompatible Temperature Sensor Based on Dark Muscovado

by Shahid Aziz, Junaid Ali, Krishna Singh Bhandari, Wenning Chen, Sijia Li and Dong Won Jung

Sensors 2022, 22(22), 8726; https://doi.org/10.3390/s22228726 - 11 Nov 2022

Viewed by 1764

Abstract

A reverse-offset printed temperature sensor based on interdigitated electrodes (IDTs) has been investigated in this study. Silver nanoparticles (AgNPs) were printed on a glass slide in an IDT pattern by reverse-offset printer. The sensing layer consisted of a sucrose film obtained by spin coating the sucrose solution on the IDTs. The temperature sensor demonstrated a negative temperature coefficient (NTC) with an exponential decrease in resistance as the temperature increased. This trend is the characteristic of a NTC thermistor. There is an overall change of ~2800 kΩ for the temperature change of 0 °C to 100 °C. The thermistor is based on a unique temperature sensor using a naturally occurring biocompatible material, i.e., sucrose. The active sensing material of the thermistor, i.e., sucrose used in the experiments was obtained from extract of Muscovado. Our temperature sensor has potential in the biomedical and food industries where environmentally friendly and biocompatible materials are more suitable for sensing accurately and reliably. Full article

(This article belongs to the Special Issue The Convergence of Artificial Intelligence, Sensor and System for Sustainable E-healthcare Services and Biomedical Applications)

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17 pages, 1856 KiB

Open AccessArticle

A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images

by Naeem Ullah, Mohammad Sohail Khan, Javed Ali Khan, Ahyoung Choi and Muhammad Shahid Anwar

Sensors 2022, 22(19), 7575; https://doi.org/10.3390/s22197575 - 6 Oct 2022

Cited by 18 | Viewed by 2453

Abstract

Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, and prone to error. Magnetic resonance imaging (MRI) scans are mostly used for tumor detection due to their non-invasive properties and also avoid painful biopsy. MRI scanning of one patient’s brain generates many 3D images from multiple directions, making the manual detection of tumors very difficult, error-prone, and time-consuming. Therefore, there is a considerable need for autonomous diagnostics tools to detect brain tumors accurately. In this research, we have presented a novel TumorResnet deep learning (DL) model for brain detection, i.e., binary classification. The TumorResNet model employs 20 convolution layers with a leaky ReLU (LReLU) activation function for feature map activation to compute the most distinctive deep features. Finally, three fully connected classification layers are used to classify brain tumors MRI into normal and tumorous. The performance of the proposed TumorResNet architecture is evaluated on a standard Kaggle brain tumor MRI dataset for brain tumor detection (BTD), which contains brain tumor and normal MR images. The proposed model achieved a good accuracy of 99.33% for BTD. These experimental results, including the cross-dataset setting, validate the superiority of the TumorResNet model over the contemporary frameworks. This study offers an automated BTD method that aids in the early diagnosis of brain cancers. This procedure has a substantial impact on improving treatment options and patient survival. Full article

(This article belongs to the Special Issue The Convergence of Artificial Intelligence, Sensor and System for Sustainable E-healthcare Services and Biomedical Applications)

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13 pages, 2858 KiB

Open AccessArticle

Real-Time Sound Source Localization for Low-Power IoT Devices Based on Multi-Stream CNN

by Jungbeom Ko, Hyunchul Kim and Jungsuk Kim

Sensors 2022, 22(12), 4650; https://doi.org/10.3390/s22124650 - 20 Jun 2022

Cited by 7 | Viewed by 3008

Abstract

Voice-activated artificial intelligence (AI) technology has advanced rapidly and is being adopted in various devices such as smart speakers and display products, which enable users to multitask without touching the devices. However, most devices equipped with cameras and displays lack mobility; therefore, users cannot avoid touching them for face-to-face interactions, which contradicts the voice-activated AI philosophy. In this paper, we propose a deep neural network-based real-time sound source localization (SSL) model for low-power internet of things (IoT) devices based on microphone arrays and present a prototype implemented on actual IoT devices. The proposed SSL model delivers multi-channel acoustic data to parallel convolutional neural network layers in the form of multiple streams to capture the unique delay patterns for the low-, mid-, and high-frequency ranges, and estimates the fine and coarse location of voices. The model adapted in this study achieved an accuracy of 91.41% on fine location estimation and a direction of arrival error of 7.43° on noisy data. It achieved a processing time of 7.811 ms per 40 ms samples on the Raspberry Pi 4B. The proposed model can be applied to a camera-based humanoid robot that mimics the manner in which humans react to trigger voices in crowded environments. Full article

(This article belongs to the Special Issue The Convergence of Artificial Intelligence, Sensor and System for Sustainable E-healthcare Services and Biomedical Applications)

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Review

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27 pages, 707 KiB

Open AccessReview

A Systematic Review of Online Speech Therapy Systems for Intervention in Childhood Speech Communication Disorders

by Geertruida Aline Attwell, Kwabena Ebo Bennin and Bedir Tekinerdogan

Sensors 2022, 22(24), 9713; https://doi.org/10.3390/s22249713 - 11 Dec 2022

Cited by 5 | Viewed by 4132

Abstract

Currently, not all children that need speech therapy have access to a therapist. With the current international shortage of speech–language pathologists (SLPs), there is a demand for online tools to support SLPs with their daily tasks. Several online speech therapy (OST) systems have been designed and proposed in the literature; however, the implementation of these systems is lacking. The technical knowledge that is needed to use these programs is a challenge for SLPs. There has been limited effort to systematically identify, analyze and report the findings of prior studies. We provide the results of an extensive literature review of OST systems for childhood speech communication disorders. We systematically review OST systems that can be used in clinical settings or from home as part of a treatment program for children with speech communication disorders. Our search strategy found 4481 papers, of which 35 were identified as focusing on speech therapy programs for speech communication disorders. The features of these programs were examined, and the main findings are extracted and presented. Our analysis indicates that most systems which are designed mainly to support the SLPs adopt and use supervised machine learning approaches that are either desktop-based or mobile-phone-based applications. Our findings reveal that speech therapy systems can provide important benefits for childhood speech. A collaboration between computer programmers and SLPs can contribute to implementing useful automated programs, leading to more children having access to good speech therapy. Full article

(This article belongs to the Special Issue The Convergence of Artificial Intelligence, Sensor and System for Sustainable E-healthcare Services and Biomedical Applications)

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Other

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26 pages, 4256 KiB

Open AccessSystematic Review

Data Provenance in Healthcare: Approaches, Challenges, and Future Directions

by Mansoor Ahmed, Amil Rohani Dar, Markus Helfert, Abid Khan and Jungsuk Kim

Sensors 2023, 23(14), 6495; https://doi.org/10.3390/s23146495 - 18 Jul 2023

Cited by 1 | Viewed by 1896

Abstract

Data provenance means recording data origins and the history of data generation and processing. In healthcare, data provenance is one of the essential processes that make it possible to track the sources and reasons behind any problem with a user’s data. With the emergence of the General Data Protection Regulation (GDPR), data provenance in healthcare systems should be implemented to give users more control over data. This SLR studies the impacts of data provenance in healthcare and GDPR-compliance-based data provenance through a systematic review of peer-reviewed articles. The SLR discusses the technologies used to achieve data provenance and various methodologies to achieve data provenance. We then explore different technologies that are applied in the healthcare domain and how they achieve data provenance. In the end, we have identified key research gaps followed by future research directions. Full article

(This article belongs to the Special Issue The Convergence of Artificial Intelligence, Sensor and System for Sustainable E-healthcare Services and Biomedical Applications)

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