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Deep Learning for Sensor-Driven Medical 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 (28 August 2023) | Viewed by 3585

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Special Issue Editors

Dr. Shankar Kathiresan

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Guest Editor

Big Data and Machine Learning Lab South Ural State, University Chelyabinsk, Chelyabinsk 454080, Russia
Interests: deep learning; medical image analysis; healthcare applications; secret sharing scheme & digital image security
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Seifedine Kadry

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Guest Editor

Department of Applied Data Science, Norrof University College, 4608 Kristiansand, Norway
Interests: AI; machine learning; medical image processing
Special Issues, Collections and Topics in MDPI journals

Dr. Gyanendra Prasad Joshi

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Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: MAC; routing protocols for next-generation wireless networks; wireless sensor networks; cognitive radio networks; RFID systems; IoT; smart city; deep learning; digital convergence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in sensing technologies have enabled the healthcare sector to improve the quality of its services. Furthermore, the design of small and lightweight smart sensors has enabled systems to act as a vital part of advanced developments in unobtrusive and unsupervised approaches to home-rehabilitation and the continuous monitoring of patients’ health status. In the present healthcare system, the application of deep learning (DL) is widespread to accomplish enhanced quality of service in disease diagnosis, acute disease detection, image analysis, drug discovery, drug delivery, and smart health monitoring. This Special Issue on “Deep learning for Sensor-Driven Medical Applications” focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. We offered this topic, being aware of the fundamental role that smart sensors can have in enhancing the quality of healthcare services in both acute and chronic conditions as well as for prevention towards a healthy life and active aging. This Special Issue welcomes both original research papers and review articles focusing on innovative ideas. Topics of interest include, but are not limited to:

Sensor-enabled medical data for disease diagnosis and monitoring;
Computer aided diagnosis models;
Wearables and Telemedicine;
Data Security and Privacy Mechanisms in sensor enabled healthcare system;
Deep learning for medical data;
Computational intelligence for medical data;
Big data analytics for healthcare applications;
Sensors and Systems for Brain Computer Interfaces.

Dr. Shankar Kathiresan
Prof. Dr. Seifedine Kadry
Dr. Gyanendra Prasad Joshi
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.

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Research

23 pages, 2787 KiB

Open AccessArticle

BIR: Biomedical Information Retrieval System for Cancer Treatment in Electronic Health Record Using Transformers

by Pir Noman Ahmad, Yuanchao Liu, Khalid Khan, Tao Jiang and Umama Burhan

Sensors 2023, 23(23), 9355; https://doi.org/10.3390/s23239355 - 23 Nov 2023

Cited by 1 | Viewed by 863

Abstract

The rapid growth of electronic health records (EHRs) has led to unprecedented biomedical data. Clinician access to the latest patient information can improve the quality of healthcare. However, clinicians have difficulty finding information quickly and easily due to the sheer data mining volume. Biomedical information retrieval (BIR) systems can help clinicians find the information required by automatically searching EHRs and returning relevant results. However, traditional BIR systems cannot understand the complex relationships between EHR entities. Transformers are a new type of neural network that is very effective for natural language processing (NLP) tasks. As a result, transformers are well suited for tasks such as machine translation and text summarization. In this paper, we propose a new BIR system for EHRs that uses transformers for predicting cancer treatment from EHR. Our system can understand the complex relationships between the different entities in an EHR, which allows it to return more relevant results to clinicians. We evaluated our system on a dataset of EHRs and found that it outperformed state-of-the-art BIR systems on various tasks, including medical question answering and information extraction. Our results show that Transformers are a promising approach for BIR in EHRs, reaching an accuracy and an F1-score of 86.46%, and 0.8157, respectively. We believe that our system can help clinicians find the information they need more quickly and easily, leading to improved patient care. Full article

(This article belongs to the Special Issue Deep Learning for Sensor-Driven Medical Applications)

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16 pages, 2127 KiB

Open AccessArticle

A Convolutional Neural Network for Beamforming and Image Reconstruction in Passive Cavitation Imaging

by Hossein J. Sharahi, Christopher N. Acconcia, Matthew Li, Anne Martel and Kullervo Hynynen

Sensors 2023, 23(21), 8760; https://doi.org/10.3390/s23218760 - 27 Oct 2023

Cited by 1 | Viewed by 1048

Abstract

Convolutional neural networks (CNNs), initially developed for image processing applications, have recently received significant attention within the field of medical ultrasound imaging. In this study, passive cavitation imaging/mapping (PCI/PAM), which is used to map cavitation sources based on the correlation of signals across an array of receivers, is evaluated. Traditional reconstruction techniques in PCI, such as delay-and-sum, yield high spatial resolution at the cost of a substantial computational time. This results from the resource-intensive process of determining sensor weights for individual pixels in these methodologies. Consequently, the use of conventional algorithms for image reconstruction does not meet the speed requirements that are essential for real-time monitoring. Here, we show that a three-dimensional (3D) convolutional network can learn the image reconstruction algorithm for a

16 \times 16

element matrix probe with a receive frequency ranging from 256 kHz up to

1.0

MHz. The network was trained and evaluated using simulated data representing point sources, resulting in the successful reconstruction of volumetric images with high sensitivity, especially for single isolated sources (100% in the test set). As the number of simultaneous sources increased, the network’s ability to detect weaker intensity sources diminished, although it always correctly identified the main lobe. Notably, however, network inference was remarkably fast, completing the task in approximately 178 s for a dataset comprising 650 frames of

41^{3}

volume images with signal duration of

20 μ s

. This processing speed is roughly thirty times faster than a parallelized implementation of the traditional time exposure acoustics algorithm on the same GPU device. This would open a new door for PCI application in the real-time monitoring of ultrasound ablation. Full article

(This article belongs to the Special Issue Deep Learning for Sensor-Driven Medical Applications)

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13 pages, 11090 KiB

Open AccessArticle

Mud Ring Optimization Algorithm with Deep Learning Model for Disease Diagnosis on ECG Monitoring System

by Ala Saleh Alluhaidan, Mashael Maashi, Munya A. Arasi, Ahmed S. Salama, Mohammed Assiri and Amani A. Alneil

Sensors 2023, 23(15), 6675; https://doi.org/10.3390/s23156675 - 26 Jul 2023

Cited by 1 | Viewed by 985

Abstract

Due to the tremendous growth of the Internet of Things (IoT), sensing technologies, and wearables, the quality of medical services has been enhanced, and it has shifted from standard medical-based health services to real time. Commonly, the sensors can be combined as numerous clinical devices to store the biosignals generated by the physiological actions of the human body. Meanwhile, a familiar method with a noninvasive and rapid biomedical electrocardiogram (ECG) signal can be used to diagnose and examine cardiovascular disease (CVD). As the growing number of patients is destroying the classification outcome because of major changes in the ECG signal patterns among numerous patients, computer-assisted automatic diagnostic tools are needed for ECG signal classification. Therefore, this study presents a mud ring optimization technique with a deep learning-based ECG signal classification (MROA-DLECGSC) technique. The presented MROA-DLECGSC approach recognizes the presence of heart disease using ECG signals. To accomplish this, the MROA-DLECGSC technique initially preprocessed the ECG signals to transform them into a uniform format. In addition, the Stacked Autoencoder Topographic Map (SAETM) approach was utilized for the classification of ECG signals to identify the presence of CVDs. Finally, the MROA was applied as a hyperparameter optimizer, which assisted in accomplishing enhanced performance. The experimental outcomes of the MROA-DLECGSC algorithm were tested on the benchmark database, and the results show the better performance of the MROA-DLECGSC methodology compared to other recent algorithms. Full article

(This article belongs to the Special Issue Deep Learning for Sensor-Driven Medical Applications)

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