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Electronics
Special Issues
Technological Advancements and Cutting-Edge Innovations for Smart and Sustainable Healthcare
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Technological Advancements and Cutting-Edge Innovations for Smart and Sustainable Healthcare

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 11766

Special Issue Editors

Dr. Rahul Paul

E-Mail Website
Guest Editor
1. Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
2. U.S. Food and Drug Administration, Silver Spring, MD 20993, USA
Interests: machine learning; deep learning; radiomics; healthcare; oncology
Prof. Dr. BM Zeeshan Hameed

E-Mail Website
Guest Editor
1. Department of Urology, Father Muller Medical College, Mangalore, Karnataka, India
2. Chairman of Youth Section of Urological Society of India, Mangalore, Karnataka, India
3. European Association of Urology – Young Academic Urologists (EAU-YAU) Urolithiasis and Endourology Working Group, Mangalore, Karnataka, India
4. Executive Committee Member, International Training and Research in Uro-Oncology and Endourology, Mangalore 576104, Karnataka, India
5. Chief Executive Officer (CEO), Curiouz TechLab Private Limited, Manipal Government of Karnataka Bio incubator, MAHE Advanced Research Centre Manipal, Mangalore 576104, Karnataka, India
Interests: urology; endourology; urooncology; robotic surgery
Nithesh Naik

E-Mail Website
Guest Editor
1. Curiouz TechLab Private Limited, Manipal Government of Karnataka Bio incubator, MAHE Advanced Research Centre Manipal, Karnataka 576104, India
2. Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
Interests: materials; polymer composites; nanocomposites; nanomaterials; bio-based composites;3D printing
Special Issues, Collections and Topics in MDPI journals
Dr. Yashbir Singh

E-Mail Website
Guest Editor
Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
Interests: biomedical engineering; medical informatics; deep learning; cardiology; liver cancer

Special Issue Information

Dear Colleagues,

Biomedical informatics relies on the development of innovative methods and procedures that can be used in various settings. Investigations in both basic and applied engineering are aimed at enhancing the delivery of healthcare services. Researchers, engineers and consultants in healthcare engineering from around the world can share cutting-edge knowledge, new technology and innovative ideas. We welcome submissions that address real-world clinical problems, develop a novel approach to solving it, and assess the appropriateness of the approach in relation to current state-of-the-art (SoA) methodologies. For healthcare professionals, participation in the motivation and evaluation of results is expected.

Topics of interest include (but are not limited to) clinical decision support, patient safety, artificial intelligence and machine learning, knowledge representation for healthcare, and clinical informatics. Papers discussing wearables, healthcare innovations, statistics and quality of medical data, security and privacy will also be considered. Submissions must focus on novel informatics methods and their comparison to the current approaches.

Dr. Rahul Paul
Prof. Dr. BM Zeeshan Hameed
Dr. Nithesh Naik
Dr. Yashbir Singh
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. Electronics 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

  • biomedical engineering (devices, equipment, procedures, and software)
  • intelligent medical devices and sensors
  • healthcare software architecture, framework, design, and engineering
  • healthcare decision analytics and support systems
  • artificial intelligence, machine learning and deep learning
  • informatics and telematics
  • medical monitoring devices and wearable health technology
  • healthcare facilities and infrastructure
  • improvement of healthcare delivery systems
  • healthcare innovations
  • security and privacy in healthcare
  • statistics and quality of medical data
  • health systems modeling and simulation
  • computer-aided diagnosis

Published Papers (3 papers)

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Research

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19 pages, 11730 KiB  
Article
A Method Noise-Based Convolutional Neural Network Technique for CT Image Denoising
by Prabhishek Singh, Manoj Diwakar, Reena Gupta, Sarvesh Kumar, Alakananda Chakraborty, Eshan Bajal, Muskan Jindal, Dasharathraj K. Shetty, Jayant Sharma, Harshit Dayal, Nithesh Naik and Rahul Paul
Electronics 2022, 11(21), 3535; https://doi.org/10.3390/electronics11213535 - 29 Oct 2022
Cited by 13 | Viewed by 2980
Abstract
Medical imaging is a complex process that capitulates images created by X-rays, ultrasound imaging, angiography, etc. During the imaging process, it also captures image noise during image acquisition, some of which are extremely corrosive, creating a disturbance that results in image degradation. The [...] Read more.
Medical imaging is a complex process that capitulates images created by X-rays, ultrasound imaging, angiography, etc. During the imaging process, it also captures image noise during image acquisition, some of which are extremely corrosive, creating a disturbance that results in image degradation. The proposed work addresses the challenge to eliminate the corrosive Gaussian additive white noise from computed tomography (CT) images while preserving the fine details. The proposed approach is synthesized by amalgamating the concept of method noise with a deep learning-based framework of a convolutional neural network (CNN). The corrupted images are obtained by explicit addition of Gaussian additive white noise at multiple noise variance levels (σ = 10, 15, 20, 25). The denoised images obtained are then evaluated according to their visual quality and quantitative metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). These metrics for denoised CT images are then compared with their respective values for the reference CT image. The average PSNR value of the proposed method is 25.82, the average SSIM value is 0.85, and the average computational time is 2.8760. To better understand the proposed approach’s effectiveness, an intensity profile of denoised and original medical images is plotted and compared. To further test the performance of the proposed methodology, the results obtained are also compared with that of other non-traditional methods. The critical analysis of the results shows the commendable efficiency of the proposed methodology in denoising the medical CT images corrupted by Gaussian noise. This approach can be utilized in multiple pragmatic areas of application in the field of medical image processing. Full article
(This article belongs to the Special Issue Technological Advancements and Cutting-Edge Innovations for Smart and Sustainable Healthcare)
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17 pages, 4462 KiB  
Article
Low-Dose COVID-19 CT Image Denoising Using Batch Normalization and Convolution Neural Network
by Manoj Diwakar, Prabhishek Singh, Girija Rani Karetla, Preeti Narooka, Arvind Yadav, Rajesh Kumar Maurya, Reena Gupta, José Luis Arias-Gonzáles, Mukund Pratap Singh, Dasharathraj K. Shetty, Rahul Paul and Nithesh Naik
Electronics 2022, 11(20), 3375; https://doi.org/10.3390/electronics11203375 - 19 Oct 2022
Cited by 7 | Viewed by 2152
Abstract
Computed tomography (CT) is used in medical applications to produce digital medical imaging of the human body and is acquired by the reconstruction process, where X-rays are the key component of CT imaging. The present coronavirus outbreak has spawned new medical device and [...] Read more.
Computed tomography (CT) is used in medical applications to produce digital medical imaging of the human body and is acquired by the reconstruction process, where X-rays are the key component of CT imaging. The present coronavirus outbreak has spawned new medical device and technology research fields. COVID-19 most severely affects people with poor immunity; children and pregnant women are more susceptible. A CT scan will be required to assess the infection’s severity. As a result, to reduce the radiation levels significantly there is a need to minimize the CT scan noise. The quality of CT images may degrade in the form of noisy images due to low radiation levels. Hence, this study proposes a novel denoising methodology for COVID-19 CT images with a low dose, where a convolution neural network (CNN) and batch normalization were utilized for denoising. From different output metrics such as peak signal-to-noise ratio (PSNR) and image quality index (IQI), the accuracy of the resulting CT images was checked and evaluated, where IQI obtained the best results in terms of 99% accuracy. The findings were also compared with the outcomes of related recent research in the domain. After a detailed review of the findings, it was noted that the proposed algorithm in the present study performed better in comparision to the existing literature. Full article
(This article belongs to the Special Issue Technological Advancements and Cutting-Edge Innovations for Smart and Sustainable Healthcare)
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Review

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50 pages, 4870 KiB  
Review
A Comprehensive Review on AI-Enabled Models for Parkinson’s Disease Diagnosis
by Shriniket Dixit, Khitij Bohre, Yashbir Singh, Yassine Himeur, Wathiq Mansoor, Shadi Atalla and Kathiravan Srinivasan
Electronics 2023, 12(4), 783; https://doi.org/10.3390/electronics12040783 - 04 Feb 2023
Cited by 10 | Viewed by 5476
Abstract
Parkinson’s disease (PD) is a devastating neurological disease that cannot be identified with traditional plasma experiments, necessitating the development of a faster, less expensive diagnostic instrument. Due to the difficulty of quantifying PD in the past, doctors have tended to focus on some [...] Read more.
Parkinson’s disease (PD) is a devastating neurological disease that cannot be identified with traditional plasma experiments, necessitating the development of a faster, less expensive diagnostic instrument. Due to the difficulty of quantifying PD in the past, doctors have tended to focus on some signs while ignoring others, primarily relying on an intuitive assessment scale because of the disease’s characteristics, which include loss of motor control and speech that can be utilized to detect and diagnose this disease. It is an illness that impacts both motion and non-motion functions. It takes years to develop and has a wide range of clinical symptoms and prognoses. Parkinson’s patients commonly display non-motor symptoms such as sleep problems, neurocognitive ailments, and cognitive impairment long before the diagnosis, even though scientists have been working to develop designs for diagnosing and categorizing the disease, only noticeable defects such as movement patterns, speech, or writing skills are offered in this paper. This article provides a thorough analysis of several AI-based ML and DL techniques used to diagnose PD and their influence on developing additional research directions. It follows the guidelines of Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This review also examines the current state of PD diagnosis and the potential applications of data-driven AI technology. It ends with a discussion of future developments, which aids in filling critical gaps in the current Parkinson’s study. Full article
(This article belongs to the Special Issue Technological Advancements and Cutting-Edge Innovations for Smart and Sustainable Healthcare)
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