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Artificial Intelligence and Beyond in Medical and Healthcare Engineering: Volume...
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Artificial Intelligence and Beyond in Medical and Healthcare Engineering: Volume II

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

Deadline for manuscript submissions: closed (10 June 2023) | Viewed by 10025

Special Issue Editors

Dr. Syoji Kobashi

E-Mail Website
Guest Editor
Graduate School of Engineering, University of Hyogo 2167, Shosha, Himeji 671-2280, Japan
Interests: medical image analysis; artificial intelligence in medicine
Special Issues, Collections and Topics in MDPI journals
Dr. Naomi Yagi

E-Mail Website
Guest Editor
Advanced Medical Engineering Research Institute, University of Hyogo, Education and training Bld. 3F, Harima-Himeji General Medical Center, 3-264 Kamiya, Himeji 670-0836, Japan
Interests: medical engineering; medical informatics

Special Issue Information

Dear Colleagues,

Quantitative radiology (QR), when brought into routine clinical practice, will bring about a significant enhancement of the role of radiology in the medical milieu, potentially spawning numerous new advances in medicine. Currently, more and more medical images (MRI, CT, Ultrasound, PETCT, OCT, etc.) are being collected and analyzed for body-region-wide or bodywide disease quantification in patients with cancer and/or disease states, in addition to during clinical tasks related with medical images, including screening, detection/diagnosis, staging, prognosis assessment, treatment planning, treatment prediction assessment, treatment response assessment, and restaging/surveillance. New algorithms for medical image processing will serve as an engineer for the above clinical tasks.

In this Special Issue, we will focus on the vast range of new algorithms for medical image processing, analysis, and quantification. Machine learning, especially deep learning, has recently been widely investigated, and its power has been demonstrated in medical image segmentation, registration, classification, response prediction, etc. For this Special Issue, we welcome manuscripts describing the use of unsupervised or supervised learning based on statistical and mathematical models for all the above clinical tasks. Other topics of interest include but are not limited to new algorithms for medical image segmentation, registration, disease response prediction, classification, image quality enhancement, image construction, and new systems in computer-aided diagnosis, perception, image-guided procedures, biomedical applications, informatics, radiology, and digital pathology.

Dr. Syoji Kobashi
Dr. Naomi Yagi
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. 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 intelligence
  • deep learning
  • statistical model
  • medical image processing
  • prediction
  • personalized medicine
  • digital health
  • patient satisfaction
  • computer-aided systems
  • deep medicine

Published Papers (4 papers)

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Research

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16 pages, 3233 KiB  
Article
2D Winograd CNN Chip for COVID-19 and Pneumonia Detection
by Yu-Cheng Fan, Kun-Yao Lin and Yen-Hsun Tsai
Appl. Sci. 2022, 12(24), 12891; https://doi.org/10.3390/app122412891 - 15 Dec 2022
Cited by 1 | Viewed by 1268
Abstract
In this paper, a two-dimensional Winograd CNN (Convolutional Neural Network) chip for COVID-19 and pneumonia detection is proposed. In light of the COVID-19 pandemic, many studies have led to a dramatic increase in the effects of the virus on the lungs. Some studies [...] Read more.
In this paper, a two-dimensional Winograd CNN (Convolutional Neural Network) chip for COVID-19 and pneumonia detection is proposed. In light of the COVID-19 pandemic, many studies have led to a dramatic increase in the effects of the virus on the lungs. Some studies have also pointed out that the clinical application of deep learning in the medical field is also increasing, and it is also pointed out that the radiation impact of CT exposure is more serious than that of X-ray films and that CT exposure is not suitable for viral pneumonia. This study will analyze the results of X-rays trained using CNN architecture and convolutional using Winograd. This research will also set up a popular model architecture to realize four kinds of grayscale image prediction to verify the actual prediction effect on this data. The experimental data is mainly composed of chest X-rays of four different types of grayscales as input material. Among them, the research method of this experiment is to design the basic CNN operation structure of the chip and apply the Winograd calculus method to the convolutional operation. Finally, according to the TSMC 0.18 μm process, the actual chip is produced, and each step is verified to ensure the correctness of the circuit. The experimental results prove that the accuracy of our proposed method reaches 87.87%, and the precision reaches 88.48%. This proves that our proposed method has an excellent recognition rate. Full article
(This article belongs to the Special Issue Artificial Intelligence and Beyond in Medical and Healthcare Engineering: Volume II)
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Review

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28 pages, 843 KiB  
Review
Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
by Zahra Solatidehkordi and Imran Zualkernan
Appl. Sci. 2022, 12(23), 12094; https://doi.org/10.3390/app122312094 - 25 Nov 2022
Cited by 7 | Viewed by 2653
Abstract
Training machine learning and deep learning models for medical image classification is a challenging task due to a lack of large, high-quality labeled datasets. As the labeling of medical images requires considerable time and effort from medical experts, models need to be specifically [...] Read more.
Training machine learning and deep learning models for medical image classification is a challenging task due to a lack of large, high-quality labeled datasets. As the labeling of medical images requires considerable time and effort from medical experts, models need to be specifically designed to train on low amounts of labeled data. Therefore, an application of semi-supervised learning (SSL) methods provides one potential solution. SSL methods use a combination of a small number of labeled datasets with a much larger number of unlabeled datasets to achieve successful predictions by leveraging the information gained through unsupervised learning to improve the supervised model. This paper provides a comprehensive survey of the latest SSL methods proposed for medical image classification tasks. Full article
(This article belongs to the Special Issue Artificial Intelligence and Beyond in Medical and Healthcare Engineering: Volume II)
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25 pages, 1173 KiB  
Review
Artificial Intelligence, Sensors and Vital Health Signs: A Review
by Sahalu Balarabe Junaid, Abdullahi Abubakar Imam, Aliyu Nuhu Shuaibu, Shuib Basri, Ganesh Kumar, Yusuf Alhaji Surakat, Abdullateef Oluwagbemiga Balogun, Muhammad Abdulkarim, Aliyu Garba, Yusra Sahalu, Abdullahi Mohammed, Yahaya Tanko Mohammed, Bashir Abubakar Abdulkadir, Abdullah Alkali Abba, Nana Aliyu Iliyasu Kakumi and Ammar Kareem Alazzawi
Appl. Sci. 2022, 12(22), 11475; https://doi.org/10.3390/app122211475 - 11 Nov 2022
Cited by 3 | Viewed by 3620
Abstract
Large amounts of patient vital/physiological signs data are usually acquired in hospitals manually via centralized smart devices. The vital signs data are occasionally stored in spreadsheets and may not be part of the clinical cloud record; thus, it is very challenging for doctors [...] Read more.
Large amounts of patient vital/physiological signs data are usually acquired in hospitals manually via centralized smart devices. The vital signs data are occasionally stored in spreadsheets and may not be part of the clinical cloud record; thus, it is very challenging for doctors to integrate and analyze the data. One possible remedy to overcome these limitations is the interconnection of medical devices through the internet using an intelligent and distributed platform such as the Internet of Things (IoT) or the Internet of Health Things (IoHT) and Artificial Intelligence/Machine Learning (AI/ML). These concepts permit the integration of data from different sources to enhance the diagnosis/prognosis of the patient’s health state. Over the last several decades, the growth of information technology (IT), such as the IoT/IoHT and AI, has grown quickly as a new study topic in many academic and business disciplines, notably in healthcare. Recent advancements in healthcare delivery have allowed more people to have access to high-quality care and improve their overall health. This research reports recent advances in AI and IoT in monitoring vital health signs. It investigates current research on AI and the IoT, as well as key enabling technologies, notably AI and sensors-enabled applications and successful deployments. This study also examines the essential issues that are frequently faced in AI and IoT-assisted vital health signs monitoring, as well as the special concerns that must be addressed to enhance these systems in healthcare, and it proposes potential future research directions. Full article
(This article belongs to the Special Issue Artificial Intelligence and Beyond in Medical and Healthcare Engineering: Volume II)
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Other

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8 pages, 786 KiB  
Brief Report
Workout Detection by Wearable Device Data Using Machine Learning
by Yutaka Yoshida and Emi Yuda
Appl. Sci. 2023, 13(7), 4280; https://doi.org/10.3390/app13074280 - 28 Mar 2023
Cited by 4 | Viewed by 1598
Abstract
There are many reports that workouts relieve daily stress and are effective in improving mental and physical health. In recent years, there has been a demand for quick and easy methods to analyze and evaluate living organisms using biological information measured from wearable [...] Read more.
There are many reports that workouts relieve daily stress and are effective in improving mental and physical health. In recent years, there has been a demand for quick and easy methods to analyze and evaluate living organisms using biological information measured from wearable sensors. In this study, we attempted workout detection for one healthy female (40 years old) based on multiple types of biological information, such as the number of steps taken, activity level, and pulse, obtained from a wristband-type wearable sensor using machine learning. Data were recorded intermittently for approximately 64 days and 57 workouts were recorded. Workouts adopted for exercise were yoga and the workout duration was 1 h. We extracted 3416 min of biometric information for each of three categories: workout, awake activities (activities other than workouts), and sleep. Classification was performed using random forest (RF), SVM, and KNN. The detection accuracy of RF and SVM was high, and the recall, precision, and F-score values when using RF were 0.962, 0.963, and 0.963, respectively. The values for SVM were 0.961, 0.962, and 0.962, respectively. In addition, as a result of calculating the importance of the feature values used for detection, sleep state (39.8%), skin temperature (33.3%), and pulse rate (13.2%) accounted for approximately 86.3% of the total. By applying RF or SVM to the biological information obtained from the wearable wristband sensor, workouts could be detected every minute with high accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence and Beyond in Medical and Healthcare Engineering: Volume II)
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