Artificial Intelligence in Medicine and Healthcare

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

Deadline for manuscript submissions: 20 July 2024 | Viewed by 15894

Special Issue Editors


E-Mail Website
Guest Editor
Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield, UK
Interests: AI in medicine; healthcare informatics; computational intelligence; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
School of Control and Computer Engineering, North China Electric Power University, Beijing, China
Interests: AI for healthcare; medical Image processing; machine learning
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Psychiatry, University of Oxford, Oxford, UK
Interests: machine learning in medication and healthcare; data science and data mining; knowledge representation and discovery

E-Mail Website
Guest Editor
Computer Science Department, University of Huddersfield, Huddersfield, UK
Interests: big data and AI for healthcare and diabetes; health sensor monitoring and detection; medical data stream anomaly detection

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is increasingly applied in various disciplines, including the health and medicine sector, where AI has been leveraged to inform disease progression, make early diagnoses, optimize medication and treatment plans, and support decision making. In spite of considerable recent advances, AI for health and medicine is also facing major challenges, such as public trust building, patient privacy protection, ethical concerns on data usage and equity, and AI–human interaction/collaboration. While the patients and medical professionals still have much to benefit from rapidly developed AI techniques, this Special Issue encourages submissions of scientific findings that present the fundamental theory, techniques, applications, and practical experiences in the context of designing, implementing, or evaluating artificial intelligence for health and medicine.

The topics of this Special Issue include, but are not limited to:

  • computational intelligence for health and medicine;
  • artificial intelligence for health and medicine;
  • data mining and knowledge discovery in health and medicine;
  • machine learning in health and medicine;
  • clinical decision support systems for health and medicine;
  • text mining and natural language processing for health and medicine;
  • deep learning for health and medicine;
  • computer vision and medical imaging;
  • modelling and reasoning with time in healthcare systems.

Dr. Tianhua Chen
Dr. Pan Su
Dr. Zhenpeng Li
Dr. Bakhtiar Amen
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.

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

11 pages, 782 KiB  
Article
Correlation between Neck Muscle Endurance Tests, Ultrasonography, and Self-Reported Outcomes in Women with Low Cervical Disability and Neck Pain
by Pilar Pardos-Aguilella, Luis Ceballos-Laita, Sara Cabanillas-Barea, Silvia Pérez-Guillén, Gianluca Ciuffreda, Sandra Jiménez-del-Barrio and Andoni Carrasco-Uribarren
Appl. Sci. 2023, 13(18), 10106; https://doi.org/10.3390/app131810106 - 7 Sep 2023
Viewed by 1019
Abstract
Background: Neck pain (NP) is a frequent condition in women, characterized by exhibiting distinct clinical manifestations such as the presence of deep neck (DN) muscle weakness. Endurance and ultrasonography of the DN muscles, and patient-reported outcome measures, are commonly used outcomes in clinical [...] Read more.
Background: Neck pain (NP) is a frequent condition in women, characterized by exhibiting distinct clinical manifestations such as the presence of deep neck (DN) muscle weakness. Endurance and ultrasonography of the DN muscles, and patient-reported outcome measures, are commonly used outcomes in clinical practice. The aim of this study is to assess and correlate the endurance of the DN muscles and their morphological characteristics with pain intensity, neck disability and headache impact. Methods: An observational and correlational study was carried out. Eighty-two women were recruited, and endurance tests of neck flexor and extensor (chin tuck flexion test and neck extensor muscles endurance test), ultrasonography of the DN muscles, pain intensity, disability (neck disability index) and headache impact (HIT-6) were measured. Spearman’s rho was used to evaluate the correlation between the outcome variables, and a simple linear regression analysis was carried out to explain the model in detail. Results: Statistically significant negative correlations between the chin tuck neck flexion test and neck disability index (NDI) (r = −0.38; p < 0.001) and HIT-6 (r = −0.26; p = 0.02) were found. The neck extensor muscles endurance test showed a negative correlation with NDI (r = −0.27; p = 0.01) and HIT-6 (r = −0.26; p = 0.02). The simple linear regression analysis showed an R squared of 26.7% and was statistically significant (NDI: R squared = 0.267; F = 3.13; p = 0.004) for NDI. Conclusion: A negative correlation between deep neck muscle endurance test results and self-reported outcome measures in women with low cervical disability and neck pain were observed. This suggests that lower endurance in the deep neck muscles may be associated with poorer self-reported symptoms and functionality in these patients. The chin tuck neck flexion test and deep extensor muscles endurance test could predict self-perceived neck disability in women with low cervical disability and NP. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medicine and Healthcare)
Show Figures

Figure 1

18 pages, 3583 KiB  
Article
Review of Soft Computing Techniques in Monitoring Cardiovascular Disease in the Context of South Asian Countries
by Gajendra Singh Thakur, Sunil Kumar Sahu, N. Kumar Swamy, Manish Gupta, Tony Jan and Mukesh Prasad
Appl. Sci. 2023, 13(17), 9555; https://doi.org/10.3390/app13179555 - 23 Aug 2023
Viewed by 1686
Abstract
The term “soft computing” refers to a system that can work with varying degrees of uncertainty and approximations in real-life complex problems using various techniques such as Fuzzy Logic, Artificial Neural Networks (ANN), Machine Learning (ML), and Genetic Algorithms (GA). Owing to the [...] Read more.
The term “soft computing” refers to a system that can work with varying degrees of uncertainty and approximations in real-life complex problems using various techniques such as Fuzzy Logic, Artificial Neural Networks (ANN), Machine Learning (ML), and Genetic Algorithms (GA). Owing to the low-cost and high-performance digital processors today, the use of soft computing techniques has become more prevalent. The main focus of this paper is to study the use of soft computing in the prediction and diagnosis of heart diseases, which are considered one of the major causes of fatalities in modern-day humans. The heart is a major human organ that can be affected by various conditions such as high blood pressure, diabetes, and heart failure. The main cause of heart failure is the narrowing of the blood vessels due to excess cholesterol deposits in the coronary arteries. The objective of this study is to review and compare the various soft computing techniques that are used for the prediction, diagnosis, failure, detection, identification, and classification of heart disease. In this paper, a comprehensive list of recent soft computing techniques in heart condition monitoring is reviewed and compared with an experiment with specific applications to developing countries including South Asian countries. The relevant experimental outcomes demonstrate the benefits of soft computing in medical services with a high accuracy of 99.4% from Fuzzy Logic and Convolutional Neural Networks, with comparable results from other competing state-of-the-art soft computing models. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medicine and Healthcare)
Show Figures

Figure 1

9 pages, 602 KiB  
Article
A Computer Vision-Based Application for the Assessment of Head Posture: A Validation and Reliability Study
by Andoni Carrasco-Uribarren, Xavier Marimon, Flora Dantony, Sara Cabanillas-Barea, Alejandro Portela, Luis Ceballos-Laita and Albert Massip-Álvarez
Appl. Sci. 2023, 13(6), 3910; https://doi.org/10.3390/app13063910 - 19 Mar 2023
Cited by 1 | Viewed by 2534
Abstract
As its name implies, the forward head position (FHP) is when the head is further forward of the trunk than normal. This can cause neck and shoulder tension, as well as headaches. The craniovertebral angle (CVA) measured with 2D systems such as Kinovea [...] Read more.
As its name implies, the forward head position (FHP) is when the head is further forward of the trunk than normal. This can cause neck and shoulder tension, as well as headaches. The craniovertebral angle (CVA) measured with 2D systems such as Kinovea software is often used to assess the FHP. Computer vision applications have proven to be reliable in different areas of daily life. The aim of this study is to analyze the test-retest and inter-rater reliability and the concurrent validity of a smartphone application based on computer vision for the measurement of the CVA. Methods: The CVAs of fourteen healthy volunteers, fourteen neck pain patients, and fourteen tension-type headache patients were assessed. The assessment was carried out twice, with a week of rest between sessions. Each examiner took a lateral photo in a standing position with the smartphone app based on computer vision. The test-retest reliability was calculated with the assessment of the CVA measured by the smartphone application, and the inter-rater reliability was also calculated. A third examiner assessed the CVA using 2D Kinovea software to calculate its concurrent validity. Results: The CVA in healthy volunteers was 54.65 (7.00); in patients with neck pain, 57.67 (5.72); and in patients with tension-type headaches, 54.63 (6.48). The test-retest reliability was excellent, showing an Intraclass Correlation Coefficient (ICC) of 0.92 (0.86–0.95) for the whole sample. The inter-rater reliability was excellent, with an ICC of 0.91 (0.84–0.95) for the whole sample. The standard error of the measurement with the app was stated as 1.83°, and the minimum detectable change was stated as 5.07°. The concurrent validity was high: r = 0.94, p < 0.001. Conclusion: The computer-based smartphone app showed excellent test-retest and inter-rater reliability and strong concurrent validity compared to Kinovea software for the measurement of CVA. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medicine and Healthcare)
Show Figures

Figure 1

Review

Jump to: Research

25 pages, 1602 KiB  
Review
Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
by Qiao Xiao, Khuan Lee, Siti Aisah Mokhtar, Iskasymar Ismail, Ahmad Luqman bin Md Pauzi, Qiuxia Zhang and Poh Ying Lim
Appl. Sci. 2023, 13(8), 4964; https://doi.org/10.3390/app13084964 - 14 Apr 2023
Cited by 22 | Viewed by 8563
Abstract
Deep learning (DL) has been introduced in automatic heart-abnormality classification using ECG signals, while its application in practical medical procedures is limited. A systematic review is performed from perspectives of the ECG database, preprocessing, DL methodology, evaluation paradigm, performance metric, and code availability [...] Read more.
Deep learning (DL) has been introduced in automatic heart-abnormality classification using ECG signals, while its application in practical medical procedures is limited. A systematic review is performed from perspectives of the ECG database, preprocessing, DL methodology, evaluation paradigm, performance metric, and code availability to identify research trends, challenges, and opportunities for DL-based ECG arrhythmia classification. Specifically, 368 studies meeting the eligibility criteria are included. A total of 223 (61%) studies use MIT-BIH Arrhythmia Database to design DL models. A total of 138 (38%) studies considered removing noise or artifacts in ECG signals, and 102 (28%) studies performed data augmentation to extend the minority arrhythmia categories. Convolutional neural networks are the dominant models (58.7%, 216) used in the reviewed studies while growing studies have integrated multiple DL structures in recent years. A total of 319 (86.7%) and 38 (10.3%) studies explicitly mention their evaluation paradigms, i.e., intra- and inter-patient paradigms, respectively, where notable performance degradation is observed in the inter-patient paradigm. Compared to the overall accuracy, the average F1 score, sensitivity, and precision are significantly lower in the selected studies. To implement the DL-based ECG classification in real clinical scenarios, leveraging diverse ECG databases, designing advanced denoising and data augmentation techniques, integrating novel DL models, and deeper investigation in the inter-patient paradigm could be future research opportunities. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medicine and Healthcare)
Show Figures

Figure 1

Back to TopTop