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Applied Sciences
Special Issues
Intelligent Diagnosis and Decision Support in Medical Applications
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Intelligent Diagnosis and Decision Support in Medical Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 6239

Special Issue Editors

Prof. Dr. Voula Georgopoulos

E-Mail Website
Guest Editor
Professor at Dept. of Speech & Language Therapy, Dean of School of Health Rehabilitation Sciences, University of Patras, Patras, Greece
Interests: technology-based tools for intervention; artificial intelligence systems in differential diagnosis for speech and language pathology; augmentative alternative communication (AAC) technology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chrysostomos Stylios

E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, University of Ioannina, GR-47100 Arta, Greece
Interests: medical decision support systems; biomedical systems; advanced methods for diagnosis and decision support in medical applications; biosignal processing and analysis using advanced computational intelligence methods; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical decision support systems assist health professionals in diagnoses, intervention/treatment planning, outcome predictions, as well as the identification of potential risks. The interest in intelligent methods is to increase the efficiency and accuracy of medical decision making, leading to better patient outcomes, particularly in the era of big and complex data analysis. Machine learning and soft computing tools offer the ability to process large amounts of data, as well as incomplete or conflicting data, efficiently and accurately, which is particularly important in critical medical decision making.

This Special Issue will be dedicated to current trends in intelligent models for diagnosis/differential diagnosis and medical decision support.

The subjects to be discussed in this Special Issue will focus on the development, implementation and testing of intelligent medical decision support systems.

Prof. Dr. Voula Georgopoulos
Prof. Dr. Chrysostomos Stylios
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 in diagnosis/differential diagnosis
  • clinical decision support
  • machine learning
  • complex data analysis
  • soft computing

Published Papers (5 papers)

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Research

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10 pages, 2512 KiB  
Article
Dynamic Analysis of the Median Nerve in Carpal Tunnel Syndrome from Ultrasound Images Using the YOLOv5 Object Detection Model
by Shuya Tanaka, Atsuyuki Inui, Yutaka Mifune, Hanako Nishimoto, Issei Shinohara, Takahiro Furukawa, Tatsuo Kato, Masaya Kusunose, Yutaka Ehara, Shunsaku Takigami and Ryosuke Kuroda
Appl. Sci. 2023, 13(24), 13256; https://doi.org/10.3390/app132413256 - 14 Dec 2023
Cited by 1 | Viewed by 714
Abstract
Carpal tunnel syndrome (CTS) is caused by subsynovial connective tissue fibrosis, resulting in median nerve (MN) mobility. The standard evaluation method is the measurement of the MN cross-sectional area using static images, and dynamic images are not widely used. In recent years, remarkable [...] Read more.
Carpal tunnel syndrome (CTS) is caused by subsynovial connective tissue fibrosis, resulting in median nerve (MN) mobility. The standard evaluation method is the measurement of the MN cross-sectional area using static images, and dynamic images are not widely used. In recent years, remarkable progress has been made in the field of deep learning (DL) in medical image processing. The aim of the present study was to evaluate MN dynamics in CTS hands using the YOLOv5 model, which is one of the object detection models of DL. We included 20 normal hands (control group) and 20 CTS hands (CTS group). We obtained ultrasonographic short-axis images of the carpal tunnel and the MN and recorded MN motion during finger flexion–extension, and evaluated MN displacement and velocity. The YOLOv5 model showed a score of 0.953 for precision and 0.956 for recall. The radial–ulnar displacement of the MN was 3.56 mm in the control group and 2.04 mm in the CTS group, and the velocity of the MN was 4.22 mm/s in the control group and 3.14 mm/s in the CTS group. The scores were significantly reduced in the CTS group. This study demonstrates the potential of DL-based dynamic MN analysis as a powerful diagnostic tool for CTS. Full article
(This article belongs to the Special Issue Intelligent Diagnosis and Decision Support in Medical Applications)
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31 pages, 844 KiB  
Article
Intelligent Medical Diagnosis Reasoning Using Composite Fuzzy Relation, Aggregation Operators and Similarity Measure of q-Rung Orthopair Fuzzy Sets
by Anastasios Dounis and Angelos Stefopoulos
Appl. Sci. 2023, 13(23), 12553; https://doi.org/10.3390/app132312553 - 21 Nov 2023
Viewed by 618
Abstract
Medical diagnosis is the process of finding out what is the disease a person may be suffering from. From the symptoms and their gradation, the doctor can decide which the dominant disease is. Nevertheless, in the process of medical diagnosis, there is ambiguity, [...] Read more.
Medical diagnosis is the process of finding out what is the disease a person may be suffering from. From the symptoms and their gradation, the doctor can decide which the dominant disease is. Nevertheless, in the process of medical diagnosis, there is ambiguity, uncertainty, and a lack of medical knowledge that can adversely affect the doctor’s judgment. Thus, a tool of artificial intelligence, fuzzy logic, has come to enhance the decision-making of diagnosis in a medical environment. Fuzzy set theory uses the membership degree to characterize the uncertainty and, therefore, fuzzy sets are integrated into imperfect data in order to make a reliable diagnosis. The patient’s medical status is represented as q-rung orthopair fuzzy values. In this paper, many versions and methodologies were applied such as the composite fuzzy relation, fuzzy sets extensions (q-ROFS) with aggregation operators, and similarity measures, which were proposed as decision-making intelligent methods. The aim of this procedure was to find out which of the diseases (viral fever, malaria fever, typhoid fever, stomach problems, and chest problems), was the most influential for each patient. The work emphasizes the contribution of aggregation operators in medical data in order to contain more than one expert’s aspect. The performance of the methodology was quite good and interesting as most of the results were in agreement with previous works. Full article
(This article belongs to the Special Issue Intelligent Diagnosis and Decision Support in Medical Applications)
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12 pages, 2984 KiB  
Article
Factors Associated with Medial Elbow Torque Measured Using a Wearable Sensor in Junior High School Baseball Pitchers
by Tomoya Yoshikawa, Atsuyuki Inui, Yutaka Mifune, Hanako Nishimoto, Kohei Yamaura, Shintaro Mukohara, Issei Shinohara, Tatsuo Kato, Takahiro Furukawa, Masaya Kusunose, Shuya Tanaka, Yuichi Hoshino, Takehiko Matsushita and Ryosuke Kuroda
Appl. Sci. 2023, 13(19), 10573; https://doi.org/10.3390/app131910573 - 22 Sep 2023
Viewed by 710
Abstract
There are no reports investigating the relationship between shoulder range of motion (ROM) and pitching elbow torque in junior high school pitchers. Therefore, we aimed to evaluate the factors associated with medial elbow torque in this population. Sixty-three junior high school baseball pitchers [...] Read more.
There are no reports investigating the relationship between shoulder range of motion (ROM) and pitching elbow torque in junior high school pitchers. Therefore, we aimed to evaluate the factors associated with medial elbow torque in this population. Sixty-three junior high school baseball pitchers were recruited for this study. The participants completed a questionnaire and passive ROM measurements of shoulder abduction and horizontal adduction. All pitchers pitched three fastballs at maximum effort while wearing a wireless sensor recording pitching mechanics and elbow valgus torque for each pitch. Age (r = 0.65, p < 0.001), height (r = 0.83, p < 0.001), body weight (r = 0.82, p < 0.001), BMI (r = 0.60, p < 0.001), and ball velocity (r = 0.80, p < 0.001) were significantly positively correlated with elbow valgus torque. Participants were divided into two groups based on elbow valgus torque, >30 (high torque [HT]) and <30 N·m (low torque [LT]). Age, height, body weight, BMI, and ball velocity were significantly higher in the HT group than in the LT group. The difference between dominant and non-dominant shoulder horizontal adduction ROM was 5.3 ± 9.3° and 1.0 ± 6.4° in the HT and LT groups, respectively, which was also significantly different. Ball velocity, age, larger physique, and increased restriction of the dominant shoulder’s horizontal adduction ROM were associated with higher medial elbow torque in junior high school pitchers. This suggests that improving the dominant shoulder’s horizontal adduction ROM contributes to preventing elbow injuries. Full article
(This article belongs to the Special Issue Intelligent Diagnosis and Decision Support in Medical Applications)
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12 pages, 5404 KiB  
Article
Agile Machine Learning Model Development Using Data Canyons in Medicine: A Step towards Explainable Artificial Intelligence and Flexible Expert-Based Model Improvement
by Bojan Žlahtič, Jernej Završnik, Helena Blažun Vošner, Peter Kokol, David Šuran and Tadej Završnik
Appl. Sci. 2023, 13(14), 8329; https://doi.org/10.3390/app13148329 - 19 Jul 2023
Viewed by 1130
Abstract
Over the past few decades, machine learning has emerged as a valuable tool in the field of medicine, driven by the accumulation of vast amounts of medical data and the imperative to harness this data for the betterment of humanity. However, many of [...] Read more.
Over the past few decades, machine learning has emerged as a valuable tool in the field of medicine, driven by the accumulation of vast amounts of medical data and the imperative to harness this data for the betterment of humanity. However, many of the prevailing machine learning algorithms in use today are characterized as black-box models, lacking transparency in their decision-making processes and are often devoid of clear visualization capabilities. The transparency of these machine learning models impedes medical experts from effectively leveraging them due to the high-stakes nature of their decisions. Consequently, the need for explainable artificial intelligence (XAI) that aims to address the demand for transparency in the decision-making mechanisms of black-box algorithms has arisen. Alternatively, employing white-box algorithms can empower medical experts by allowing them to contribute their knowledge to the decision-making process and obtain a clear and transparent output. This approach offers an opportunity to personalize machine learning models through an agile process. A novel white-box machine learning algorithm known as Data canyons was employed as a transparent and robust foundation for the proposed solution. By providing medical experts with a web framework where their expertise is transferred to a machine learning model and enabling the utilization of this process in an agile manner, a symbiotic relationship is fostered between the domains of medical expertise and machine learning. The flexibility to manipulate the output machine learning model and visually validate it, even without expertise in machine learning, establishes a crucial link between these two expert domains. Full article
(This article belongs to the Special Issue Intelligent Diagnosis and Decision Support in Medical Applications)
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Review

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21 pages, 9741 KiB  
Review
Material Design in Implantable Biosensors toward Future Personalized Diagnostics and Treatments
by Faezeh Ghorbanizamani, Hichem Moulahoum, Emine Guler Celik and Suna Timur
Appl. Sci. 2023, 13(7), 4630; https://doi.org/10.3390/app13074630 - 06 Apr 2023
Cited by 2 | Viewed by 2519
Abstract
The growing demand for personalized treatments and the constant observation of vital signs for extended periods could positively solve the problematic concerns associated with the necessity for patient control and hospitalization. The impressive development in biosensing devices has led to the creation of [...] Read more.
The growing demand for personalized treatments and the constant observation of vital signs for extended periods could positively solve the problematic concerns associated with the necessity for patient control and hospitalization. The impressive development in biosensing devices has led to the creation of man-made implantable devices that are temporarily or permanently introduced into the human body, and thus, diminishing the pain and discomfort of the person. Despite all promising achievements in this field, there are some critical challenges to preserve reliable functionality in the complex environment of the human body over time. Biosensors in the in vivo environment are required to have specific features, including biocompatibility (minimal immune response or biofouling), biodegradability, reliability, high accuracy, and miniaturization (flexible, stretchable, lightweight, and ultra-thin). However, the performance of implantable biosensors is limited by body responses and insufficient power supplies (due to minimized batteries/electronics and data transmission without wires). In addition, the current processes and developments in the implantable biosensors field will open new routes in biomedicine and diagnostic systems that monitor occurrences happening inside the body in a certain period. This topical paper aims to give an overview of the state-of-the-art implantable biosensors and their design methods. It also discusses the latest developments in material science, including nanomaterials, hydrogel, hydrophilic, biomimetic, and other polymeric materials to overcome failures in implantable biosensors’ reliability. Lastly, we discuss the main challenges faced and future research prospects toward the development of dependable implantable biosensors. Full article
(This article belongs to the Special Issue Intelligent Diagnosis and Decision Support in Medical Applications)
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