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Intelligent Diagnosis and Decision Support in Medical Applications

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 9393

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Special Issue Editors

Prof. Dr. Voula Georgopoulos

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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

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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

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Keywords

artificial intelligence in diagnosis/differential diagnosis
clinical decision support
machine learning
complex data analysis
soft computing

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Published Papers (6 papers)

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Research

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22 pages, 5478 KiB

Open AccessArticle

Staining-Independent Malaria Parasite Detection and Life Stage Classification in Blood Smear Images

by Tong Xu, Nipon Theera-Umpon and Sansanee Auephanwiriyakul

Appl. Sci. 2024, 14(18), 8402; https://doi.org/10.3390/app14188402 - 18 Sep 2024

Viewed by 568

Abstract

Malaria is a leading cause of morbidity and mortality in tropical and sub-tropical regions. This research proposed a malaria diagnosis system based on the you only look once algorithm for malaria parasite detection and the convolutional neural network algorithm for malaria parasite life stage classification. Two public datasets are utilized: MBB and MP-IDB. The MBB dataset includes human blood smears infected with Plasmodium vivax (P. vivax). While the MP-IDB dataset comprises 4 species of malaria parasites: P. vivax, P. ovale, P. malariae, and P. falciparum. Four distinct stages of life exist in every species, including ring, trophozoite, schizont, and gametocyte. For the MBB dataset, detection and classification accuracies of 0.92 and 0.93, respectively, were achieved. For the MP-IDB dataset, the proposed algorithms yielded the accuracies for detection and classification as follows: 0.84 and 0.94 for P. vivax; 0.82 and 0.93 for P. ovale; 0.79 and 0.93 for P. malariae; and 0.92 and 0.96 for P. falciparum. The detection results showed the models trained by P. vivax alone provide good detection capabilities also for other species of malaria parasites. The classification performance showed the proposed algorithms yielded good malaria parasite life stage classification performance. The future directions include collecting more data and exploring more sophisticated algorithms. Full article

(This article belongs to the Special Issue Intelligent Diagnosis and Decision Support in Medical Applications)

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10 pages, 2512 KiB

Open AccessArticle

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 1068

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 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

Open AccessArticle

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 936

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, 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

Open AccessArticle

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 1058

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 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

Open AccessArticle

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

Cited by 2 | Viewed by 1634

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 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

Open AccessReview

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 - 6 Apr 2023

Cited by 4 | Viewed by 3396

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 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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