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Bioengineering
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New Scenes of Artificial Intelligence in Medical Research: Latest Information...
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New Scenes of Artificial Intelligence in Medical Research: Latest Information and Future Directions

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Regenerative Engineering".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 9958

Special Issue Editor

Prof. Kaoru Sakatani

E-Mail Website
Guest Editor
Department of Human and Engineered Environmental Studies, Universal Sports Health Science Laboratory, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwanoha, Kashiwa-shi, Chiba, Japan
Interests: feedforward neural nets; infrared spectra; learning (artificial intelligence); medical computing; time-resolved spectra; EEG; MRI

Special Issue Information

Dear Colleagues，

In recent years, artificial intelligence has been rapidly applied in various medical fields, and its usefulness in diagnosis, treatment, and prevention has been demonstrated.

This Special Issue, "New Scenes of Artificial Intelligence in Medical Research: New Features and Future Directions," covers new applications of AI to a wide range of medical fields including medical devices and telemedicine and disease prediction using PHR/health care data or sensor data.

Currently, with the aging of the population, geriatric diseases based on lifestyle-related diseases such as dementia and malignant tumors are becoming a social problem worldwide. Therefore, we are interested in publishing research addressing this problem with the use of AI technologies towards the early detection and prevention of these geriatric diseases.

The scope also covers Traditional Chinese Medicine and other complementary and alternative medicine as new areas of application of AI.

Prof. Kaoru Sakatani
Guest Editor

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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • AI
  • alternative medicine
  • malignant tumors
  • dementia

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Published Papers (5 papers)

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Research

21 pages, 1366 KiB  
Article
A New Fuzzy-Based Classification Method for Use in Smart/Precision Medicine
by Elena Zaitseva, Vitaly Levashenko, Jan Rabcan and Miroslav Kvassay
Bioengineering 2023, 10(7), 838; https://doi.org/10.3390/bioengineering10070838 - 15 Jul 2023
Cited by 10 | Viewed by 1666
Abstract
The development of information technology has had a significant impact on various areas of human activity, including medicine. It has led to the emergence of the phenomenon of Industry 4.0, which, in turn, led to the development of the concept of Medicine 4.0. [...] Read more.
The development of information technology has had a significant impact on various areas of human activity, including medicine. It has led to the emergence of the phenomenon of Industry 4.0, which, in turn, led to the development of the concept of Medicine 4.0. Medicine 4.0, or smart medicine, can be considered as a structural association of such areas as AI-based medicine, telemedicine, and precision medicine. Each of these areas has its own characteristic data, along with the specifics of their processing and analysis. Nevertheless, at present, all these types of data must be processed simultaneously, in order to provide the most complete picture of the health of each individual patient. In this paper, after a brief analysis of the topic of medical data, a new classification method is proposed that allows the processing of the maximum number of data types. The specificity of this method is its use of a fuzzy classifier. The effectiveness of this method is confirmed by an analysis of the results from the classification of various types of data for medical applications and health problems. In this paper, as an illustration of the proposed method, a fuzzy decision tree has been used as the fuzzy classifier. The accuracy of the classification in terms of the proposed method, based on a fuzzy classifier, gives the best performance in comparison with crisp classifiers. Full article
(This article belongs to the Special Issue New Scenes of Artificial Intelligence in Medical Research: Latest Information and Future Directions)
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16 pages, 4588 KiB  
Article
BU-DLNet: Breast Ultrasonography-Based Cancer Detection Using Deep-Learning Network Selection and Feature Optimization
by Amad Zafar, Jawad Tanveer, Muhammad Umair Ali and Seung Won Lee
Bioengineering 2023, 10(7), 825; https://doi.org/10.3390/bioengineering10070825 - 11 Jul 2023
Cited by 1 | Viewed by 1699
Abstract
Early detection of breast lesions and distinguishing between malignant and benign lesions are critical for breast cancer (BC) prognosis. Breast ultrasonography (BU) is an important radiological imaging modality for the diagnosis of BC. This study proposes a BU image-based framework for the diagnosis [...] Read more.
Early detection of breast lesions and distinguishing between malignant and benign lesions are critical for breast cancer (BC) prognosis. Breast ultrasonography (BU) is an important radiological imaging modality for the diagnosis of BC. This study proposes a BU image-based framework for the diagnosis of BC in women. Various pre-trained networks are used to extract the deep features of the BU images. Ten wrapper-based optimization algorithms, including the marine predator algorithm, generalized normal distribution optimization, slime mold algorithm, equilibrium optimizer (EO), manta-ray foraging optimization, atom search optimization, Harris hawks optimization, Henry gas solubility optimization, path finder algorithm, and poor and rich optimization, were employed to compute the optimal subset of deep features using a support vector machine classifier. Furthermore, a network selection algorithm was employed to determine the best pre-trained network. An online BU dataset was used to test the proposed framework. After comprehensive testing and analysis, it was found that the EO algorithm produced the highest classification rate for each pre-trained model. It produced the highest classification accuracy of 96.79%, and it was trained using only a deep feature vector with a size of 562 in the ResNet-50 model. Similarly, the Inception-ResNet-v2 had the second highest classification accuracy of 96.15% using the EO algorithm. Moreover, the results of the proposed framework are compared with those in the literature. Full article
(This article belongs to the Special Issue New Scenes of Artificial Intelligence in Medical Research: Latest Information and Future Directions)
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13 pages, 2429 KiB  
Article
Toward Evaluating Critical Factors of Extubation Outcome with XCSR-Generated Rules
by Po-Hsun Huang, Lian-Yu Chen, Wei-Chan Chung, Chau-Chyun Sheu, Tzu-Chien Hsiao and Jong-Rung Tsai
Bioengineering 2022, 9(11), 701; https://doi.org/10.3390/bioengineering9110701 - 17 Nov 2022
Cited by 2 | Viewed by 2071
Abstract
Predicting the correct timing for extubation is pivotal for critically ill patients with mechanical ventilation support. Evidence suggests that extubation failure occurs in approximately 15–20% of patients, despite their passing of the extubation evaluation, necessitating reintubation. For critically ill patients, reintubation invariably increases [...] Read more.
Predicting the correct timing for extubation is pivotal for critically ill patients with mechanical ventilation support. Evidence suggests that extubation failure occurs in approximately 15–20% of patients, despite their passing of the extubation evaluation, necessitating reintubation. For critically ill patients, reintubation invariably increases mortality risk and medical costs. The numerous parameters that have been proposed for extubation decision-making, which constitute the key predictors of successful extubation, remains unclear. In this study, an extended classifier system capable of processing real-value inputs was proposed to select features of successful extubation. In total, 40 features linked to clinical information and variables acquired during spontaneous breathing trial (SBT) were used as the environmental inputs. According to the number of “don’t care” rules in a population set, Probusage, the probability of the feature not being classified as above rules, can be calculated. A total of 228 subjects’ results showed that Probusage was higher than 90% for minute ventilation at the 1st, 30th, 60th, and 90th minutes; respiratory rate at the 90th minute; and body weight, indicating that the variance in respiratory parameters during an SBT are critical predictors of successful extubation. The present XCSR model is useful to evaluate critical factors of extubation outcomes. Additionally, the current findings suggest that SBT duration should exceed 90 min, and that clinicians should consider the variance in respiratory variables during an SBT before making extubation decisions. Full article
(This article belongs to the Special Issue New Scenes of Artificial Intelligence in Medical Research: Latest Information and Future Directions)
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18 pages, 2615 KiB  
Article
Estimation of Caenorhabditis Elegans Lifespan Stages Using a Dual-Path Network Combining Biomarkers and Physiological Changes
by Yao Song, Jun Liu, Yanhao Yin and Jinshan Tang
Bioengineering 2022, 9(11), 689; https://doi.org/10.3390/bioengineering9110689 - 14 Nov 2022
Cited by 2 | Viewed by 1848
Abstract
Assessing individual aging has always been an important topic in aging research. Caenorhabditis elegans (C. elegans) has a short lifespan and is a popular model organism widely utilized in aging research. Studying the differences in C. elegans life stages is of [...] Read more.
Assessing individual aging has always been an important topic in aging research. Caenorhabditis elegans (C. elegans) has a short lifespan and is a popular model organism widely utilized in aging research. Studying the differences in C. elegans life stages is of great significance for human health and aging. In order to study the differences in C. elegans lifespan stages, the classification of lifespan stages is the first task to be performed. In the past, biomarkers and physiological changes captured with imaging were commonly used to assess aging in isogenic C. elegans individuals. However, all of the current research has focused only on physiological changes or biomarkers for the assessment of aging, which affects the accuracy of assessment. In this paper, we combine two types of features for the assessment of lifespan stages to improve assessment accuracy. To fuse the two types of features, an improved high-efficiency network (Att-EfficientNet) is proposed. In the new EfficientNet, attention mechanisms are introduced so that accuracy can be further improved. In addition, in contrast to previous research, which divided the lifespan into three stages, we divide the lifespan into six stages. We compared the classification method with other CNN-based methods as well as other classic machine learning methods. The results indicate that the classification method has a higher accuracy rate (72%) than other CNN-based methods and some machine learning methods. Full article
(This article belongs to the Special Issue New Scenes of Artificial Intelligence in Medical Research: Latest Information and Future Directions)
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14 pages, 1431 KiB  
Article
Integrated Clinical Environment Security Analysis Using Reinforcement Learning
by Mariam Ibrahim and Ruba Elhafiz
Bioengineering 2022, 9(6), 253; https://doi.org/10.3390/bioengineering9060253 - 13 Jun 2022
Cited by 5 | Viewed by 1924
Abstract
Many communication standards have been proposed recently and more are being developed as a vision for dynamically composable and interoperable medical equipment. However, few have security systems that are sufficiently extensive or flexible to meet current and future safety requirements. This paper aims [...] Read more.
Many communication standards have been proposed recently and more are being developed as a vision for dynamically composable and interoperable medical equipment. However, few have security systems that are sufficiently extensive or flexible to meet current and future safety requirements. This paper aims to analyze the cybersecurity of the Integrated Clinical Environment (ICE) through the investigation of its attack graph and the application of artificial intelligence techniques that can efficiently demonstrate the subsystems’ vulnerabilities. Attack graphs are widely used for assessing network security. On the other hand, they are typically too huge and sophisticated for security administrators to comprehend and evaluate. Therefore, this paper presents a Q-learning-based attack graph analysis approach in which an attack graph that is generated for the Integrated Clinical Environment system resembles the environment, and the agent is assumed to be the attacker. Q-learning can aid in determining the best route that the attacker can take in order to damage the system as much as possible with the least number of actions. Numeric values will be assigned to the attack graph to better determine the most vulnerable part of the system and suggest this analysis to be further utilized for bigger graphs. Full article
(This article belongs to the Special Issue New Scenes of Artificial Intelligence in Medical Research: Latest Information and Future Directions)
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