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Bioengineering
Special Issues
New Insights into Machine Learning and Biomedicine: Updates and Directions
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New Insights into Machine Learning and Biomedicine: Updates and Directions

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 4964

Special Issue Editors

Dr. Cǎtǎlina Luca

E-Mail Website
Guest Editor
Faculty of Medical Bioengineering, University of Medicine and Pharmacy Grigore T. Popa, 700588 Iasi, Romania
Interests: medical engineering; biomedical instrumentation; biomedical device design; professional mentoring; medical device management
Dr. Calin Corciova

E-Mail Website
Guest Editor
Faculty of Medical Bioengineering, University of Medicine and Pharmacy Grigore T. Popa, 700588 Iasi, Romania
Interests: medical bioengineering; biomedical instrumentation; physiological measurements; assistive devices; health technology assessment; clinical decision support
Dr. Mario Petretta

E-Mail Website
Guest Editor
IRCCS SYNLAB SDN, Via Gianturco 113, 80121 Naples, Italy
Interests: coronary artery disease; risk stratification; survival analysis; machine learning; cardiac PET/CT; ECG

Special Issue Information

Dear Colleagues,

The Special Issue "New sight of machine learning and biomedicine: updates and directions" aims to provide an updated and comprehensive overview of the current state of the art in the field of machine learning and its applications to biomedicine. The scope of this Special Issue covers recent advances and directions in computational intelligence and machine learning techniques that have been applied to various biomedical problems such as disease diagnosis, treatment planning, drug design, and medical imaging analysis. The purpose of this Special Issue is first of all to define the concept of machine learning in biomedicine. Through this Issue, we want to discover what are the latest machine learning concepts applicable in the healthcare system and what are their real benefits for patients and medical staff. What are the current directions of study in this field and what future applications based on machine learning will we have in biomedicine.

The Special Issue aims to bring together researchers, practitioners, and experts in the field to share their experiences and insights on the latest developments in this rapidly evolving area. The topics of interest include, but are not limited to, the following:

Machine learning algorithms and models for biomedical data analysis;

Deep learning and neural networks in biomedicine;

Applications of machine learning in genomics and proteomics;

Medical image analysis and computer vision;

Electronic healthcare records and clinical decision support systems;

Predictive modeling and precision medicine;

Data integration and data mining in healthcare;

Ethical and legal issues in the use of machine learning in biomedicine.

The Special Issue also aims to highlight the future directions and challenges of this field, with a focus on the potential impact of machine learning on healthcare and its implications for society.

Dr. Cǎtǎlina Luca
Dr. Calin Corciova
Dr. Mario Petretta
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. 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

  • machine learning
  • biomedical applications
  • biomedicine

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Published Papers (4 papers)

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21 pages, 2548 KiB  
Article
ABNet: AI-Empowered Abnormal Action Recognition Method for Laboratory Mouse Behavior
by Yuming Chen, Chaopeng Guo, Yue Han, Shuang Hao and Jie Song
Bioengineering 2024, 11(9), 930; https://doi.org/10.3390/bioengineering11090930 - 17 Sep 2024
Viewed by 228
Abstract
The automatic recognition and quantitative analysis of abnormal behavior in mice play a crucial role in behavioral observation experiments in neuroscience, pharmacology, and toxicology. Due to the challenging definition of abnormal behavior and difficulty in collecting training samples, directly applying behavior recognition methods [...] Read more.
The automatic recognition and quantitative analysis of abnormal behavior in mice play a crucial role in behavioral observation experiments in neuroscience, pharmacology, and toxicology. Due to the challenging definition of abnormal behavior and difficulty in collecting training samples, directly applying behavior recognition methods to identify abnormal behavior is often infeasible. This paper proposes ABNet, an AI-empowered abnormal action recognition approach for mice. ABNet utilizes an enhanced Spatio-Temporal Graph Convolutional Network (ST-GCN) as an encoder; ST-GCN combines graph convolution and temporal convolution to efficiently capture and analyze spatio-temporal dynamic features in graph-structured data, making it suitable for complex tasks such as action recognition and traffic prediction. ABNet trains the encoding network with normal behavior samples, then employs unsupervised clustering to identify abnormal behavior in mice. Compared to the original ST-GCN network, the method significantly enhances the capabilities of feature extraction and encoding. We conduct comprehensive experiments on the Kinetics-Skeleton dataset and the mouse behavior dataset to evaluate and validate the performance of ABNet in behavior recognition and abnormal motion detection. In the behavior recognition experiments conducted on the Kinetics-Skeleton dataset, ABNet achieves an accuracy of 32.7% for the top one and 55.2% for the top five. Moreover, in the abnormal behavior analysis experiments conducted on the mouse behavior dataset, ABNet achieves an average accuracy of 83.1%. Full article
(This article belongs to the Special Issue New Insights into Machine Learning and Biomedicine: Updates and Directions)
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10 pages, 1405 KiB  
Article
Continuous Detection of Stimulus Brightness Differences Using Visual Evoked Potentials in Healthy Volunteers with Closed Eyes
by Stephan Kalb, Carl Böck, Matthias Bolz, Christine Schlömmer, Lucija Kudumija, Martin W. Dünser and Jens Meier
Bioengineering 2024, 11(6), 605; https://doi.org/10.3390/bioengineering11060605 - 13 Jun 2024
Viewed by 836
Abstract
Background/Objectives: We defined the value of a machine learning algorithm to distinguish between the EEG response to no light or any light stimulations, and between light stimulations with different brightnesses in awake volunteers with closed eyelids. This new method utilizing EEG analysis is [...] Read more.
Background/Objectives: We defined the value of a machine learning algorithm to distinguish between the EEG response to no light or any light stimulations, and between light stimulations with different brightnesses in awake volunteers with closed eyelids. This new method utilizing EEG analysis is visionary in the understanding of visual signal processing and will facilitate the deepening of our knowledge concerning anesthetic research. Methods: X-gradient boosting models were used to classify the cortical response to visual stimulation (no light vs. light stimulations and two lights with different brightnesses). For each of the two classifications, three scenarios were tested: training and prediction in all participants (all), training and prediction in one participant (individual), and training across all but one participant with prediction performed in the participant left out (one out). Results: Ninety-four Caucasian adults were included. The machine learning algorithm had a very high predictive value and accuracy in differentiating between no light and any light stimulations (AUCROCall: 0.96; accuracyall: 0.94; AUCROCindividual: 0.96 ± 0.05, accuracyindividual: 0.94 ± 0.05; AUCROConeout: 0.98 ± 0.04; accuracyoneout: 0.96 ± 0.04). The machine learning algorithm was highly predictive and accurate in distinguishing between light stimulations with different brightnesses (AUCROCall: 0.97; accuracyall: 0.91; AUCROCindividual: 0.98 ± 0.04, accuracyindividual: 0.96 ± 0.04; AUCROConeout: 0.96 ± 0.05; accuracyoneout: 0.93 ± 0.06). The predictive value and accuracy of both classification tasks was comparable between males and females. Conclusions: Machine learning algorithms could almost continuously and reliably differentiate between the cortical EEG responses to no light or light stimulations using visual evoked potentials in awake female and male volunteers with eyes closed. Our findings may open new possibilities for the use of visual evoked potentials in the clinical and intraoperative setting. Full article
(This article belongs to the Special Issue New Insights into Machine Learning and Biomedicine: Updates and Directions)
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14 pages, 1297 KiB  
Article
Precision Non-Alcoholic Fatty Liver Disease (NAFLD) Diagnosis: Leveraging Ensemble Machine Learning and Gender Insights for Cost-Effective Detection
by Azadeh Alizargar, Yang-Lang Chang, Mohammad Alkhaleefah and Tan-Hsu Tan
Bioengineering 2024, 11(6), 600; https://doi.org/10.3390/bioengineering11060600 - 12 Jun 2024
Viewed by 1001
Abstract
Non-Alcoholic Fatty Liver Disease (NAFLD) is characterized by the accumulation of excess fat in the liver. If left undiagnosed and untreated during the early stages, NAFLD can progress to more severe conditions such as inflammation, liver fibrosis, cirrhosis, and even liver failure. In [...] Read more.
Non-Alcoholic Fatty Liver Disease (NAFLD) is characterized by the accumulation of excess fat in the liver. If left undiagnosed and untreated during the early stages, NAFLD can progress to more severe conditions such as inflammation, liver fibrosis, cirrhosis, and even liver failure. In this study, machine learning techniques were employed to predict NAFLD using affordable and accessible laboratory test data, while the conventional technique hepatic steatosis index (HSI)was calculated for comparison. Six algorithms (random forest, K-nearest Neighbors, Logistic Regression, Support Vector Machine, extreme gradient boosting, decision tree), along with an ensemble model, were utilized for dataset analysis. The objective was to develop a cost-effective tool for enabling early diagnosis, leading to better management of the condition. The issue of imbalanced data was addressed using the Synthetic Minority Oversampling Technique Edited Nearest Neighbors (SMOTEENN). Various evaluation metrics including the F1 score, precision, accuracy, recall, confusion matrix, the mean absolute error (MAE), receiver operating characteristics (ROC), and area under the curve (AUC) were employed to assess the suitability of each technique for disease prediction. Experimental results using the National Health and Nutrition Examination Survey (NHANES) dataset demonstrated that the ensemble model achieved the highest accuracy (0.99) and AUC (1.00) compared to the machine learning techniques that we used and HSI. These findings indicate that the ensemble model holds potential as a beneficial tool for healthcare professionals to predict NAFLD, leveraging accessible and cost-effective laboratory test data. Full article
(This article belongs to the Special Issue New Insights into Machine Learning and Biomedicine: Updates and Directions)
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11 pages, 587 KiB  
Article
ResnetAge: A Resnet-Based DNA Methylation Age Prediction Method
by Lijuan Shi, Boquan Hai, Zhejun Kuang, Han Wang and Jian Zhao
Bioengineering 2024, 11(1), 34; https://doi.org/10.3390/bioengineering11010034 - 28 Dec 2023
Cited by 2 | Viewed by 1776
Abstract
Aging is a significant contributing factor to degenerative diseases such as cancer. The extent of DNA methylation in human cells indicates the aging process and screening for age-related methylation sites can be used to construct epigenetic clocks. Thereby, it can be a new [...] Read more.
Aging is a significant contributing factor to degenerative diseases such as cancer. The extent of DNA methylation in human cells indicates the aging process and screening for age-related methylation sites can be used to construct epigenetic clocks. Thereby, it can be a new aging-detecting marker for clinical diagnosis and treatments. Predicting the biological age of human individuals is conducive to the study of physical aging problems. Although many researchers have developed epigenetic clock prediction methods based on traditional machine learning and even deep learning, higher prediction accuracy is still required to match the clinical applications. Here, we proposed an epigenetic clock prediction method based on a Resnet neuro networks model named ResnetAge. The model accepts 22,278 CpG sites as a sample input, supporting both the Illumina 27K and 450K identification frameworks. It was trained using 32 public datasets containing multiple tissues such as whole blood, saliva, and mouth. The Mean Absolute Error (MAE) of the training set is 1.29 years, and the Median Absolute Deviation (MAD) is 0.98 years. The Mean Absolute Error (MAE) of the validation set is 3.24 years, and the Median Absolute Deviation (MAD) is 2.3 years. Our method has higher accuracy in age prediction in comparison with other methylation-based age prediction methods. Full article
(This article belongs to the Special Issue New Insights into Machine Learning and Biomedicine: Updates and Directions)
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