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Bioengineering
Special Issues
Machine Learning and Artificial Intelligence for Biomedical Applications, 2nd Edition
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Machine Learning and Artificial Intelligence for Biomedical Applications, 2nd Edition

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

Deadline for manuscript submissions: 15 July 2024 | Viewed by 2892

Special Issue Editors

Dr. Crescenzio Gallo

E-Mail Website
Guest Editor
Department of Clinical and Experimental Medicine, Università degli studi di Foggia, 71122 Foggia, FG, Italy
Interests: bioinformatics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Gianluca Zaza

E-Mail Website
Guest Editor
Department of Computer Science, University of Bari "Aldo Moro", 70125 Bari, Italy
Interests: artificial intelligence; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, an increase in the accuracy of information technology has led to several scientific breakthroughs. The first researchers to benefit from improved hardware components have been the developers of artificial intelligence algorithms, who have been able to apply these algorithms in several scientific fields, including biomedicine. Biomedicine is a field of medicine that applies the principles of biology and natural sciences to the development of relevant technologies for healthcare. The combination of artificial intelligence algorithms and biomedicine has led to many applications, such as:

  • Image analysis of human organs using magnetic resonance images (MRI);
  • DNA/RNA sequencing and protein structure interactions and predictions;
  • Analysis of different biosignals, via methods involving electroencephalograms (EEG), electromyography (EEG), and electrocardiograms (ECG).

In this context, machine learning algorithms enable us to learn from observational data and construct highly accurate artificial intelligence models to support the physician. However, obtaining models with high accuracy may not be enough, as AI-based biomedical decisions must be understandable to the physician. Therefore, it is necessary to equip machine learning methods with explainability capacity, leading to explainable artificial intelligence techniques that enable the physician to understand the decisions suggested by the models they use.

This is the second volume of our Special Issue, "Machine Learning and Artificial Intelligence for Biomedical Applications". Please feel free to download and read it freely via the following link:
https://www.mdpi.com/journal/bioengineering/special_issues/39708P1H4A.

Dr. Crescenzio Gallo
Dr. Gianluca Zaza
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

  • artificial intelligence models
  • biomedicine
  • machine learning methods
  • artificial neural networks
  • precision medicine
  • personalized health care

Published Papers (3 papers)

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Research

16 pages, 5355 KiB  
Article
Deep-Learning-Based Automated Anomaly Detection of EEGs in Intensive Care Units
by Jacky Chung-Hao Wu, Nien-Chen Liao, Ta-Hsin Yang, Chen-Cheng Hsieh, Jin-An Huang, Yen-Wei Pai, Yi-Jhen Huang, Chieh-Liang Wu and Henry Horng-Shing Lu
Bioengineering 2024, 11(5), 421; https://doi.org/10.3390/bioengineering11050421 - 25 Apr 2024
Viewed by 320
Abstract
An intensive care unit (ICU) is a special ward in the hospital for patients who require intensive care. It is equipped with many instruments monitoring patients’ vital signs and supported by the medical staff. However, continuous monitoring demands a massive workload of medical [...] Read more.
An intensive care unit (ICU) is a special ward in the hospital for patients who require intensive care. It is equipped with many instruments monitoring patients’ vital signs and supported by the medical staff. However, continuous monitoring demands a massive workload of medical care. To ease the burden, we aim to develop an automatic detection model to monitor when brain anomalies occur. In this study, we focus on electroencephalography (EEG), which monitors the brain electroactivity of patients continuously. It is mainly for the diagnosis of brain malfunction. We propose the gated-recurrent-unit-based (GRU-based) model for detecting brain anomalies; it predicts whether the spike or sharp wave happens within a short time window. Based on the banana montage setting, the proposed model exploits characteristics of multiple channels simultaneously to detect anomalies. It is trained, validated, and tested on separated EEG data and achieves more than 90% testing performance on sensitivity, specificity, and balanced accuracy. The proposed anomaly detection model detects the existence of a spike or sharp wave precisely; it will notify the ICU medical staff, who can provide immediate follow-up treatment. Consequently, it can reduce the medical workload in the ICU significantly. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Biomedical Applications, 2nd Edition)
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23 pages, 8470 KiB  
Article
Leveraging Deep Learning for Fine-Grained Categorization of Parkinson’s Disease Progression Levels through Analysis of Vocal Acoustic Patterns
by Hadi Sedigh Malekroodi, Nuwan Madusanka, Byeong-il Lee and Myunggi Yi
Bioengineering 2024, 11(3), 295; https://doi.org/10.3390/bioengineering11030295 - 21 Mar 2024
Viewed by 1020
Abstract
Speech impairments often emerge as one of the primary indicators of Parkinson’s disease (PD), albeit not readily apparent in its early stages. While previous studies focused predominantly on binary PD detection, this research explored the use of deep learning models to automatically classify [...] Read more.
Speech impairments often emerge as one of the primary indicators of Parkinson’s disease (PD), albeit not readily apparent in its early stages. While previous studies focused predominantly on binary PD detection, this research explored the use of deep learning models to automatically classify sustained vowel recordings into healthy controls, mild PD, or severe PD based on motor symptom severity scores. Popular convolutional neural network (CNN) architectures, VGG and ResNet, as well as vision transformers, Swin, were fine-tuned on log mel spectrogram image representations of the segmented voice data. Furthermore, the research investigated the effects of audio segment lengths and specific vowel sounds on the performance of these models. The findings indicated that implementing longer segments yielded better performance. The models showed strong capability in distinguishing PD from healthy subjects, achieving over 95% precision. However, reliably discriminating between mild and severe PD cases remained challenging. The VGG16 achieved the best overall classification performance with 91.8% accuracy and the largest area under the ROC curve. Furthermore, focusing analysis on the vowel /u/ could further improve accuracy to 96%. Applying visualization techniques like Grad-CAM also highlighted how CNN models focused on localized spectrogram regions while transformers attended to more widespread patterns. Overall, this work showed the potential of deep learning for non-invasive screening and monitoring of PD progression from voice recordings, but larger multi-class labeled datasets are needed to further improve severity classification. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Biomedical Applications, 2nd Edition)
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20 pages, 3854 KiB  
Article
Reliable Off-Resonance Correction in High-Field Cardiac MRI Using Autonomous Cardiac B0 Segmentation with Dual-Modality Deep Neural Networks
by Xinqi Li, Yuheng Huang, Archana Malagi, Chia-Chi Yang, Ghazal Yoosefian, Li-Ting Huang, Eric Tang, Chang Gao, Fei Han, Xiaoming Bi, Min-Chi Ku, Hsin-Jung Yang and Hui Han
Bioengineering 2024, 11(3), 210; https://doi.org/10.3390/bioengineering11030210 - 23 Feb 2024
Viewed by 972
Abstract
B0 field inhomogeneity is a long-lasting issue for Cardiac MRI (CMR) in high-field (3T and above) scanners. The inhomogeneous B0 fields can lead to corrupted image quality, prolonged scan time, and false diagnosis. B0 shimming is the most straightforward way [...] Read more.
B0 field inhomogeneity is a long-lasting issue for Cardiac MRI (CMR) in high-field (3T and above) scanners. The inhomogeneous B0 fields can lead to corrupted image quality, prolonged scan time, and false diagnosis. B0 shimming is the most straightforward way to improve the B0 homogeneity. However, today’s standard cardiac shimming protocol requires manual selection of a shim volume, which often falsely includes regions with large B0 deviation (e.g., liver, fat, and chest wall). The flawed shim field compromises the reliability of high-field CMR protocols, which significantly reduces the scan efficiency and hinders its wider clinical adoption. This study aims to develop a dual-channel deep learning model that can reliably contour the cardiac region for B0 shim without human interaction and under variable imaging protocols. By utilizing both the magnitude and phase information, the model achieved a high segmentation accuracy in the B0 field maps compared to the conventional single-channel methods (Dice score: 2D-mag = 0.866, 3D-mag = 0.907, and 3D-mag-phase = 0.938, all p < 0.05). Furthermore, it shows better generalizability against the common variations in MRI imaging parameters and enables significantly improved B0 shim compared to the standard method (SD(B0Shim): Proposed = 15 ± 11% vs. Standard = 6 ± 12%, p < 0.05). The proposed autonomous model can boost the reliability of cardiac shimming at 3T and serve as the foundation for more reliable and efficient high-field CMR imaging in clinical routines. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Biomedical Applications, 2nd Edition)
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