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Bioengineering
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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: closed (31 January 2025) | Viewed by 13936

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

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Related Special Issue

Published Papers (7 papers)

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14 pages, 367 KiB  
Article
Integrating Wearable Sensor Signal Processing with Unsupervised Learning Methods for Tremor Classification in Parkinson’s Disease
by Serena Dattola, Augusto Ielo, Angelo Quartarone and Maria Cristina De Cola
Bioengineering 2025, 12(1), 37; https://doi.org/10.3390/bioengineering12010037 - 6 Jan 2025
Viewed by 1152
Abstract
Tremor is one of the most common symptoms of Parkinson’s disease (PD), assessed using clinician-assigned clinical scales, which can be subjective and prone to variability. This study evaluates the potential of unsupervised learning for the classification and assessment of tremor severity from wearable [...] Read more.
Tremor is one of the most common symptoms of Parkinson’s disease (PD), assessed using clinician-assigned clinical scales, which can be subjective and prone to variability. This study evaluates the potential of unsupervised learning for the classification and assessment of tremor severity from wearable sensor data. We analyzed 25 resting tremor signals from 24 participants (13 PD patients and 11 controls), focusing on motion intensities derived from accelerometer recordings. The k-means clustering algorithm was employed, achieving a classification accuracy of 76% for tremor versus non-tremor states. However, performance decreased for multiclass tremor severity classification (57.1%) and binary classification of severe versus mild tremor (71.4%), highlighting challenges in detecting subtle intensity variations. The findings underscore the utility of unsupervised learning in enabling scalable, objective tremor analysis. Integration of such models into wearable systems could improve continuous monitoring, enhance rehabilitation strategies, and support standardized clinical assessments. Future work should explore advanced algorithms, enriched feature sets, and larger datasets to improve robustness and generalizability. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Biomedical Applications, 2nd Edition)
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13 pages, 2573 KiB  
Article
Contrast-Enhancing Lesion Segmentation in Multiple Sclerosis: A Deep Learning Approach Validated in a Multicentric Cohort
by Martina Greselin, Po-Jui Lu, Lester Melie-Garcia, Mario Ocampo-Pineda, Riccardo Galbusera, Alessandro Cagol, Matthias Weigel, Nina de Oliveira Siebenborn, Esther Ruberte, Pascal Benkert, Stefanie Müller, Sebastian Finkener, Jochen Vehoff, Giulio Disanto, Oliver Findling, Andrew Chan, Anke Salmen, Caroline Pot, Claire Bridel, Chiara Zecca, Tobias Derfuss, Johanna M. Lieb, Michael Diepers, Franca Wagner, Maria I. Vargas, Renaud Du Pasquier, Patrice H. Lalive, Emanuele Pravatà, Johannes Weber, Claudio Gobbi, David Leppert, Olaf Chan-Hi Kim, Philippe C. Cattin, Robert Hoepner, Patrick Roth, Ludwig Kappos, Jens Kuhle and Cristina Granzieraadd Show full author list remove Hide full author list
Bioengineering 2024, 11(8), 858; https://doi.org/10.3390/bioengineering11080858 - 22 Aug 2024
Viewed by 1623
Abstract
The detection of contrast-enhancing lesions (CELs) is fundamental for the diagnosis and monitoring of patients with multiple sclerosis (MS). This task is time-consuming and suffers from high intra- and inter-rater variability in clinical practice. However, only a few studies proposed automatic approaches for [...] Read more.
The detection of contrast-enhancing lesions (CELs) is fundamental for the diagnosis and monitoring of patients with multiple sclerosis (MS). This task is time-consuming and suffers from high intra- and inter-rater variability in clinical practice. However, only a few studies proposed automatic approaches for CEL detection. This study aimed to develop a deep learning model that automatically detects and segments CELs in clinical Magnetic Resonance Imaging (MRI) scans. A 3D UNet-based network was trained with clinical MRI from the Swiss Multiple Sclerosis Cohort. The dataset comprised 372 scans from 280 MS patients: 162 showed at least one CEL, while 118 showed no CELs. The input dataset consisted of T1-weighted before and after gadolinium injection, and FLuid Attenuated Inversion Recovery images. The sampling strategy was based on a white matter lesion mask to confirm the existence of real contrast-enhancing lesions. To overcome the dataset imbalance, a weighted loss function was implemented. The Dice Score Coefficient and True Positive and False Positive Rates were 0.76, 0.93, and 0.02, respectively. Based on these results, the model developed in this study might well be considered for clinical decision support. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Biomedical Applications, 2nd Edition)
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12 pages, 6530 KiB  
Article
Machine Learning-Enhanced Estimation of Cellular Protein Levels from Bright-Field Images
by Takeshi Tohgasaki, Arisa Touyama, Shohei Kousai and Kaita Imai
Bioengineering 2024, 11(8), 774; https://doi.org/10.3390/bioengineering11080774 - 31 Jul 2024
Viewed by 1698
Abstract
In this study, we aimed to develop a novel method for non-invasively determining intracellular protein levels, which is essential for understanding cellular phenomena. This understanding hinges on insights into gene expression, cell morphology, dynamics, and intercellular interactions. Traditional cell analysis techniques, such as [...] Read more.
In this study, we aimed to develop a novel method for non-invasively determining intracellular protein levels, which is essential for understanding cellular phenomena. This understanding hinges on insights into gene expression, cell morphology, dynamics, and intercellular interactions. Traditional cell analysis techniques, such as immunostaining, live imaging, next-generation sequencing, and single-cell analysis, despite rapid advancements, face challenges in comprehensively integrating gene and protein expression data with spatiotemporal information. Leveraging advances in machine learning for image analysis, we designed a new model to estimate cellular biomarker protein levels using a blend of phase-contrast and fluorescent immunostaining images of epidermal keratinocytes. By iterating this process across various proteins, our model can estimate multiple protein levels from a single phase-contrast image. Additionally, we developed a system for analyzing multiple protein expression levels alongside spatiotemporal data through live imaging and phase-contrast methods. Our study offers valuable tools for cell-based research and presents a new avenue for addressing molecular biological challenges. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Biomedical Applications, 2nd Edition)
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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
Cited by 1 | Viewed by 1995
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
Cited by 9 | Viewed by 2732
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
Cited by 1 | Viewed by 2374
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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17 pages, 2199 KiB  
Systematic Review
Artificial Intelligence-Driven Wireless Sensing for Health Management
by Merih Deniz Toruner, Victoria Shi, John Sollee, Wen-Chi Hsu, Guangdi Yu, Yu-Wei Dai, Christian Merlo, Karthik Suresh, Zhicheng Jiao, Xuyu Wang, Shiwen Mao and Harrison Bai
Bioengineering 2025, 12(3), 244; https://doi.org/10.3390/bioengineering12030244 - 27 Feb 2025
Viewed by 795
Abstract
(1) Background: With technological advancements, the integration of wireless sensing and artificial intelligence (AI) has significant potential for real-time monitoring and intervention. Wireless sensing devices have been applied to various medical areas for early diagnosis, monitoring, and treatment response. This review focuses on [...] Read more.
(1) Background: With technological advancements, the integration of wireless sensing and artificial intelligence (AI) has significant potential for real-time monitoring and intervention. Wireless sensing devices have been applied to various medical areas for early diagnosis, monitoring, and treatment response. This review focuses on the latest advancements in wireless, AI-incorporated methods applied to clinical medicine. (2) Methods: We conducted a comprehensive search in PubMed, IEEEXplore, Embase, and Scopus for articles that describe AI-incorporated wireless sensing devices for clinical applications. We analyzed the strengths and limitations within their respective medical domains, highlighting the value of wireless sensing in precision medicine, and synthesized the literature to provide areas for future work. (3) Results: We identified 10,691 articles and selected 34 that met our inclusion criteria, focusing on real-world validation of wireless sensing. The findings indicate that these technologies demonstrate significant potential in improving diagnosis, treatment monitoring, and disease prevention. Notably, the use of acoustic signals, channel state information, and radar emerged as leading techniques, showing promising results in detecting physiological changes without invasive procedures. (4) Conclusions: This review highlights the role of wireless sensing in clinical care and suggests a growing trend towards integrating these technologies into routine healthcare, particularly patient monitoring and diagnostic support. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Biomedical Applications, 2nd Edition)
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