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Machine Learning Technology in Biomedical Engineering

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 11725

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Special Issue Editors

Dr. Hongqing Yu

E-Mail Website
Guest Editor

School of Computing and Data Science Research Centre, University of Derby, Derby DE22 3AW, UK
Interests: data science; machine learning; knowledge discovery and representation; semantic technologies; deep machine learning; natural language processing
Special Issues, Collections and Topics in MDPI journals

Dr. Alaa AlZoubi

E-Mail Website
Co-Guest Editor

College of Science and Engineering, University of Derby, Derby, UK
Interests: artificial intelligence; AI decision explainability; deep learning and computer vision
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yifan Zhao

E-Mail Website
Co-Guest Editor

School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, UK
Interests: computing, simulation and modelling; human factors; industrial automation; instrumentation, sensors and measurement science; systems engineering; through-life engineering services
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hongbo Du

E-Mail Website
Co-Guest Editor

School of Computing, University of Buckingham, Buckingham, UK
Interests: big data processing; data mining; machine learning; image and time series analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue on "Machine Learning Technology in Biomedical Engineering" aims to provide a platform for researchers to showcase their latest research and findings on the application of machine learning technology in the field of biomedical engineering. The use of machine learning technology in healthcare has been growing rapidly in recent years and has the potential to revolutionize many aspects of healthcare, including disease diagnosis, treatment, and personalized medicine.

The Special Issue will cover a wide range of topics related to the application of machine learning in biomedical engineering, including predictive modeling, image and signal processing, deep learning, drug discovery, biomarker discovery, and medical decision-making. Contributions from interdisciplinary teams combining expertise in machine learning and biomedical engineering are encouraged.

Importance:

The use of machine learning technology in biomedical engineering has significant potential to improve healthcare outcomes and make healthcare more efficient and accessible. By applying machine learning algorithms to large datasets of biomedical information, researchers and healthcare professionals can gain new insights into disease mechanisms, identify new biomarkers for disease, and develop more effective treatments. Machine learning algorithms can also be used to improve medical imaging analysis, automate medical diagnosis and decision-making, and optimize drug-discovery processes.

This Special Issue is important because it provides a platform for researchers to share their latest findings and perspectives on the application of machine learning technology in biomedical engineering, and to encourage interdisciplinary collaboration between machine learning and biomedical engineering researchers. It is an exciting opportunity for researchers to contribute to the development of new technologies and methodologies that have the potential to significantly improve healthcare outcomes

Dr. Hongqing Yu
Dr. Alaa AlZoubi
Dr. Yifan Zhao
Prof. Dr. Hongbo Du
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 engineering
big data
predictive modeling
image and signal processing
medical image analysis
deep learning
biomarker
personalized medicine
wearable devices and mobile health

Related Special Issue

Machine Learning Technology in Biomedical Engineering—2nd Edition in Bioengineering (3 articles)

Published Papers (10 papers)

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Research

32 pages, 2806 KiB

Open AccessArticle

Dense Multi-Scale Graph Convolutional Network for Knee Joint Cartilage Segmentation

by Christos Chadoulos, Dimitrios Tsaopoulos, Andreas Symeonidis, Serafeim Moustakidis and John Theocharis

Bioengineering 2024, 11(3), 278; https://doi.org/10.3390/bioengineering11030278 - 14 Mar 2024

Viewed by 786

Abstract

In this paper, we propose a dense multi-scale adaptive graph convolutional network (DMA-GCN) method for automatic segmentation of the knee joint cartilage from MR images. Under the multi-atlas setting, the suggested approach exhibits several novelties, as described in the following. First, our models integrate both local-level and global-level learning simultaneously. The local learning task aggregates spatial contextual information from aligned spatial neighborhoods of nodes, at multiple scales, while global learning explores pairwise affinities between nodes, located globally at different positions in the image. We propose two different structures of building models, whereby the local and global convolutional units are combined by following an alternating or a sequential manner. Secondly, based on the previous models, we develop the DMA-GCN network, by utilizing a densely connected architecture with residual skip connections. This is a deeper GCN structure, expanded over different block layers, thus being capable of providing more expressive node feature representations. Third, all units pertaining to the overall network are equipped with their individual adaptive graph learning mechanism, which allows the graph structures to be automatically learned during training. The proposed cartilage segmentation method is evaluated on the entire publicly available Osteoarthritis Initiative (OAI) cohort. To this end, we have devised a thorough experimental setup, with the goal of investigating the effect of several factors of our approach on the classification rates. Furthermore, we present exhaustive comparative results, considering traditional existing methods, six deep learning segmentation methods, and seven graph-based convolution methods, including the currently most representative models from this field. The obtained results demonstrate that the DMA-GCN outperforms all competing methods across all evaluation measures, providing

D S C = 95.71 %

and

D S C = 94.02 %

for the segmentation of femoral and tibial cartilage, respectively. Full article

(This article belongs to the Special Issue Machine Learning Technology in Biomedical Engineering)

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12 pages, 2261 KiB

Open AccessArticle

Comparing the Robustness of ResNet, Swin-Transformer, and MLP-Mixer under Unique Distribution Shifts in Fundus Images

by Kazuaki Ishihara and Koutarou Matsumoto

Bioengineering 2023, 10(12), 1383; https://doi.org/10.3390/bioengineering10121383 - 1 Dec 2023

Viewed by 956

Abstract

Background: Diabetic retinopathy (DR) is the leading cause of visual impairment and blindness. Consequently, numerous deep learning models have been developed for the early detection of DR. Safety-critical applications employed in medical diagnosis must be robust to distribution shifts. Previous studies have focused on model performance under distribution shifts using natural image datasets such as ImageNet, CIFAR-10, and SVHN. However, there is a lack of research specifically investigating the performance using medical image datasets. To address this gap, we investigated trends under distribution shifts using fundus image datasets. Methods: We used the EyePACS dataset for DR diagnosis, introduced noise specific to fundus images, and evaluated the performance of ResNet, Swin-Transformer, and MLP-Mixer models under a distribution shift. The discriminative ability was evaluated using the Area Under the Receiver Operating Characteristic curve (ROC-AUC), while the calibration ability was evaluated using the monotonic sweep calibration error (ECE sweep). Results: Swin-Transformer exhibited a higher ROC-AUC than ResNet under all types of noise and displayed a smaller reduction in the ROC-AUC due to noise. ECE sweep did not show a consistent trend across different model architectures. Conclusions: Swin-Transformer consistently demonstrated superior discrimination compared to ResNet. This trend persisted even under unique distribution shifts in the fundus images. Full article

(This article belongs to the Special Issue Machine Learning Technology in Biomedical Engineering)

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16 pages, 2277 KiB

Open AccessArticle

Sophisticated Study of Time, Frequency and Statistical Analysis for Gradient-Switching-Induced Potentials during MRI

by Karim Bouzrara, Odette Fokapu, Ahmed Fakhfakh and Faouzi Derbel

Bioengineering 2023, 10(11), 1282; https://doi.org/10.3390/bioengineering10111282 - 3 Nov 2023

Viewed by 748

Abstract

Magnetic resonance imaging (MRI) is a standard procedure in medical imaging, on a par with echography and tomodensitometry. In contrast to radiological procedures, no harmful radiation is produced. The constant development of magnetic resonance imaging (MRI) techniques has enabled the production of higher resolution images. The switching of magnetic field gradients for MRI imaging generates induced voltages that strongly interfere with the electrophysiological signals (EPs) collected simultaneously. When the bandwidth of the collection amplifiers is higher than 150 Hz, these induced voltages are difficult to eliminate. Understanding the behavior of these artefacts contributes to the development of new digital processing tools for better quality EPs. In this paper, we present a study of induced voltages collected in vitro using a device (350 Hz bandwidth). The experiments were conducted on a 1.5T MRI machine with two MRI sequences (fast spin echo (FSE) and cine gradient echo (CINE)) and three slice orientations. The recorded induced voltages were then segmented into extract patterns called “artefact puffs”. Two analysis series, “global” and “local”, were then performed. The study found that the temporal and frequency characteristics were specific to the sequences and orientations of the slice and that, despite the pseudo-periodic character of the artefacts, the variabilities within the same recording were significant. These evolutions were confirmed by two stationarity tests: the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) and the time-frequency approach. The induced potentials, all stationary at the global scale, are no longer stationary at the local scale, which is an important issue in the design of optimal filters adapted to reduce MRI artifacts contaminating a large bandwidth, which varies between 0 and 500 Hz. Full article

(This article belongs to the Special Issue Machine Learning Technology in Biomedical Engineering)

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12 pages, 2368 KiB

Open AccessArticle

Implicit HbA1c Achieving 87% Accuracy within 90 Days in Non-Invasive Fasting Blood Glucose Measurements Using Photoplethysmography

by Justin Chu, Yao-Ting Chang, Shien-Kuei Liaw and Fu-Liang Yang

Bioengineering 2023, 10(10), 1207; https://doi.org/10.3390/bioengineering10101207 - 16 Oct 2023

Viewed by 1095

Abstract

To reduce the error induced by overfitting or underfitting in predicting non-invasive fasting blood glucose (NIBG) levels using photoplethysmography (PPG) data alone, we previously demonstrated that incorporating HbA1c led to a notable 10% improvement in NIBG prediction accuracy (the ratio in zone A of Clarke’s error grid). However, this enhancement came at the cost of requiring an additional HbA1c measurement, thus being unfriendly to users. In this study, the enhanced HbA1c NIBG deep learning model (blood glucose level predicted from PPG and HbA1c) was trained with 1494 measurements, and we replaced the HbA1c measurement (explicit HbA1c) with “implicit HbA1c” which is reversely derived from pretested PPG and finger-pricked blood glucose levels. The implicit HbA1c is then evaluated across intervals up to 90 days since the pretest, achieving an impressive 87% accuracy, while the remaining 13% falls near the CEG zone A boundary. The implicit HbA1c approach exhibits a remarkable 16% improvement over the explicit HbA1c method by covering personal correction items automatically. This improvement not only refines the accuracy of the model but also enhances the practicality of the previously proposed model that relied on an HbA1c input. The nonparametric Wilcoxon paired test conducted on the percentage error of implicit and explicit HbA1c prediction results reveals a substantial difference, with a p-value of 2.75 × 10^–7. Full article

(This article belongs to the Special Issue Machine Learning Technology in Biomedical Engineering)

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13 pages, 1704 KiB

Open AccessArticle

Implementing a Novel Machine Learning System for Nutrition Education in Diabetes Mellitus Nutritional Clinic: Predicting 1-Year Blood Glucose Control

by Mei-Yuan Liu, Chung-Feng Liu, Tzu-Chi Lin and Yu-Shan Ma

Bioengineering 2023, 10(10), 1139; https://doi.org/10.3390/bioengineering10101139 - 28 Sep 2023

Cited by 2 | Viewed by 1231

Abstract

(1) Background: Persistent hyperglycemia in diabetes mellitus (DM) increases the risk of death and causes cardiovascular disease (CVD), resulting in significant social and economic costs. This study used a machine learning (ML) technique to build prediction models with the factors of lifestyle, medication compliance, and self-control in eating habits and then implemented a predictive system based on the best model to forecast whether blood glucose can be well-controlled within 1 year in diabetic patients attending a DM nutritional clinic. (2) Methods: Data were collected from outpatients aged 20 years or older with type 2 DM who received nutrition education in Chi Mei Medical Center. Multiple ML algorithms were used to build the predictive models. (3) Results: The predictive models achieved accuracies ranging from 0.611 to 0.690. The XGBoost model with the highest area under the curve (AUC) of 0.738 was regarded as the best and used for the predictive system implementation. SHAP analysis was performed to interpret the feature importance in the best model. The predictive system, evaluated by dietitians, received positive feedback as a beneficial tool for diabetes nutrition consultations. (4) Conclusions: The ML prediction model provides a promising approach for diabetes nutrition consultations to maintain good long-term blood glucose control, reduce diabetes-related complications, and enhance the quality of medical care. Full article

(This article belongs to the Special Issue Machine Learning Technology in Biomedical Engineering)

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18 pages, 2449 KiB

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AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research

by Hong Qing Yu, Sam O’Neill and Ali Kermanizadeh

Bioengineering 2023, 10(10), 1134; https://doi.org/10.3390/bioengineering10101134 - 27 Sep 2023

Viewed by 943

Abstract

The fusion of machine learning and biomedical research offers novel ways to understand, diagnose, and treat various health conditions. However, the complexities of biomedical data, coupled with the intricate process of developing and deploying machine learning solutions, often pose significant challenges to researchers in these fields. Our pivotal achievement in this research is the introduction of the Automatic Semantic Machine Learning Microservice (AIMS) framework. AIMS addresses these challenges by automating various stages of the machine learning pipeline, with a particular emphasis on the ontology of machine learning services tailored to the biomedical domain. This ontology encompasses everything from task representation, service modeling, and knowledge acquisition to knowledge reasoning and the establishment of a self-supervised learning policy. Our framework has been crafted to prioritize model interpretability, integrate domain knowledge effortlessly, and handle biomedical data with efficiency. Additionally, AIMS boasts a distinctive feature: it leverages self-supervised knowledge learning through reinforcement learning techniques, paired with an ontology-based policy recording schema. This enables it to autonomously generate, fine-tune, and continually adapt to machine learning models, especially when faced with new tasks and data. Our work has two standout contributions demonstrating that machine learning processes in the biomedical domain can be automated, while integrating a rich domain knowledge base and providing a way for machines to have self-learning ability, ensuring they handle new tasks effectively. To showcase AIMS in action, we have highlighted its prowess in three case studies of biomedical tasks. These examples emphasize how our framework can simplify research routines, uplift the caliber of scientific exploration, and set the stage for notable advances. Full article

(This article belongs to the Special Issue Machine Learning Technology in Biomedical Engineering)

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20 pages, 1441 KiB

Open AccessArticle

Hybrid Deep Neural Network Framework Combining Skeleton and Gait Features for Pathological Gait Recognition

by Kooksung Jun, Keunhan Lee, Sanghyub Lee, Hwanho Lee and Mun Sang Kim

Bioengineering 2023, 10(10), 1133; https://doi.org/10.3390/bioengineering10101133 - 27 Sep 2023

Cited by 1 | Viewed by 935

Abstract

Human skeleton data obtained using a depth camera have been used for pathological gait recognition to support doctor or physician diagnosis decisions. Most studies for skeleton-based pathological gait recognition have used either raw skeleton sequences directly or gait features, such as gait parameters and joint angles, extracted from raw skeleton sequences. We hypothesize that using skeleton, joint angles, and gait parameters together can improve recognition performance. This study aims to develop a deep neural network model that effectively combines different types of input data. We propose a hybrid deep neural network framework composed of a graph convolutional network, recurrent neural network, and artificial neural network to effectively encode skeleton sequences, joint angle sequences, and gait parameters, respectively. The features extracted from three different input data types are fused and fed into the final classification layer. We evaluate the proposed model on two different skeleton datasets (a simulated pathological gait dataset and a vestibular disorder gait dataset) that were collected using an Azure Kinect. The proposed model, with multiple types of input, improved the pathological gait recognition performance compared to single input models on both datasets. Furthermore, it achieved the best performance among the state-of-the-art models for skeleton-based action recognition. Full article

(This article belongs to the Special Issue Machine Learning Technology in Biomedical Engineering)

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Graphical abstract

14 pages, 2008 KiB

Open AccessArticle

New Approach for Generating Synthetic Medical Data to Predict Type 2 Diabetes

by Zarnigor Tagmatova, Akmalbek Abdusalomov, Rashid Nasimov, Nigorakhon Nasimova, Ali Hikmet Dogru and Young-Im Cho

Bioengineering 2023, 10(9), 1031; https://doi.org/10.3390/bioengineering10091031 - 1 Sep 2023

Viewed by 1392

Abstract

The lack of medical databases is currently the main barrier to the development of artificial intelligence-based algorithms in medicine. This issue can be partially resolved by developing a reliable high-quality synthetic database. In this study, an easy and reliable method for developing a synthetic medical database based only on statistical data is proposed. This method changes the primary database developed based on statistical data using a special shuffle algorithm to achieve a satisfactory result and evaluates the resulting dataset using a neural network. Using the proposed method, a database was developed to predict the risk of developing type 2 diabetes 5 years in advance. This dataset consisted of data from 172,290 patients. The prediction accuracy reached 94.45% during neural network training of the dataset. Full article

(This article belongs to the Special Issue Machine Learning Technology in Biomedical Engineering)

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13 pages, 1954 KiB

Open AccessArticle

PPChain: A Blockchain for Pandemic Prevention and Control Assisted by Federated Learning

by Tianruo Cao, Yongqi Pan, Honghui Chen, Jianming Zheng and Tao Hu

Bioengineering 2023, 10(8), 965; https://doi.org/10.3390/bioengineering10080965 - 15 Aug 2023

Cited by 1 | Viewed by 954

Abstract

Taking COVID-19 as an example, we know that a pandemic can have a huge impact on normal human life and the economy. Meanwhile, the population flow between countries and regions is the main factor affecting the changes in a pandemic, which is determined by the airline network. Therefore, realizing the overall control of airports is an effective way to control a pandemic. However, this is restricted by the differences in prevention and control policies in different areas and privacy issues, such as how a patient’s personal data from a medical center cannot be effectively combined with their passenger personal data. This prevents more precise airport control decisions from being made. To address this, this paper designed a novel data-sharing framework (i.e., PPChain) based on blockchain and federated learning. The experiment uses a CPU i7-12800HX and uses Docker to simulate multiple virtual nodes. The model is deployed to run on an NVIDIA GeForce GTX 3090Ti GPU. The experiment shows that the relationship between a pandemic and aircraft transport can be effectively explored by PPChain without sharing raw data. This approach does not require centralized trust and improves the security of the sharing process. The scheme can help formulate more scientific and rational prevention and control policies for the control of airports. Additionally, it can use aerial data to predict pandemics more accurately. Full article

(This article belongs to the Special Issue Machine Learning Technology in Biomedical Engineering)

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13 pages, 1135 KiB

Open AccessArticle

ClearF++: Improved Supervised Feature Scoring Using Feature Clustering in Class-Wise Embedding and Reconstruction

by Sehee Wang, So Yeon Kim and Kyung-Ah Sohn

Bioengineering 2023, 10(7), 824; https://doi.org/10.3390/bioengineering10070824 - 10 Jul 2023

Viewed by 815

Abstract

Feature selection methods are essential for accurate disease classification and identifying informative biomarkers. While information-theoretic methods have been widely used, they often exhibit limitations such as high computational costs. Our previously proposed method, ClearF, addresses these issues by using reconstruction error from low-dimensional embeddings as a proxy for the entropy term in the mutual information. However, ClearF still has limitations, including a nontransparent bottleneck layer selection process, which can result in unstable feature selection. To address these limitations, we propose ClearF++, which simplifies the bottleneck layer selection and incorporates feature-wise clustering to enhance biomarker detection. We compare its performance with other commonly used methods such as MultiSURF and IFS, as well as ClearF, across multiple benchmark datasets. Our results demonstrate that ClearF++ consistently outperforms these methods in terms of prediction accuracy and stability, even with limited samples. We also observe that employing the Deep Embedded Clustering (DEC) algorithm for feature-wise clustering improves performance, indicating its suitability for handling complex data structures with limited samples. ClearF++ offers an improved biomarker prioritization approach with enhanced prediction performance and faster execution. Its stability and effectiveness with limited samples make it particularly valuable for biomedical data analysis. Full article

(This article belongs to the Special Issue Machine Learning Technology in Biomedical Engineering)

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