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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 17429

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Special Issue Editors

Dr. Mohammed Abdelsamea

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Guest Editor

School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG , UK
Interests: biomedical image analysis; machine learning; deep learning; computer vision

Prof. Dr. Mohamed Medhat Gaber

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Guest Editor

Department of Computing and Data Science, College of Computing, Birmingham City University, Millennium Point, 1 Curzon Street, Birmingham B4 7XG, UK
Interests: artificial intelligence; data mining; data stream mining; machine learning; random forests
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Special Issue Information

Dear Colleagues,

Advances in deep learning and Artificial Intelligence (AI) have emerged as powerful tools for analysing biomedical data. AI has facilitated answering critical clinical questions at cellular and tissue levels of biological structures using both structured and unstructured data for different applications. This Special Issue aims to promote and critically discuss original and recent advances in AI (machine learning and deep learning) tools in biomedical science and to cover several applications such as diagnosis, prognosis, and treatment of disease. Teams are invited to present recent advances, challenges, and opportunities in biomedical science applications using AI. Submissions with valid clinical assessments and potential impact are strongly recommended. High-quality papers are welcome with topics that include, but are not limited to:

Development of deep learning architectures for biomedical data.
Biomedical signal and image processing and analysis.
Multimodal learning and prediction.
Biomedical image classification and segmentation.
Design diagnostic, prognostic, and grading applications using histology and clinicopathological data.
Explainability AI for biomedical data analysis.

Dr. Mohammed Abdelsamea
Prof. Dr. Mohamed Medhat Gaber
Guest Editors

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Keywords

machine and deep learning
digital pathology
biomedical image analysis
multimodal learning
explainability AI

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Published Papers (6 papers)

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17 pages, 2079 KiB

Open AccessArticle

Dataset and System Design for Orthopedic Walker Fall Detection and Activity Logging Using Motion Classification

by Maxwell Huang and Antony Garcia

Appl. Sci. 2023, 13(20), 11379; https://doi.org/10.3390/app132011379 - 17 Oct 2023

Cited by 2 | Viewed by 2718

Abstract

An accurate, economical, and reliable device for detecting falls in persons ambulating with the assistance of an orthopedic walker is crucially important for the elderly and patients with limited mobility. Existing wearable devices, such as wristbands, are not designed for walker users, and patients may not wear them at all times. This research proposes a novel idea of attaching an internet-of-things (IoT) device with an inertial measurement unit (IMU) sensor directly to an orthopedic walker to perform real-time fall detection and activity logging. A dataset is collected and labeled for walker users in four activities, including idle, motion, step, and fall. Classic machine learning algorithms are evaluated using the dataset by comparing their classification performance. Deep learning with a convolutional neural network (CNN) is also explored. Furthermore, the hardware prototype is designed by integrating a low-power microcontroller for onboard machine learning, an IMU sensor, a rechargeable battery, and Bluetooth wireless connectivity. The research results show the promise of improved safety and well-being of walker users. Full article

(This article belongs to the Special Issue The Applications of Machine Learning in Biomedical Science)

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15 pages, 2759 KiB

Open AccessArticle

An Identification Method of Feature Interpretation for Melanoma Using Machine Learning

by Zhenwei Li, Qing Ji, Xiaoli Yang, Yu Zhou and Shulong Zhi

Appl. Sci. 2023, 13(18), 10076; https://doi.org/10.3390/app131810076 - 7 Sep 2023

Cited by 2 | Viewed by 1941

Abstract

Melanoma is a fatal skin cancer that can be treated efficiently with early detection. There is a pressing need for dependable computer-aided diagnosis (CAD) systems to address this concern effectively. In this work, a melanoma identification method with feature interpretation was designed. The method included preprocessing, feature extraction, feature ranking, and classification. Initially, image quality was improved through preprocessing and k-means segmentation was used to identify the lesion area. The texture, color, and shape features of this region were then extracted. These features were further refined through feature recursive elimination (RFE) to optimize them for the classifiers. The classifiers, including support vector machine (SVM) with four kernels, logistic regression (LR), and Gaussian naive Bayes (GaussianNB) were applied. Additionally, cross-validation and 100 randomized experiments were designed to guarantee the generalization of the model. The experiments generated explainable feature importance rankings, and importantly, the model demonstrated robust performance across diverse datasets. Full article

(This article belongs to the Special Issue The Applications of Machine Learning in Biomedical Science)

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26 pages, 9087 KiB

Open AccessArticle

AHA-AO: Artificial Hummingbird Algorithm with Aquila Optimization for Efficient Feature Selection in Medical Image Classification

by Mohamed Abd Elaziz, Abdelghani Dahou, Shaker El-Sappagh, Alhassan Mabrouk and Mohamed Medhat Gaber

Appl. Sci. 2022, 12(19), 9710; https://doi.org/10.3390/app12199710 - 27 Sep 2022

Cited by 17 | Viewed by 3080

Abstract

This paper presents a system for medical image diagnosis that uses transfer learning (TL) and feature selection techniques. The main aim of TL on pre-trained models such as MobileNetV3 is to extract features from raw images. Here, a novel feature selection optimization algorithm called the Artificial Hummingbird Algorithm based on Aquila Optimization (AHA-AO) is proposed. The AHA-AO is used to select only the most relevant features and ensure the improvement of the overall model classification. Our methodology was evaluated using four datasets, namely, ISIC-2016, PH2, Chest-XRay, and Blood-Cell. We compared the proposed feature selection algorithm with five of the most popular feature selection optimization algorithms. We obtained an accuracy of 87.30% for the ISIC-2016 dataset, 97.50% for the PH2 dataset, 86.90% for the Chest-XRay dataset, and 88.60% for the Blood-cell dataset. The AHA-AO outperformed the other optimization techniques. Moreover, the developed AHA-AO was faster than the other feature selection models during the process of determining the relevant features. The proposed feature selection algorithm successfully improved the performance and the speed of the overall deep learning models. Full article

(This article belongs to the Special Issue The Applications of Machine Learning in Biomedical Science)

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16 pages, 1260 KiB

Open AccessArticle

Implementing an Ensemble Learning Model with Feature Selection to Predict Mortality among Patients Who Underwent Three-Vessel Percutaneous Coronary Intervention

by Yen-Chun Huang, Kuan-Yu Chen, Shao-Jung Li, Chih-Kuang Liu, Yang-Chao Lin and Mingchih Chen

Appl. Sci. 2022, 12(16), 8135; https://doi.org/10.3390/app12168135 - 14 Aug 2022

Cited by 4 | Viewed by 2271

Abstract

Coronary artery disease (CAD) is a common major disease. Revascularization with percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG) could relieve symptoms and myocardial ischemia. As the treatment improves and evolves, the number of aged patients with complex diseases and multiple comorbidities gradually increases. Furthermore, in patients with multivessel disease, 3-vessel PCI may lead to a higher risk of complications during the procedure, leading to further ischemia and higher long-term mortality than PCI for one vessel or two vessels. Nevertheless, the risk factors for accurately predicting patient mortality after 3-vessel PCI are unclear. Thus, a new risk prediction model for primary PCI (PPCI) patients’ needs to be established to help physicians and patients make decisions more quickly and accurately. This research aimed to construct a prediction model and find which risk factors will affect mortality in 3-vessel PPCI patients. This nationwide population-based cohort study crossed multiple hospitals and selected 3-vessel PPCI patients from January 2007 to December 2009. Then five different single machine learning methods were applied to select significant predictors and implement ensemble models to predict the mortality rate. Of the 2337 patients who underwent 3-vessel PPCI, a total of 1188 (50.83%) survived and 1149 (49.17%) died. Age, congestive heart failure (CHF), and chronic renal failure (CRF) are mortality’s most important variables. When CRF patients accept 3-vessel PPCI at ages between 68–75, they will possibly have a 94% death rate; Furthermore, this study used the top 15 variables averaged by each machine learning method to make a prediction model, and the ensemble learning model can accurately predict the long-term survival of 3-vessel PPCI patients, the accurate predictions rate achieved in 88.7%. Prediction models can provide helpful information for the clinical physician and enhance clinical decision-making. Furthermore, it can help physicians quickly identify the risk features, design clinical trials, and allocate hospital resources effectively. Full article

(This article belongs to the Special Issue The Applications of Machine Learning in Biomedical Science)

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16 pages, 1830 KiB

Open AccessArticle

Analysis of Heart Rate Variability and Game Performance in Normal and Cognitively Impaired Elderly Subjects Using Serious Games

by Chun-Ju Hou, Yen-Ting Chen, Mycel A. Capilayan, Min-Wei Huang and Ji-Jer Huang

Appl. Sci. 2022, 12(9), 4164; https://doi.org/10.3390/app12094164 - 20 Apr 2022

Cited by 6 | Viewed by 2608

Abstract

Cognitive decline is one of the primary concerns in the elderly population. Serious games have been used for different purposes related to elderly care, such as physical therapy, cognitive training and mood management. There has been scientific evidence regarding the relationship between cognition and the autonomic nervous system (ANS) through heart rate variability (HRV). This paper explores the changes in the ANS among elderly people of normal and impaired cognition through measured HRV. Forty-eight subjects were classified into two groups: normal cognition (NC) (n = 24) and mild cognitive impairment (MCI) (n = 24). The subjects went through the following experiment flow: rest for 3 min (Rest 1), play a cognitive aptitude game (Game 1), rest for another 3 min (Rest 2), then play two reaction-time games (Game 2&3). Ten HRV features were extracted from measured electrocardiography (ECG) signals. Based on statistical analysis, there was no significant difference on the HRV between the two groups, but the experiment sessions do have a significant effect. There was no significant interaction between sessions and cognitive status. This implies that the HRV between the two groups have no significant difference, and they will experience similar changes in their HRV regardless of their cognitive status. Based on the game performance, there was a significant difference between the two groups of elderly people. Tree-based pipeline optimization tool (TPOT) was used for generating a machine learning pipeline for classification. Classification accuracy of 68.75% was achieved using HRV features, but higher accuracies of 83.33% and 81.20% were achieved using game performance or both HRV and game performance features, respectively. These results show that HRV has the potential to be used for detection of mild cognition impairment, but game performance can yield better accuracy. Thus, serious games have the potential to be used for assessing cognitive decline among the elderly. Full article

(This article belongs to the Special Issue The Applications of Machine Learning in Biomedical Science)

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14 pages, 466 KiB

Open AccessSystematic Review

Machine Learning Approaches to Intracranial Pressure Prediction in Patients with Traumatic Brain Injury: A Systematic Review

by George R. E. Bradley, María Roldán and Panayiotis A. Kyriacou

Appl. Sci. 2023, 13(14), 8015; https://doi.org/10.3390/app13148015 - 9 Jul 2023

Cited by 1 | Viewed by 2348

Abstract

Purpose: Intracranial pressure (ICP) monitoring is a “gold standard” monitoring modality for severe traumatic brain injury (TBI) patients. The capacity to predict ICP crises could further minimise the rate of secondary brain injury and improve the outcomes of TBI patients by facilitating timely intervention prior to a potential crisis. This systematic review sought (i) to identify the most efficacious approaches to the prediction of ICP crises within TBI patients, (ii) to access the clinical suitability of existing predictive models and (iii) to suggest potential areas for future research. Methods: Peer-reviewed primary diagnostic accuracy studies, assessing the performance of ICP crisis prediction methods within TBI patients, were included. The QUADAS-2 tool was used to evaluate the quality of the studies. Results: Three optimal solutions to predicting the ICP crisis were identified: a long short-term memory (LSTM) model, a Gaussian processes (GP) approach and a logistic regression model. These approaches performed with an area under the receiver operating characteristics curve (AUC-ROC) ranging from 0.86 to 0.95. Conclusions: The review highlights the existing disparity of the definition of an ICP crisis and what prediction horizon is the most clinically relevant. Moreover, this review draws attention to the existing lack of focus on the clinical intelligibility of algorithms, the measure of how algorithms improve patient care and how algorithms may raise ethical, legal or social concerns. The review was registered with the International Prospective Register of Systematic Reviews (PROSPERO) (ID: CRD42022314278). Full article

(This article belongs to the Special Issue The Applications of Machine Learning in Biomedical Science)

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