Submit to Special Issue Submit Abstract to Special Issue Review for Electronics Propose a Special Issue

Journal Browser

► Journal Browser

Artificial Intelligence Technologies for Biomedicine and Healthcare Applications

Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: 15 June 2024 | Viewed by 14139

Share This Special Issue

Special Issue Editors

Prof. Dr. Andres Hernandez-Matamoros

E-Mail Website
Guest Editor

Department of Frontier Media Science, Meiji University, Tokyo 164-8525, Japan
Interests: bioinformatics; data privacy; machine learning; deep learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Tun-Wen Pai

E-Mail Website
Guest Editor

Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Interests: biomedical informatics; functional genomics; immunoinformatics; medical imaging analysis; medical data analysis; biomarker detection; systems biology; bio-signal analysis; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hamido Fujita

E-Mail Website
Guest Editor

Regional Research Center, Iwate Prefectural University, Iwate 020-8550, Japan
Interests: applied intelligence; machine learning for health care; granular computing; health care prediction; three-way decision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to publish your research findings on new Artificial Intelligence Technologies for Biomedicine and Healthcare Applications. The application of artificial intelligence (AI) technologies in biomedicine and healthcare has been rising, and these techniques are crucial in the development of cost–benefit healthcare systems.

Machine learning and deep learning are still new to many medical researchers, representing a considerable obstacle for medical researchers to understand and use to these new tools.

This Special Issue aims to publish research papers explaining and discussing the viability of machine learning and deep learning technologies, in addition to deepening our knowledge and furthering the adaptation of these artificial intelligence methods to address medical research challenges.

Toward this end, we invite the submission of articles that build bridges between machine learning and deep learning research and its medical applications.

Prof. Dr. Andres Hernandez-Matamoros
Prof. Dr. Tun-Wen Pai
Prof. Dr. Hamido Fujita
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

deep learning in health care
machine learning in medical diagnosis
medical image analysis
biomedical applications
physiological signals
pattern recognition for medical systems
COVID-19 analytics
signal processing in health care analysis
early predictions in medical applications

Published Papers (10 papers)

Download All Papers

Research

16 pages, 2623 KiB

Open AccessArticle

Using Data Augmentation to Improve the Accuracy of Blood Pressure Measurement Based on Photoplethysmography

by Hanlin Mou, Congjian Li, Haoran Zhou, Daobing Zhang, Wensheng Wang, Junsheng Yu and Jing Tian

Electronics 2024, 13(8), 1599; https://doi.org/10.3390/electronics13081599 - 22 Apr 2024

Viewed by 390

Abstract

Convenient and accurate blood pressure (BP) measurement is of great importance in both clinical and daily life. Although deep learning (DL) can achieve cuff-less BP measurement based on Photoplethysmography (PPG), the performance of DL is affected by few-shot data. Data augmentation becomes an effective way to enhance the size of the training data. In this paper, we use cropping, flipping, DTW barycentric averaging (DBA), generative adversarial network (GAN) and variational auto-encoder (VAE) for the data augmentation of PPG. Furthermore, a PE–CNN–GRU model is designed for cuff-less BP measurement applying position encoding (PE), convolutional neural networks (CNNs) and gated recurrent unit (GRU) networks. Experiment results based on real-life datasets show that VAE is the most suitable method for PPG data augmentation, which can reduce the mean absolute error (MAE) of PE–CNN–GRU when measuring systolic blood pressure (SBP) and diastolic blood pressure (DBP) by 18.80% and 19.84%. After the data augmentation of PPG, PE–CNN–GRU achieves accurate and cuff-less BP measurement, thus providing convenient support for preventing cardiovascular diseases. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Biomedicine and Healthcare Applications)

► Show Figures

Figure 1

11 pages, 1471 KiB

Open AccessArticle

Material Point Method-Based Simulation Techniques for Medical Applications

by Su-Kyung Sung, Jae-Hyeong Kim and Byeong-Seok Shin

Electronics 2024, 13(7), 1340; https://doi.org/10.3390/electronics13071340 - 02 Apr 2024

Viewed by 498

Abstract

We propose a method for recognizing fragment objects to model the detailed tearing of elastic objects like human organs. Traditional methods require high-performance GPUs for real-time calculations to accurately simulate the detailed fragmentation of rapidly deforming objects or create random fragments to improve visual effects with minimal computation. The proposed method utilizes a deep neural network (DNN) to produce physically accurate results without requiring high-performance GPUs. Physically parameterized material point method (MPM) simulation data were used to learn small-scale detailed fragments. The tearing process is segmented and learned based on various training data from different spaces and external forces. The inference algorithm classifies the fragments from the training data and modifies the deformation gradient using a modifier. An experiment was conducted to compare the proposed method and the traditional MPM in the same environment. As a result, it was confirmed that visual fidelity for the tearing of elastic objects has been improved. This supports the simulation of various incision types in a virtual surgery. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Biomedicine and Healthcare Applications)

► Show Figures

Figure 1

15 pages, 2775 KiB

Open AccessArticle

VF-Mask-Net: A Visual Field Noise Reduction Method Using Neural Networks

by Zhenyu Zhang, Haogang Zhu and Lei Li

Electronics 2024, 13(3), 646; https://doi.org/10.3390/electronics13030646 - 04 Feb 2024

Viewed by 475

Abstract

Visual Field (VF) measurements, crucial for diagnosing and treating glaucoma, often contain noise originating from both the instrument and subjects during the response process. This study proposes a neural network-based denoising method for VF data, obviating the need for ground truth labels or paired measurements. Using a mask-imposed VF as an input for the neural network, while the original VF serves as a training label, we evaluated performance metrics such as the accuracy, precision, and sensitivity of denoised VFs. Orthogonal experiments were also employed to assess the impact of mask number, mask structure, and replacement strategy on model accuracy. This study reveals that mask number, replacement strategy, and their interaction significantly affect the accuracy of the denoising model. Under recommended parameters, VF-Mask-Net effectively enhances the accuracy and precision of VF measurements. Furthermore, in deterioration detection tasks, denoised VFs display heightened sensitivity compared to their pre-denoising counterparts. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Biomedicine and Healthcare Applications)

► Show Figures

Figure 1

27 pages, 1233 KiB

Open AccessArticle

Learning Spatio-Temporal Radon Footprints for Assessment of Parkinson’s Dyskinesia

by Paraskevi Antonia Theofilou, Georgios Tsatiris and Stefanos Kollias

Electronics 2024, 13(3), 635; https://doi.org/10.3390/electronics13030635 - 02 Feb 2024

Viewed by 538

Abstract

Parkinson’s disease is a severe neurodegenerative disorder that leads to loss of control over various motor and mental functions. Its progression can be limited with medication, particularly through the use of levodopa. However, prolonged administration of levodopa often results in disorders independent of those caused by the disease. The detection of these disorders is based on the clinical examination of patients, through different type of activities and tasks, using the Unified Dyskinesia Rating Scale (UDysRS). In the present work, our aim is to develop a state-of-the-art assessment system for levodopa-induced dyskinesia (LID) using the joint coordinate data of a human skeleton body depicted on videotaped activities related to UDysRS. For this reason, we combine a robust mathematical method for encoding action sequences known as Spatio-temporal Radon Footprints (SRF) with a Convolutional Neural Network (CNN), in order to estimate dyskinesia’s ratings for six body parts. We introduce two different methodological approaches: Global SRF-CNN and Local SRF-CNN, based on the set of skeletal points used in the encoding scheme. A comparison between these approaches reveals that Local SRF-CNN demonstrates better performance than the Global one. Finally, Local SRF-CNN outperforms the state-of-the-art technique, on both tasks, for UDysRS dyskinesia assessment, using joint coordinate data of the human body, achieving an overall performance in mean RMSE value of 0.6198 for Drinking task and 0.4885 for Communication, compared to 0.6575 and 0.5175, respectively. This illustrates the ability of the proposed machine learning system to successfully assess LID. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Biomedicine and Healthcare Applications)

► Show Figures

Figure 1

15 pages, 4767 KiB

Open AccessArticle

FDA-Approved Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices: An Updated Landscape

by Geeta Joshi, Aditi Jain, Shalini Reddy Araveeti, Sabina Adhikari, Harshit Garg and Mukund Bhandari

Electronics 2024, 13(3), 498; https://doi.org/10.3390/electronics13030498 - 24 Jan 2024

Cited by 9 | Viewed by 5697

Abstract

As artificial intelligence (AI) has been highly advancing in the last decade, machine learning (ML)-enabled medical devices are increasingly used in healthcare. In this study, we collected publicly available information on AI/ML-enabled medical devices approved by the FDA in the United States, as of the latest update on 19 October 2023. We performed comprehensive analysis of a total of 691 FDA-approved artificial intelligence and machine learning (AI/ML)-enabled medical devices and offer an in-depth analysis of clearance pathways, approval timeline, regulation type, medical specialty, decision type, recall history, etc. We found a significant surge in approvals since 2018, with clear dominance of the radiology specialty in the application of machine learning tools, attributed to the abundant data from routine clinical data. The study also reveals a reliance on the 510(k)-clearance pathway, emphasizing its basis on substantial equivalence and often bypassing the need for new clinical trials. Also, it notes an underrepresentation of pediatric-focused devices and trials, suggesting an opportunity for expansion in this demographic. Moreover, the geographical limitation of clinical trials, primarily within the United States, points to a need for more globally inclusive trials to encompass diverse patient demographics. This analysis not only maps the current landscape of AI/ML-enabled medical devices but also pinpoints trends, potential gaps, and areas for future exploration, clinical trial practices, and regulatory approaches. In conclusion, our analysis sheds light on the current state of FDA-approved AI/ML-enabled medical devices and prevailing trends, contributing to a wider comprehension. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Biomedicine and Healthcare Applications)

► Show Figures

Figure 1

24 pages, 7188 KiB

Open AccessArticle

Healthcare Big Data Analysis with Artificial Neural Network for Cardiac Disease Prediction

by Sulagna Mohapatra, Prasan Kumar Sahoo and Suvendu Kumar Mohapatra

Electronics 2024, 13(1), 163; https://doi.org/10.3390/electronics13010163 - 29 Dec 2023

Viewed by 788

Abstract

The generation of a huge volume of structured, semi-structured and unstructured real-time health monitoring data and its storage in the form of electronic health records (EHRs) need to be processed and analyzed intelligently to provide timely healthcare. A big data analytic platform is an alternative to the traditional warehouse paradigms for the processing, analysis and storage of the tremendous volume of healthcare data. However, the manual analysis of these voluminous, multi-variate patients data is tedious and error-prone. Therefore, an intelligent solution method is highly essential to perform multiple correlation analyses for disease diagnosis and prediction. In this paper, first, a structural framework is proposed to process the huge volume of cardiological big data generated from the hospital and patients. Then, an intelligent analytical model for the cardiological big data analysis is proposed by combining the concept of artificial neural network (ANN) and particle swarm optimization (PSO) to predict the abnormalities in the cardiac health of a person. In the proposed cardiac disease prediction model, an extensive electrocardiogram (ECG) data analysis method is developed to identify the probable normal and abnormal cardiac feature points. Simulation results show the effects of a number of attributes for improving the accuracy of the cardiac disease prediction and data processing time in the cloud with an increase in the number of the cardiac patients. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Biomedicine and Healthcare Applications)

► Show Figures

Figure 1

16 pages, 575 KiB

Open AccessArticle

A Knowledge-Enhanced Hierarchical Reinforcement Learning-Based Dialogue System for Automatic Disease Diagnosis

by Ying Zhu, Yameng Li, Yuan Cui, Tianbao Zhang, Daling Wang, Yifei Zhang and Shi Feng

Electronics 2023, 12(24), 4896; https://doi.org/10.3390/electronics12244896 - 05 Dec 2023

Viewed by 828

Abstract

Deep Reinforcement Learning is a key technology for the diagnosis-oriented medical dialogue system, determining the type of disease according to the patient’s utterances. The existing dialogue models for disease diagnosis cannot achieve good performance due to the large number of symptoms and diseases. In this paper, we propose a knowledge-enhanced hierarchical reinforcement learning model for strategy learning in the medical dialogue system for disease diagnosis. Our hierarchical strategy alleviates the problem of a large action space in reinforcement learning. In addition, the knowledge enhancement module integrates a learnable disease–symptom relationship matrix and medical knowledge graph into the hierarchical strategy for higher diagnosis success rate. Our proposed model has been proved to be effective on a medical dialogue dataset for automatic disease diagnosis. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Biomedicine and Healthcare Applications)

► Show Figures

Figure 1

19 pages, 797 KiB

Open AccessArticle

Utilizing Fractional Artificial Neural Networks for Modeling Cancer Cell Behavior

by Reza Behinfaraz, Amir Aminzadeh Ghavifekr, Roberto De Fazio and Paolo Visconti

Electronics 2023, 12(20), 4245; https://doi.org/10.3390/electronics12204245 - 13 Oct 2023

Viewed by 874

Abstract

In this paper, a novel approach involving a fractional recurrent neural network (RNN) is proposed to achieve the observer-based synchronization of a cancer cell model. According to the properties of recurrent neural networks, our proposed framework serves as a predictive method for the behavior of fractional-order chaotic cancer systems with uncertain orders. Through a stability analysis of weight updating laws, we design a fractional-order Nonlinear Autoregressive with Exogenous Inputs (NARX) network, in which its learning algorithm demonstrates admissible and faster convergence. The main contribution of this paper lies in the development of a fractional neural observer for the fractional-order cancer systems, which is robust in the presence of uncertain orders. The proposed fractional-order model for cancer can capture complex and nonlinear behaviors more accurately than traditional integer-order models. This improved accuracy can provide a more realistic representation of cancer dynamics. Simulation results are presented to demonstrate the effectiveness of the proposed method, where mean square errors of synchronization by applying integer and fractional weight matrix laws are calculated. The density of tumor cell, density of healthy host cell and density of effector immune cell errors for the observer-based synchronization of fractional-order (OSFO) cancer system are less than 0.0.0048, 0.0062 and 0.0068, respectively. Comparative tables are provided to validate the improved accuracy achieved by the proposed framework. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Biomedicine and Healthcare Applications)

► Show Figures

Figure 1

17 pages, 2462 KiB

Open AccessArticle

Deep Learning–Based Segmentation of Trypanosoma cruzi Nests in Histopathological Images

by Nidiyare Hevia-Montiel, Paulina Haro, Leonardo Guillermo-Cordero and Jorge Perez-Gonzalez

Electronics 2023, 12(19), 4144; https://doi.org/10.3390/electronics12194144 - 05 Oct 2023

Cited by 1 | Viewed by 878

Abstract

The use of artificial intelligence has shown good performance in the medical imaging area, in particular the deep learning methods based on convolutional neural networks for classification, detection, and/or segmentation tasks. The task addressed in this research work is the segmentation of amastigote nests from histological microphotographs in the study of Trypanosoma cruzi infection (Chagas disease) implementing a U-Net convolutional network architecture. For the nests’ segmentation, a U-Net architecture was trained on histological images of an acute-stage murine experimental model performing a 5-fold cross-validation, while the final tests were carried out with data unseen by the U-Net from three image groups of different experimental models. During the training stage, the obtained results showed an average accuracy of 98.19 ± 0.01, while in the case of the final tests, an average accuracy of 99.9 ± 0.1 was obtained for the control group, as well as 98.8 ± 0.9 and 99.1 ± 0.8 for two infected groups; in all cases, high sensitivity and specificity were observed in the results. We can conclude that the use of a U-Net architecture proves to be a relevant tool in supporting the diagnosis and analysis of histological images for the study of Chagas disease. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Biomedicine and Healthcare Applications)

► Show Figures

Figure 1

15 pages, 478 KiB

Open AccessArticle

Ischemic Stroke Lesion Segmentation Using Mutation Model and Generative Adversarial Network

by Rawan Ghnemat, Ashwaq Khalil and Qasem Abu Al-Haija

Electronics 2023, 12(3), 590; https://doi.org/10.3390/electronics12030590 - 25 Jan 2023

Cited by 7 | Viewed by 1877

Abstract

Ischemic stroke lesion segmentation using different types of images, such as Computed Tomography Perfusion (CTP), is important for medical and Artificial intelligence fields. These images are potential resources to enhance machine learning and deep learning models. However, collecting these types of images is a considerable challenge. Therefore, new augmentation techniques are required to handle the lack of collected images presenting Ischemic strokes. In this paper, the proposed model of mutation model using a distance map is integrated into the generative adversarial network (GAN) to generate a synthetic dataset. The Euclidean distance is used to compute the average distance of each pixel with its neighbor in the right and bottom directions. Then a threshold is used to select the adjacent locations with similar intensities for the mutation process. Furthermore, semi-supervised GAN is enhanced and transformed into supervised GAN, where the segmentation and discriminator are shared the same convolution neural network to reduce the computation process. The mutation and GAN models are trained as an end-to-end model. The results show that the mutation model enhances the dice coefficient of the proposed GAN model by 2.54%. Furthermore, it slightly enhances the recall of the proposed GAN model compared to other GAN models. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Biomedicine and Healthcare Applications)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Artificial Intelligence Technologies for Biomedicine and Healthcare Applications

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (10 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI