Intelligent Computer-Aided Designs for Biomedical Applications

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

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 3171

Special Issue Editors

Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne 3011, Australia
Interests: biomedical signal processing; artificial intelligence (AI); data mining; detection of neurological diseases from EEGs; brain–computer interface (BCI)
Special Issues, Collections and Topics in MDPI journals
Centre for Health Research and School of Sciences, The University of Southern Queensland, Toowoomba, QLD 4350, Australia
Interests: big data analysis; data mining; signal processing; statistical modelling; AI and machine learning applications
Department of Electrical & Computer Engineering, Aarhus University, 8000 Aarhus, Denmark
Interests: EEG signal processing; data mining; detection of brain disorders from EEGs; brain–computer interface (BCI); AI
School of Architecture, Technology and Engineering, University of Brighton, Brighton BN2 4AT, UK
Interests: biomedical signal processing; artificial intelligence; pattern recognition; intelligent systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of technology, computer-aided diagnosis (CAD) has made significant advances in recent years. This technology performs clinical diagnoses and provides treatment recommendations. To increase the effectiveness of treatments, reduce the risk of false diagnoses, select appropriate courses of treatment, and predict the outcomes of many clinical scenarios, modern health research must take on the challenge of acquiring, analyzing, and applying vast amounts of biomedical data (such as signal data, image data, etc.). This Special Issue is intended to serve as a platform for the publication of articles that address significant issues related to both theoretical and practical CAD in biomedical applications.

Topics of interest for submissions to this Special Issue include, but are not limited to, the following:

  • Computer-aided diagnostic systems for identifying neurological diseases and disorders;
  • Biomedical signal and image processing and analysis;
  • Intelligent systems for mental disorder detection;
  • Medical data mining;
  • Artificial intelligence techniques for biomedical data analysis;
  • Health data modelling;
  • Knowledge engineering in medical data-driven research;
  • Applications of machine learning techniques and deep learning in biomedical engineering;
  • Pattern recognition;
  • Medical data capture;
  • Applications of neural networks in biomedicine;
  • Applications of AI methods to the brain–computer interface.

Dr. Siuly Siuly
Dr. Enamul Kabir
Dr. Smith Kashiram Khare
Dr. Muhammad Tariq Sadiq
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

  • computer-aided diagnostic systems
  • biomedical signal and image processing
  • medical data mining
  • artificial intelligence techniques for biomedical applications

Published Papers (2 papers)

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13 pages, 5429 KiB  
Article
Volumetric Imitation Generative Adversarial Networks for Anatomical Human Body Modeling
by Jion Kim, Yan Li and Byeong-Seok Shin
Bioengineering 2024, 11(2), 163; https://doi.org/10.3390/bioengineering11020163 - 07 Feb 2024
Viewed by 768
Abstract
Volumetric representation is a technique used to express 3D objects in various fields, such as medical applications. On the other hand, tomography images for reconstructing volumetric data have limited utilization because they contain personal information. Existing GAN-based medical image generation techniques can produce [...] Read more.
Volumetric representation is a technique used to express 3D objects in various fields, such as medical applications. On the other hand, tomography images for reconstructing volumetric data have limited utilization because they contain personal information. Existing GAN-based medical image generation techniques can produce virtual tomographic images for volume reconstruction while preserving the patient’s privacy. Nevertheless, these images often do not consider vertical correlations between the adjacent slices, leading to erroneous results in 3D reconstruction. Furthermore, while volume generation techniques have been introduced, they often focus on surface modeling, making it challenging to represent the internal anatomical features accurately. This paper proposes volumetric imitation GAN (VI-GAN), which imitates a human anatomical model to generate volumetric data. The primary goal of this model is to capture the attributes and 3D structure, including the external shape, internal slices, and the relationship between the vertical slices of the human anatomical model. The proposed network consists of a generator for feature extraction and up-sampling based on a 3D U-Net and ResNet structure and a 3D-convolution-based LFFB (local feature fusion block). In addition, a discriminator utilizes 3D convolution to evaluate the authenticity of the generated volume compared to the ground truth. VI-GAN also devises reconstruction loss, including feature and similarity losses, to converge the generated volumetric data into a human anatomical model. In this experiment, the CT data of 234 people were used to assess the reliability of the results. When using volume evaluation metrics to measure similarity, VI-GAN generated a volume that realistically represented the human anatomical model compared to existing volume generation methods. Full article
(This article belongs to the Special Issue Intelligent Computer-Aided Designs for Biomedical Applications)
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Review

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23 pages, 2325 KiB  
Review
Prediction of Gait Kinematics and Kinetics: A Systematic Review of EMG and EEG Signal Use and Their Contribution to Prediction Accuracy
by Nissrin Amrani El Yaakoubi, Caitlin McDonald and Olive Lennon
Bioengineering 2023, 10(10), 1162; https://doi.org/10.3390/bioengineering10101162 - 04 Oct 2023
Cited by 2 | Viewed by 1463
Abstract
Human-machine interfaces hold promise in enhancing rehabilitation by predicting and responding to subjects’ movement intent. In gait rehabilitation, neural network architectures utilize lower-limb muscle and brain activity to predict continuous kinematics and kinetics during stepping and walking. This systematic review, spanning five databases, [...] Read more.
Human-machine interfaces hold promise in enhancing rehabilitation by predicting and responding to subjects’ movement intent. In gait rehabilitation, neural network architectures utilize lower-limb muscle and brain activity to predict continuous kinematics and kinetics during stepping and walking. This systematic review, spanning five databases, assessed 16 papers meeting inclusion criteria. Studies predicted lower-limb kinematics and kinetics using electroencephalograms (EEGs), electromyograms (EMGs), or a combination with kinematic data and anthropological parameters. Long short-term memory (LSTM) and convolutional neural network (CNN) tools demonstrated highest accuracies. EEG focused on joint angles, while EMG predicted moments and torque joints. Useful EEG electrode locations included C3, C4, Cz, P3, F4, and F8. Vastus Lateralis, Rectus Femoris, and Gastrocnemius were the most commonly accessed muscles for kinematic and kinetic prediction using EMGs. No studies combining EEGs and EMGs to predict lower-limb kinematics and kinetics during stepping or walking were found, suggesting a potential avenue for future development in this technology. Full article
(This article belongs to the Special Issue Intelligent Computer-Aided Designs for Biomedical Applications)
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