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Bioengineering
Special Issues
Recent Progress of Deep Learning in Healthcare
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Recent Progress of Deep Learning in Healthcare

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

Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 5877

Special Issue Editor

Dr. Taiyu Zhu

E-Mail Website
Guest Editor
Department of Psychiatry, University of Oxford, Oxford, UK
Interests: artificial intelligence; deep learning; cardiometabolic disease; medical devices; genetics

Special Issue Information

Dear Colleagues,

The field of healthcare stands at the cusp of a transformation, driven by the rapid advancements in artificial intelligence. Deep learning, a subset of machine learning, has shown unprecedented success in interpreting complex data, making it a potent tool for clinical decision support, medical diagnostics, treatment planning, and disease prediction. By analyzing complex medical data, these algorithms enhance the management of chronic disease, improve diagnostic accuracy, and aid in early disease detection. They also revolutionize personalized medicine, tailoring treatments to individual genetic and health profiles. In drug development, deep learning accelerates the discovery and testing of new drugs. Additionally, it supports clinicians with decision-making tools and powers health monitoring wearables, offering real-time insights into patient health. This technology is not just transforming how healthcare is delivered, but also improving patient outcomes, making healthcare more accessible, effective, and personalized.

This Special Issue aims to provide a comprehensive overview of the current state and future potential of deep learning in healthcare. It features pioneering research demonstrating the use of these algorithms in applications to complex diseases, such as diabetes, dementia, and cardiovascular diseases, offering insights into the innovations that are set to redefine healthcare in the coming years.

Dr. Taiyu Zhu
Guest Editor

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

  • deep learning
  • personalized medicine
  • digital health
  • bioinformatics
  • genomic data analysis

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Published Papers (3 papers)

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Research

16 pages, 4743 KiB  
Article
NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections via Neural Implicit Representation
by Yiying Wang, Abhirup Banerjee and Vicente Grau
Bioengineering 2024, 11(12), 1227; https://doi.org/10.3390/bioengineering11121227 - 4 Dec 2024
Cited by 1 | Viewed by 1540
Abstract
Cardiovascular diseases (CVDs) are the most common health threats worldwide. 2D X-ray invasive coronary angiography (ICA) remains the most widely adopted imaging modality for CVD assessment during real-time cardiac interventions. However, it is often difficult for the cardiologists to interpret the 3D geometry [...] Read more.
Cardiovascular diseases (CVDs) are the most common health threats worldwide. 2D X-ray invasive coronary angiography (ICA) remains the most widely adopted imaging modality for CVD assessment during real-time cardiac interventions. However, it is often difficult for the cardiologists to interpret the 3D geometry of coronary vessels based on 2D planes. Moreover, due to the radiation limit, often only two angiographic projections are acquired, providing limited information of the vessel geometry and necessitating 3D coronary tree reconstruction based only on two ICA projections. In this paper, we propose a self-supervised deep learning method called NeCA, which is based on neural implicit representation using the multiresolution hash encoder and differentiable cone-beam forward projector layer, in order to achieve 3D coronary artery tree reconstruction from two 2D projections. We validate our method using six different metrics on a dataset generated from coronary computed tomography angiography of right coronary artery and left anterior descending artery. The evaluation results demonstrate that our NeCA method, without requiring 3D ground truth for supervision or large datasets for training, achieves promising performance in both vessel topology and branch-connectivity preservation compared to the supervised deep learning model. Full article
(This article belongs to the Special Issue Recent Progress of Deep Learning in Healthcare)
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16 pages, 17310 KiB  
Article
Machine Learning and Optical-Coherence-Tomography-Derived Radiomics Analysis to Predict the Postoperative Anatomical Outcome of Full-Thickness Macular Hole
by Yuqian Hu, Yongan Meng, Youling Liang, Yiwei Zhang, Biying Chen, Jianing Qiu, Zhishang Meng and Jing Luo
Bioengineering 2024, 11(9), 949; https://doi.org/10.3390/bioengineering11090949 - 22 Sep 2024
Viewed by 1560
Abstract
Full-thickness macular hole (FTMH) leads to central vision loss. It is essential to identify patients with FTMH at high risk of postoperative failure accurately to achieve anatomical closure. This study aimed to construct a predictive model for the anatomical outcome of FTMH after [...] Read more.
Full-thickness macular hole (FTMH) leads to central vision loss. It is essential to identify patients with FTMH at high risk of postoperative failure accurately to achieve anatomical closure. This study aimed to construct a predictive model for the anatomical outcome of FTMH after surgery. A retrospective study was performed, analyzing 200 eyes from 197 patients diagnosed with FTMH. Radiomics features were extracted from optical coherence tomography (OCT) images. Logistic regression, support vector machine (SVM), and backpropagation neural network (BPNN) classifiers were trained and evaluated. Decision curve analysis and survival analysis were performed to assess the clinical implications. Sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC) were calculated to assess the model effectiveness. In the training set, the AUC values were 0.998, 0.988, and 0.995, respectively. In the test set, the AUC values were 0.941, 0.943, and 0.968, respectively. The OCT-omics scores were significantly higher in the “Open” group than in the “Closed” group and were positively correlated with the minimum diameter (MIN) and base diameter (BASE) of FTMH. Therefore, in this study, we developed a model with robust discriminative ability to predict the postoperative anatomical outcome of FTMH. Full article
(This article belongs to the Special Issue Recent Progress of Deep Learning in Healthcare)
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Graphical abstract

16 pages, 4571 KiB  
Article
DiffPhys: Enhancing Signal-to-Noise Ratio in Remote Photoplethysmography Signal Using a Diffusion Model Approach
by Shutao Chen, Kwan-Long Wong, Jing-Wei Chin, Tsz-Tai Chan and Richard H. Y. So
Bioengineering 2024, 11(8), 743; https://doi.org/10.3390/bioengineering11080743 - 23 Jul 2024
Cited by 2 | Viewed by 2243
Abstract
Remote photoplethysmography (rPPG) is an emerging non-contact method for monitoring cardiovascular health based on facial videos. The quality of the captured videos largely determines the efficacy of rPPG in this application. Traditional rPPG techniques, while effective for heart rate (HR) estimation, often produce [...] Read more.
Remote photoplethysmography (rPPG) is an emerging non-contact method for monitoring cardiovascular health based on facial videos. The quality of the captured videos largely determines the efficacy of rPPG in this application. Traditional rPPG techniques, while effective for heart rate (HR) estimation, often produce signals with an inadequate signal-to-noise ratio (SNR) for reliable vital sign measurement due to artifacts like head motion and measurement noise. Another pivotal factor is the overlooking of the inherent properties of signals generated by rPPG (rPPG-signals). To address these limitations, we introduce DiffPhys, a novel deep generative model particularly designed to enhance the SNR of rPPG-signals. DiffPhys leverages the conditional diffusion model to learn the distribution of rPPG-signals and uses a refined reverse process to generate rPPG-signals with a higher SNR. Experimental results demonstrate that DiffPhys elevates the SNR of rPPG-signals across within-database and cross-database scenarios, facilitating the extraction of cardiovascular metrics such as HR and HRV with greater precision. This enhancement allows for more accurate monitoring of health conditions in non-clinical settings. Full article
(This article belongs to the Special Issue Recent Progress of Deep Learning in Healthcare)
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