AI and Big Data Research in Biomedical Engineering

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

Deadline for manuscript submissions: 20 August 2024 | Viewed by 7900

Special Issue Editor


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Guest Editor
School of Medicine, SKKU School of Medicine, Suwon 16419, Republic of Korea
Interests: medical AI; medical big data; digital health

Special Issue Information

Dear Colleagues,

The intersection of Artificial Intelligence (AI) and Big Data with Biomedical Engineering heralds a transformative era in modern healthcare. As AI methodologies mature and data reservoirs grow, their collaborative impact is rapidly reshaping the foundational landscapes of diagnostics, therapeutics, and patient care. The immense potential of AI algorithms, when combined with vast datasets, can pave the way for more accurate diagnoses, personalized treatments, and even predictive healthcare. While the union of AI and Big Data promises profound advancements, it also introduces novel challenges related to data privacy, algorithmic biases, and integration with existing medical frameworks. The time is ripe to address these challenges, to harness the full potential of this convergence and to ensure a future in which technology and medicine walk hand in hand for enhanced patient outcomes. This Special Issue of Bioengineering, entitled "AI and Big Data Researches on Biomedical Engineering", aims to capture the essence of this exciting juncture. We seek contributions from global pioneers in the realms of AI-driven algorithms, big data analytics in healthcare, integrative biomedical platforms, and more. By presenting a tapestry of expert opinions, research, and innovative solutions, this Special Issue strives to chart the way forward in this burgeoning domain.

Dr. Seung Won Lee
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

  • medical AI
  • medical big data
  • digital health

Published Papers (3 papers)

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Research

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13 pages, 1176 KiB  
Article
Joint Representation Learning for Retrieval and Annotation of Genomic Interval Sets
by Erfaneh Gharavi, Nathan J. LeRoy, Guangtao Zheng, Aidong Zhang, Donald E. Brown and Nathan C. Sheffield
Bioengineering 2024, 11(3), 263; https://doi.org/10.3390/bioengineering11030263 - 8 Mar 2024
Viewed by 882
Abstract
As available genomic interval data increase in scale, we require fast systems to search them. A common approach is simple string matching to compare a search term to metadata, but this is limited by incomplete or inaccurate annotations. An alternative is to compare [...] Read more.
As available genomic interval data increase in scale, we require fast systems to search them. A common approach is simple string matching to compare a search term to metadata, but this is limited by incomplete or inaccurate annotations. An alternative is to compare data directly through genomic region overlap analysis, but this approach leads to challenges like sparsity, high dimensionality, and computational expense. We require novel methods to quickly and flexibly query large, messy genomic interval databases. Here, we develop a genomic interval search system using representation learning. We train numerical embeddings for a collection of region sets simultaneously with their metadata labels, capturing similarity between region sets and their metadata in a low-dimensional space. Using these learned co-embeddings, we develop a system that solves three related information retrieval tasks using embedding distance computations: retrieving region sets related to a user query string, suggesting new labels for database region sets, and retrieving database region sets similar to a query region set. We evaluate these use cases and show that jointly learned representations of region sets and metadata are a promising approach for fast, flexible, and accurate genomic region information retrieval. Full article
(This article belongs to the Special Issue AI and Big Data Research in Biomedical Engineering)
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19 pages, 7992 KiB  
Article
A Deep Learning Approach for Improving Two-Photon Vascular Imaging Speeds
by Annie Zhou, Samuel A. Mihelic, Shaun A. Engelmann, Alankrit Tomar, Andrew K. Dunn and Vagheesh M. Narasimhan
Bioengineering 2024, 11(2), 111; https://doi.org/10.3390/bioengineering11020111 - 24 Jan 2024
Viewed by 1016
Abstract
A potential method for tracking neurovascular disease progression over time in preclinical models is multiphoton fluorescence microscopy (MPM), which can image cerebral vasculature with capillary-level resolution. However, obtaining high-quality, three-dimensional images with traditional point scanning MPM is time-consuming and limits sample sizes for [...] Read more.
A potential method for tracking neurovascular disease progression over time in preclinical models is multiphoton fluorescence microscopy (MPM), which can image cerebral vasculature with capillary-level resolution. However, obtaining high-quality, three-dimensional images with traditional point scanning MPM is time-consuming and limits sample sizes for chronic studies. Here, we present a convolutional neural network-based (PSSR Res-U-Net architecture) algorithm for fast upscaling of low-resolution or sparsely sampled images and combine it with a segmentation-less vectorization process for 3D reconstruction and statistical analysis of vascular network structure. In doing so, we also demonstrate that the use of semi-synthetic training data can replace the expensive and arduous process of acquiring low- and high-resolution training pairs without compromising vectorization outcomes, and thus open the possibility of utilizing such approaches for other MPM tasks where collecting training data is challenging. We applied our approach to images with large fields of view from a mouse model and show that our method generalizes across imaging depths, disease states and other differences in neurovasculature. Our pretrained models and lightweight architecture can be used to reduce MPM imaging time by up to fourfold without any changes in underlying hardware, thereby enabling deployability across a range of settings. Full article
(This article belongs to the Special Issue AI and Big Data Research in Biomedical Engineering)
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Review

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21 pages, 3292 KiB  
Review
How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications
by Luís Pinto-Coelho
Bioengineering 2023, 10(12), 1435; https://doi.org/10.3390/bioengineering10121435 - 18 Dec 2023
Cited by 6 | Viewed by 5526
Abstract
The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The [...] Read more.
The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways. Full article
(This article belongs to the Special Issue AI and Big Data Research in Biomedical Engineering)
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