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Bioengineering
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Artificial Intelligence (AI) in Biomedicine
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Artificial Intelligence (AI) in Biomedicine

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 5620

Special Issue Editors

Prof. Dr. Jian Wu

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Interests: medical artificial intelligence; medical imaging; big data; data mining
Dr. Hongxia Xu

E-Mail Website
Guest Editor
Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, China
Interests: bioinformatics; novel drug delivery system; precision cancer treatment

Special Issue Information

Dear Colleagues,

It is with great excitement that we introduce this Special Issue, "Artificial Intelligence (AI) in Biomedicine," a pivotal conversation on the evolving synergy of AI and biomedicine. In this Special Issue, we bring the spotlight onto the vital role of medical artificial intelligence, particularly its capacity to facilitate quantitative analysis and revolutionize healthcare delivery. The increasing sophistication of medical imaging, image segmentation, and predictive computer simulations, propelled by AI advancements, has redefined therapeutic paradigms. These tools have led to increased precision in patient-specific diagnostics and individualized surgical planning, demonstrating the transformative potential of AI in biomedicine.

In a similar vein, this issue explores the remarkable interplay between bioinformatics and AI. Bioinformatics stands at the heart of contemporary biomedicine, utilizing computational methodologies to address biological conundrums and manage vast biomedical data. By integrating AI into this domain, we can supercharge our ability to process, analyze, and interpret complex biological datasets, leading to breakthroughs in areas like genomics, proteomics, and drug discovery.

The themes of interest for this Special Issue include, but are not limited to:

- Novel AI algorithms for advanced characterization of biological tissue mechanics;

- Data analysis for better healthcare;

- AI approaches for better clinical assistance;

- Quantitative analysis for monitoring physician/patient pressure;

- AI-aided quantification of in vivo functional biomechanical properties of biological tissues;

- Investigations into the relationship between tissue's biomechanical behavior and its underlying microstructure through AI;

- Validation and uncertainty quantification in AI-enhanced, image-based patient-specific simulations;

- Advanced computational biomechanics utilizing AI for rapid personalized surgical simulations and pre-operative treatment planning.

This Special Issue welcomes all relevant research areas as long as AI, experimentation, and/or predictive simulations are the main study drivers. We anticipate that this will be an insightful resource for scholars, researchers, and healthcare professionals alike, offering a blend of theoretical research and practical insights. We eagerly await your contributions and active participation in this journey of discovery.

Best regards,

Prof. Dr. Jian Wu
Dr. Hongxia Xu
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

  • medical artificial intelligence
  • quantitative analysis/ai for healthcare
  • bioinformatics

Published Papers (4 papers)

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Research

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11 pages, 1901 KiB  
Article
A Clinical Trial Evaluating the Efficacy of Deep Learning-Based Facial Recognition for Patient Identification in Diverse Hospital Settings
by Ayako Sadahide, Hideki Itoh, Ken Moritou, Hirofumi Kameyama, Ryoya Oda, Hitoshi Tabuchi and Yoshiaki Kiuchi
Bioengineering 2024, 11(4), 384; https://doi.org/10.3390/bioengineering11040384 - 15 Apr 2024
Viewed by 552
Abstract
Background: Facial recognition systems utilizing deep learning techniques can improve the accuracy of facial recognition technology. However, it remains unclear whether these systems should be available for patient identification in a hospital setting. Methods: We evaluated a facial recognition system using deep learning [...] Read more.
Background: Facial recognition systems utilizing deep learning techniques can improve the accuracy of facial recognition technology. However, it remains unclear whether these systems should be available for patient identification in a hospital setting. Methods: We evaluated a facial recognition system using deep learning and the built-in camera of an iPad to identify patients. We tested the system under different conditions to assess its authentication scores (AS) and determine its efficacy. Our evaluation included 100 patients in four postures: sitting, supine, and lateral positions, with and without masks, and under nighttime sleeping conditions. Results: Our results show that the unmasked certification rate of 99.7% was significantly higher than the masked rate of 90.8% (p < 0.0001). In addition, we found that the authentication rate exceeded 99% even during nighttime sleeping. Furthermore, the facial recognition system was safe and acceptable for patient identification within a hospital environment. Even for patients wearing masks, we achieved a 100% success rate for authentication regardless of illumination if they were sitting with their eyes open. Conclusions: This is the first systematical study to evaluate facial recognition among hospitalized patients under different situations. The facial recognition system using deep learning for patient identification shows promising results, proving its safety and acceptability, especially in hospital settings where accurate patient identification is crucial. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedicine)
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23 pages, 12160 KiB  
Article
Segmentation of Variants of Nuclei on Whole Slide Images by Using Radiomic Features
by Taimoor Shakeel Sheikh and Migyung Cho
Bioengineering 2024, 11(3), 252; https://doi.org/10.3390/bioengineering11030252 - 04 Mar 2024
Viewed by 880
Abstract
The histopathological segmentation of nuclear types is a challenging task because nuclei exhibit distinct morphologies, textures, and staining characteristics. Accurate segmentation is critical because it affects the diagnostic workflow for patient assessment. In this study, a framework was proposed for segmenting various types [...] Read more.
The histopathological segmentation of nuclear types is a challenging task because nuclei exhibit distinct morphologies, textures, and staining characteristics. Accurate segmentation is critical because it affects the diagnostic workflow for patient assessment. In this study, a framework was proposed for segmenting various types of nuclei from different organs of the body. The proposed framework improved the segmentation performance for each nuclear type using radiomics. First, we used distinct radiomic features to extract and analyze quantitative information about each type of nucleus and subsequently trained various classifiers based on the best input sub-features of each radiomic feature selected by a LASSO operator. Second, we inputted the outputs of the best classifier to various segmentation models to learn the variants of nuclei. Using the MoNuSAC2020 dataset, we achieved state-of-the-art segmentation performance for each category of nuclei type despite the complexity, overlapping, and obscure regions. The generalized adaptability of the proposed framework was verified by the consistent performance obtained in whole slide images of different organs of the body and radiomic features. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedicine)
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17 pages, 1749 KiB  
Article
Low-Data Drug Design with Few-Shot Generative Domain Adaptation
by Ke Liu, Yuqiang Han, Zhichen Gong and Hongxia Xu
Bioengineering 2023, 10(9), 1104; https://doi.org/10.3390/bioengineering10091104 - 21 Sep 2023
Viewed by 1254
Abstract
Developing new drugs for emerging diseases, such as COVID-19, is crucial for promoting public health. In recent years, the application of artificial intelligence (AI) has significantly advanced drug discovery pipelines. Generative models, such as generative adversarial networks (GANs), exhibit the potential for discovering [...] Read more.
Developing new drugs for emerging diseases, such as COVID-19, is crucial for promoting public health. In recent years, the application of artificial intelligence (AI) has significantly advanced drug discovery pipelines. Generative models, such as generative adversarial networks (GANs), exhibit the potential for discovering novel drug molecules by relying on a vast number of training samples. However, for new diseases, only a few samples are typically available, posing a significant challenge to learning a generative model that produces both high-quality and diverse molecules under limited supervision. To address this low-data drug generation issue, we propose a novel molecule generative domain adaptation paradigm (Mol-GenDA), which transfers a pre-trained GAN on a large-scale drug molecule dataset to a new disease domain using only a few references. Specifically, we introduce a molecule adaptor into the GAN generator during the fine tuning, allowing the generator to reuse prior knowledge learned in pre-training to the greatest extent and maintain the quality and diversity of the generated molecules. Comprehensive downstream experiments demonstrate that Mol-GenDA can produce high-quality and diverse drug candidates. In summary, the proposed approach offers a promising solution to expedite drug discovery for new diseases, which could lead to the timely development of effective drugs to combat emerging outbreaks. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedicine)
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Review

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24 pages, 6681 KiB  
Review
Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody–Antigen Interactions
by Doo Nam Kim, Andrew D. McNaughton and Neeraj Kumar
Bioengineering 2024, 11(2), 185; https://doi.org/10.3390/bioengineering11020185 - 15 Feb 2024
Viewed by 2000
Abstract
This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein–protein interactions (PPIs), enhancing the efficacy [...] Read more.
This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein–protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedicine)
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