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Biomimetics
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Artificial Intelligence (AI) in Biomedical Engineering
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Artificial Intelligence (AI) in Biomedical Engineering

A special issue of Biomimetics (ISSN 2313-7673).

Deadline for manuscript submissions: 31 May 2025 | Viewed by 5888

Special Issue Editor

Dr. Chang Seok Bang

E-Mail Website
Guest Editor
Department of Internal Medicine, Hallym University College of Medicine, Chuncheon 24253, Gangwon-do, Republic of Korea
Interests: gastroenterology; Helicobacter pylori; gastric cancer; colon cancer; endoscopy; deep learning; machine learning; meta-analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is at the cutting edge of innovation in biomedical engineering, enhancing various domains, such as biosignal analysis, medical imaging, disease diagnosis, and treatment planning, through sophisticated data-driven approaches. By significantly improving accuracy, efficiency, and adaptability, AI contributes to the advancement of personalized healthcare and precision medicine. One of the most exciting developments is the integration of AI with biomimicry, where AI systems draw inspiration from and mimic biological processes, leading to the creation of nature-inspired technologies. Examples include bio-mimetic robotics, adaptive prosthetics, and intelligent drug delivery systems that emulate natural behaviors. This Special Issue seeks to explore the synergy between AI and biomimicry and invites researchers to submit their latest studies and innovative applications that address complex medical challenges through these cutting-edge approaches.

Dr. Chang Seok Bang
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. Biomimetics 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 2200 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

  • artificial intelligence
  • deep learning
  • neural network
  • large language model

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Published Papers (6 papers)

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Research

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14 pages, 1880 KiB  
Article
MultiSEss: Automatic Sleep Staging Model Based on SE Attention Mechanism and State Space Model
by Zhentao Huang, Yuyao Yang, Zhiyuan Wang, Yuan Li, Zuowen Chen, Yahong Ma and Shanwen Zhang
Biomimetics 2025, 10(5), 288; https://doi.org/10.3390/biomimetics10050288 - 3 May 2025
Viewed by 280
Abstract
Sleep occupies about one-third of human life and is crucial for health, but traditional sleep staging relies on experts manually performing polysomnography (PSG), a process that is time-consuming, labor-intensive, and susceptible to subjective differences between evaluators. With the development of deep learning technologies, [...] Read more.
Sleep occupies about one-third of human life and is crucial for health, but traditional sleep staging relies on experts manually performing polysomnography (PSG), a process that is time-consuming, labor-intensive, and susceptible to subjective differences between evaluators. With the development of deep learning technologies, particularly the application of convolutional neural networks and recurrent neural networks, significant progress has been made in automatic sleep staging. However, existing methods still face challenges in feature extraction and cross-modal data fusion. This paper introduces an innovative deep learning architecture, MultiSEss, aimed at solving key issues in automatic sleep stage classification. The MultiSEss architecture utilizes a multi-scale convolution module to capture signal features from different frequency bands and incorporates a Squeeze-and-Excitation attention mechanism to enhance the learning of channel feature weights. Furthermore, the architecture discards complex attention mechanisms or encoder–decoder structures in favor of a state–space sequence coupling module, which more accurately captures and integrates correlations between multi-modal data. Experiments show that MultiSEss achieved accuracy results of 83.84% and 82.30% in five-fold cross-subject testing on the Sleep-EDF-20 and Sleep-EDF-78 datasets. MultiSEss demonstrates its potential in improving sleep stage accuracy, which is significant for enhancing the diagnosis and treatment of sleep disorders. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedical Engineering)
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19 pages, 1026 KiB  
Article
Surface EMG Sensing and Granular Gesture Recognition for Rehabilitative Pouring Tasks: A Case Study
by Congyi Zhang, Dalin Zhou, Yinfeng Fang, Naoyuki Kubota and Zhaojie Ju
Biomimetics 2025, 10(4), 229; https://doi.org/10.3390/biomimetics10040229 - 7 Apr 2025
Viewed by 382
Abstract
Surface electromyography (sEMG) non-invasively captures the electrical activity generated by muscle contractions, offering valuable insights into motion intentions. While sEMG has been widely applied to general gesture recognition in rehabilitation, there has been limited exploration of specific, intricate daily tasks, such as the [...] Read more.
Surface electromyography (sEMG) non-invasively captures the electrical activity generated by muscle contractions, offering valuable insights into motion intentions. While sEMG has been widely applied to general gesture recognition in rehabilitation, there has been limited exploration of specific, intricate daily tasks, such as the pouring action. Pouring is a common yet complex movement requiring precise muscle coordination and control, making it an ideal focus for rehabilitation studies. This research proposes a granular computing-based deep learning approach utilizing ConvMixer architecture enhanced with feature fusion and granular computing to improve gesture recognition accuracy. Our findings indicate that the addition of hand-crafted features significantly improves model performance; specifically, the ConvMixer model’s accuracy improved from 0.9512 to 0.9929. These results highlight the potential of our approach in rehabilitation technologies and assistive systems for restoring motor functions in daily activities. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedical Engineering)
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20 pages, 2066 KiB  
Article
Double Attention: An Optimization Method for the Self-Attention Mechanism Based on Human Attention
by Zeyu Zhang, Bin Li, Chenyang Yan, Kengo Furuichi and Yuki Todo
Biomimetics 2025, 10(1), 34; https://doi.org/10.3390/biomimetics10010034 - 8 Jan 2025
Viewed by 1174
Abstract
Artificial intelligence, with its remarkable adaptability, has gradually integrated into daily life. The emergence of the self-attention mechanism has propelled the Transformer architecture into diverse fields, including a role as an efficient and precise diagnostic and predictive tool in medicine. To enhance accuracy, [...] Read more.
Artificial intelligence, with its remarkable adaptability, has gradually integrated into daily life. The emergence of the self-attention mechanism has propelled the Transformer architecture into diverse fields, including a role as an efficient and precise diagnostic and predictive tool in medicine. To enhance accuracy, we propose the Double-Attention (DA) method, which improves the neural network’s biomimetic performance of human attention. By incorporating matrices generated from shifted images into the self-attention mechanism, the network gains the ability to preemptively acquire information from surrounding regions. Experimental results demonstrate the superior performance of our approaches across various benchmark datasets, validating their effectiveness. Furthermore, the method was applied to patient kidney datasets collected from hospitals for diabetes diagnosis, where they achieved high accuracy with significantly reduced computational demands. This advancement showcases the potential of our methods in the field of biomimetics, aligning well with the goals of developing innovative bioinspired diagnostic tools. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedical Engineering)
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11 pages, 2671 KiB  
Article
Edge Artificial Intelligence Device in Real-Time Endoscopy for Classification of Gastric Neoplasms: Development and Validation Study
by Eun Jeong Gong, Chang Seok Bang and Jae Jun Lee
Biomimetics 2024, 9(12), 783; https://doi.org/10.3390/biomimetics9120783 - 22 Dec 2024
Viewed by 1269
Abstract
Objective: We previously developed artificial intelligence (AI) diagnosis algorithms for predicting the six classes of stomach lesions. However, this required significant computational resources. The incorporation of AI into medical devices has evolved from centralized models to decentralized edge computing devices. In this study, [...] Read more.
Objective: We previously developed artificial intelligence (AI) diagnosis algorithms for predicting the six classes of stomach lesions. However, this required significant computational resources. The incorporation of AI into medical devices has evolved from centralized models to decentralized edge computing devices. In this study, a deep learning endoscopic image classification model was created to automatically categorize all phases of gastric carcinogenesis using an edge computing device. Design: A total of 15,910 endoscopic images were collected retrospectively and randomly assigned to train, validation, and internal-test datasets in an 8:1:1 ratio. The major outcomes were as follows: 1. lesion classification accuracy in six categories: normal/atrophy/intestinal metaplasia/dysplasia/early/advanced gastric cancer; and 2. the prospective evaluation of classification accuracy in real-world procedures. Results: The internal-test lesion-classification accuracy was 93.8% (95% confidence interval: 93.4–94.2%); precision was 88.6%, recall was 88.3%, and F1 score was 88.4%. For the prospective performance test, the established model attained an accuracy of 93.3% (91.5–95.1%). The established model’s lesion classification inference speed was 2–3 ms on GPU and 5–6 ms on CPU. The expert endoscopists reported no delays in lesion classification or any interference from the deep learning model throughout their exams. Conclusions: We established a deep learning endoscopic image classification model to automatically classify all stages of gastric carcinogenesis using an edge computing device. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedical Engineering)
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Review

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25 pages, 478 KiB  
Review
Electromyography Signals in Embedded Systems: A Review of Processing and Classification Techniques
by José Félix Castruita-López, Marcos Aviles, Diana C. Toledo-Pérez, Idalberto Macías-Socarrás and Juvenal Rodríguez-Reséndiz
Biomimetics 2025, 10(3), 166; https://doi.org/10.3390/biomimetics10030166 - 10 Mar 2025
Cited by 1 | Viewed by 852
Abstract
This article provides an overview of the implementation of electromyography (EMG) signal classification algorithms in various embedded system architectures. They address the specifications used for implementation in different devices, such as the number of movements and the type of classification method. Architectures analyzed [...] Read more.
This article provides an overview of the implementation of electromyography (EMG) signal classification algorithms in various embedded system architectures. They address the specifications used for implementation in different devices, such as the number of movements and the type of classification method. Architectures analyzed include microcontrollers, DSP, FPGA, SoC, and neuromorphic computers/chips in terms of precision, processing time, energy consumption, and cost. This analysis highlights the capabilities of each technology for real-time wearable applications such as smart prosthetics and gesture control devices, as well as the importance of local inference in artificial intelligence models to minimize execution times and resource consumption. The results show that the choice of device depends on the required system specifications, the robustness of the model, the number of movements to be classified, and the limits of knowledge concerning design and budget. This work provides a reference for selecting technologies for developing embedded biomedical solutions based on EMG. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedical Engineering)
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14 pages, 240 KiB  
Review
Use of Robotic Surgery in Plastic and Reconstructive Surgery: A Narrative Review
by Jennifer Novo, Ishith Seth, Yi Mon, Akshay Soni, Olivia Elkington, Gianluca Marcaccini and Warren M. Rozen
Biomimetics 2025, 10(2), 97; https://doi.org/10.3390/biomimetics10020097 - 9 Feb 2025
Viewed by 1372
Abstract
Background/Objectives: Robotic systems offer enhanced precision, dexterity, and visualization, which are essential in addressing the complex nature of plastic surgery procedures. Despite widespread adoption in other surgical specialties, such as urology and gynecology, their application in plastic surgery remains underexplored. This review examines [...] Read more.
Background/Objectives: Robotic systems offer enhanced precision, dexterity, and visualization, which are essential in addressing the complex nature of plastic surgery procedures. Despite widespread adoption in other surgical specialties, such as urology and gynecology, their application in plastic surgery remains underexplored. This review examines the use of robotic systems in plastic and reconstructive surgery with a focus on clinical outcomes. Methods: A literature search was conducted using PubMed, Embase, Scopus, and Web of Science. Search terms included (“robotic surgery” OR “surgical robots”) AND (“plastic surgery” OR “reconstructive surgery”). Studies on clinical outcomes and biomimetic innovations published between 1980 and 2024 were included, while non-English, cadaver-based, and animal studies were excluded. Data were systematically extracted using Covidence and analyzed. Results: Twenty-nine studies were identified that evaluated the clinical outcomes of robotics in areas including breast reconstruction, microsurgery, and craniofacial procedures. Robotic systems like the Da Vinci and Symani platforms offer motion scaling, tremor elimination, and enhanced depth perception. In nipple-sparing mastectomies, they reduced skin necrosis rates from 8% to 2%, while in DIEP flap reconstruction, they enabled smaller fascial incisions (2.67 ± 1.13 cm vs. 8.14 ± 1.69 cm) and faster recovery with fewer complications. In microsurgery, they achieved 100% patency for vessels under 0.3 mm and a 25.2% limb volume reduction in lymphedema patients in 3 months. Conclusions: Robotic systems show significant promise, particularly in procedures such as nipple-sparing mastectomies, and have the potential to overcome challenges including surgeon fatigue. However, challenges such as longer operating times, high costs, and limited haptic feedback remain barriers to their adoption. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedical Engineering)
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