Objective: This study aims to introduce a biopsy capsule robot based on the negative pressure suction principle to achieve liquid sampling in the digestive tract.
Methods: The proposed capsule robot is designed with a magnetic spring configuration. By controlling the direction of an external magnetic field, the suction port can be aligned with the target sampling area. The sampling operation can then be achieved by increasing the external field to start the magnetic spring and a negative pressure can be generated to achieve the liquid sampling. Moreover, a locking mechanism is designed to prevent the magnetic spring from retracting, ensuring that the collected liquid is not squeezed out.
Results: The capsule robot prototype has dimensions of 16.3mm × 24.4mm. Both phantom and in-vitro experiments have been carried out. Results showed that it can sample liquids with viscosities ranging from 0.7mPa s to 200mPa s and absorb up to 0.24ml liquid. Additionally, the sealing of the capsule also meets clinical requirements.
Conclusion: The experimental results indicate that the designed capsule robot can satisfy the clinical requirements for liquid sampling within the digestive tract.
Significance: This study has designed and developed a micro capsule robot in the digestive tract, which can achieve safe and efficient liquid sampling operations. The proposed robot can benefit the clinical diagnosis of digestive diseases, especially in the small intestine.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/TBME.2025.3550179 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Objective: This study aims to introduce a biopsy capsule robot based on the negative pressure suction principle to achieve liquid sampling in the digestive tract.
Methods: The proposed capsule robot is designed with a magnetic spring configuration. By controlling the direction of an external magnetic field, the suction port can be aligned with the target sampling area.
Tough hydrogel-coated containment capsule of magnetic liquid metal for remote gastrointestinal operation.
Natl Sci Rev
April 2025
Center for X-Mechanics, Department of Engineering Mechanics, Zhejiang University, Hangzhou 310027, China.
Gallium-based liquid metals, when combined with magnetic agents, emerge as intelligent materials with potential applications in soft robotics within biomedical engineering. However, concerns have arisen from the residual presence of liquid metal, raising long-term biological risks. Herein, we propose a containment method that involves the rolling of magnetic liquid-metal droplets in lyophilized powders, resulting in the formation of intact hydrogel coatings upon hydration.
View Article and Find Full Text PDFA rare case of posterior mediastinal myelolipoma resected using robot-assisted thoracic surgery.
Int J Surg Case Rep
March 2025
Department of Thoracic and Cardiovascular Surgery, Osaka Medical and Pharmaceutical University Hospital, Takatsuki, Japan.
Introduction: Myelolipomas are rare tumors containing adipose tissue and normal hematopoietic cells that mainly occur in the adrenal cortex. Mediastinal myelolipomas are extremely rare. The three-dimensional, high-definition view with up to 10 times image magnification, 7 degrees of freedom of surgical instruments, and filtration of physiologic hand tremors with robotic systems are ideal for removing mediastinal tumors in a small space.
View Article and Find Full Text PDFA visual SLAM loop closure detection method based on lightweight siamese capsule network.
Sci Rep
March 2025
College of Engineering, Zhejiang Normal University, Wucheng District No. 688, Yingbin Avenue, Jinhua, 321004, Zhejiang, China.
Loop closure detection is a key module in visual SLAM. During the robot's movement, the cumulative error of the robot is reduced by the loop closure detection method, which can provide constraints for the back-end pose optimization, and the SLAM system can build an accurate map. Traditional loop closure detection algorithms rely on the bag-of-words model, which involves a complex process, has slow loading speeds, and is sensitive to changes in illumination or viewing angles.
View Article and Find Full Text PDFA multimodal deep learning model for detecting endoscopic images of near-infrared fluorescence capsules.
Biosens Bioelectron
February 2025
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Medical and Engineering Cross Research Institute, School of Medicine, Henan University, Kaifeng, 475004, China. Electronic address:
Early screening for gastrointestinal (GI) diseases is critical for preventing cancer development. With the rapid advancement of deep learning technology, artificial intelligence (AI) has become increasingly prominent in the early detection of GI diseases. Capsule endoscopy is a non-invasive medical imaging technique used to examine the gastrointestinal tract.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!