This paper presents a multi-particle parallel manipulation optoelectronic tweezers system integrated with computer vision technology, enabling the parallel and precise manipulation of dozens of particles. This system significantly enhances manipulation efficiency while maintaining high precision. By real-time monitoring of particle motion and light patterns, the system can rapidly adjust and optimize its manipulation strategy, thereby improving the stability and reliability of multi-particle synchronization in complex environments. Extensive experimental results demonstrate the system’s outstanding performance. For instance, it can quickly arrange complex patterns and letter sequences, facilitate the coordinated assembly of organoids from particle groups, and efficiently perform the precise separation and arrangement of mixed particles. The core advantage of this system lies in its high parallelism and flexibility, enabling it to handle large-scale synchronous manipulation tasks with exceptional operating accuracy. With continuous technological advancements and the broadening of application scenarios, this system is expected to have a profound impact in fields such as cell sorting, micro-device assembly, and organoid construction, providing robust support for research and technological development in these areas.

The efficacy of targeted cancer drug therapy is significantly compromised by imprecise drug delivery mechanisms. Micro/nano robots (MNRs), characterized by their controllable motion, present a promising solution to this challenge. However, the non-Newtonian nature of blood, with its high viscosity and blood cells’ interference, poses substantial limitations on the upstream efficiency of MNRs. This paper innovatively discusses for the first time the effects of blood viscosity and blood cell interference on the motion of MNRs, investigating their upstream motion capabilities in blood through comprehensive theoretical modeling, simulation, and experimental validation. A dynamic model of MNR motion was developed, and the velocity formula for MNRs in non-Newtonian fluid was derived. Experiments were conducted using different magnetic fields in pure water, high-viscosity simulated blood, and diluted blood. Results indicated that under a gradient magnetic field, the upstream velocities of MNRs in pure water, simulated blood, and diluted blood were 45.0, 14.4, and 11.1 mm/s, respectively. Under a rotating magnetic field, the velocities of vortex swarms were 825, 240, and 145 µm/s, respectively. Increased fluid viscosity reduced MNR velocity by 70%, while blood cells caused an additional 10% reduction. This research establishes a theoretical and experimental framework for the upstream motion of MNRs against blood flow, enhancing their potential in targeted drug delivery and broader biomedical applications.

In recent years, capsule robots have gained wide acceptance among doctors and patients for the examination of gastrointestinal diseases due to their non-invasive, safe, and painless advantages. However, the image resolution captured by capsule robots is limited by space size and power, which hinders doctors' ability to accurately assess patients' stomach conditions and real-time control of the capsule robot. This paper proposes the design of two super-resolution networks for capsule robot videos. The first network, EndoVSR, is a high-performance offline video super-resolution network based on a generative adversarial network. It is designed to enhance the resolution of captured videos during offline processing. The second network, Bi-RUN, is a real-time video super-resolution network based on recurrent neural networks. It is designed to enhance the resolution of videos in real-time, enabling doctors to have a clearer view of the stomach condition during the examination. Extensive training and verification of these networks have been conducted using different datasets. All the performance indicators achieved leading positions. Furthermore, simulation experiments were carried out on pig stomachs in vitro to further validate the performance of the proposed networks in practical applications.

Magnetically controlled micro-nano robots hold revolutionary significance in the clinical targeted treatment of brain tumors. Imaging and tracking miniature robots can provide feedback for precise magnetic field control. The cooperation among micro-nano robots, magnetic field control system, and imaging system is a significant challenge for transitioning micro-nano robots from laboratory research to clinical applications. This study explores the control and spatial localization of magnetic nanorobot swarms in a highly realistic, human-sized vascular phantom which is manufactured using the raw CT scan images. The cerebral arterial vessels are the key focus area with four main inlets and twenty-six branch outlets. The simulation results show that, under the influence of a magnetic field, the nanorobots can accumulate at the target tumor site. The Kernelized Correlation Filter (KCF) algorithm was employed to achieve single-plane tracking of nanorobots. Furthermore, based on a biplanar imaging system, three-dimensional spatial trajectory tracking of nanorobots was realized. This study provides a reference for in vivo spatial localization and imaging of magnetic nanorobot swarms (MNRS) transported through vascular system.

Neck lymph node detection is crucial for early cancer metastasis detection and treatment, influencing treatment success and patient survival rates. It also aids in disease staging, monitoring, and treatment selection. It requires the expertise of professional senior radiologists, as the accuracy of current automated detection methods is not sufficiently high. In this study, the neck lymph node detection network (LymoNet) based on YOLOv8 is proposed to detect and classify normal, inflammatory, and metastatic neck lymph nodes from ultrasound images. The advanced attention mechanism modules are utilized to enhance performance of the model, including the Coordinate Attention (CA) which helps the network focus on learning key features in the images, and the Multi-Head Self-Attention (MHSA) which captures global information at different scales. Meanwhile, the medical knowledge embedding which introduces prior knowledge from the medical domain is used to improve the classification performance. By integrating these elements, the YOLOv8 network can achieve better performance in neck lymph node detection tasks. Finally, LymoNet surpassed the benchmark model YOLOv8 by 6.6% in the [email protected], achieving the state-of-the-art (SOTA). This model provides a promising solution for automated neck lymph node detection in clinical environments. The proposed methods can also serve as a reference for applying deep learning algorithms in other fields. The source codes, trained weights, and validation data are available on GitHub.

As a medical microrobot, magnetic-controlled capsule robots (MCRs) are pivotal in internal diagnostics and therapeutic interventions. Achieving high-precision localization of MCRs is essential for the successful execution of medical procedures. This paper introduces a novel Dung Beetle Optimizer (DBO)-based localization method for MCR, demonstrating high localization accuracy and flexibility in static magnetic field environments and under the control of existing magnetic control systems. With the aid of an FPGA-based parallel measurement system, it can effectively eliminate measurement distortion. The average position and orientation errors could achieve 0.53 mm and 0.60° when performing 600 iterations per computation, and further increasing the number of iterations reduces the errors, which is superior to existing methods. Experimental validations underscore the method’s robust performance and compatibility with existing magnetic control systems.