Yiming Rong

With the advancement of pervasive commerce on the Internet, the way to purchase consumer products has been changing to personalized products with preferred individual customization. A new manufacturing paradigm named “Customer-to-Manufacturer (C2M)” has been prevalent in the E-commerce sector and the context of Industry 5.0. It establishes a direct connection between consumers and manufacturers via E-commerce platforms, allowing individual clients to participate in the model design process alongside engineers to fulfill their specific requirements. Moreover, it aims to provide manufacturers with systematic approaches for producing customized products more efficiently, resulting in shorter delivery times, lower inventory costs, and higher customer satisfaction and loyalty. In this paper, the authors briefly review the evolution of the manufacturing paradigm, suggest a structurally well-defined C2M concept, and propose a framework for C2M that utilizes contemporary digital technologies in the context of Industry 5.0 to streamline the manufacturing-to-delivery process through intelligent, human-centric, resilient, and environment-friendly design and manufacturing solutions. Additionally, a case study for the implementation of C2M has been explored. The findings of this study suggest that the C2M paradigm is an effective way to meet the growing demand for customization and personalization in E-commerce. It not only improves customer satisfaction and loyalty but also has the potential to improve supply chain efficiency and reduce costs for both sides. E-commerce retailers or manufacturing enterprises may consider adopting a C2M paradigm for online customization to stay competitive in a rapidly evolving market.

Ultrasound image guidance could display the movement of soft tissue in real time, which provides an important basis for the operation path selection of tumor for examinations and interventions such as precise localization and puncture biopsy. However, factors such as tissue deformation, patient motion, and instrument contact pose great challenges for real-time smooth localization tracking of target tissues and maintaining a suitable acoustic window. Not only does it reduce the accuracy of ultrasound examination, but also may prolong the examination time. In this paper, a real-time autonomous ultrasound robot trajectory planning framework is proposed to achieve global scanning of breast tissue and real-time local tracking of suspicious tumor targets. In addition, an ultrasound suspicious tumor target next moment motion attitude and position estimation algorithm and an acceleration-continuous online trajectory generation (ACOTG) algorithm are proposed. Experiments on breast phantom showed that the fluctuation difference of both global scanning and tracking scanning force was less than 5 N $\pm$ 15%, and ACOTG takes less than 1 microsecond to calculate the next moment state. The position, velocity, and acceleration of the tracking path did not change abruptly when the unknown target changed. The ultrasound image of the suspected tumor target could always be smoothly maintained in a suitable acoustic window, while the errors between the center of the suspected tumor target image and the center of the acoustic window in the horizontal direction and the depth direction were less than $\pm$ 1 mm and 30 mm $\pm$ 10%, respectively. Note to Practitioners —The motivation of this study was to solve the clinical problem of difficulty in keeping the suspected breast tumor target stably in the center of the field of view under the influence of tissue deformation, patient motion and instrument contact in ultrasound guidance. We proposed the idea of global conventional scanning and local online tracking to achieve the autonomous scanning, detecting and tracking of suspicious tumor targets within the global scope of the breast. ACOTG algorithm is proposed and implemented, which can make the ultrasound image of suspicious tumor target can be displayed stably in the center of the acoustic window. With the help of this system, the accuracy of ultrasound examination can be improved and the time of examination can be reduced. The proposed system can also be applied to ultrasound scanning of other parts of the body.

The fusion of robotics and ultrasound scanning has been used to overcome the shortcomings of traditional ultrasound examinations, such as high variability and low repeatability of ultrasound images acquired by operators. However, there are huge differences in the surface morphology of different human bodies, and the current clinical practice still relies heavily on the manually generating a suitable scanning trajectory for the freeform surface. In this paper, a practical strategy for automatic generation of ultrasound scanning trajectory on breast surfaces is proposed, which can automatically and efficiently generate scanning trajectory for any free-form breast surface. First, the probe attitude and contact model of ultrasound automatic scanning are analyzed. Second, an end-to-end automatic trajectory generation workflow based on 3D point clouds is proposed. Furthermore, a trajectory point offset strategy considering skin deformation and a simultaneous double breast scanning strategy are proposed. In addition, a probe attitude normal admittance controller based on position inner loop control is designed. The experimental results show that the overall time of trajectory generation is less than 4 s. The automatic scanning is 30.84 times more stable in controlling the contact force than the manual scanning, which effectively improves the repeatability of the scanning.

Two-dimensional lung ultrasound (LUS) has widely emerged as a rapid and noninvasive imaging tool for the detection and diagnosis of coronavirus disease 2019 (COVID-19). However, image differences will be magnified due to changes in ultrasound (US) imaging experience, such as US probe attitude control and force control, which will directly affect the diagnosis results. In addition, the risk of virus transmission between sonographer and patients is increased due to frequent physical contact. In this study, a fully automatic dual-probe US scanning robot for the acquisition of LUS images is proposed and developed. Furthermore, the trajectory was optimized based on the velocity look-ahead strategy, the stability of contact force of the system and the scanning efficiency were improved by 24.13% and 29.46%, respectively. Also, the control ability of the contact force of robotic automatic scanning was 34.14 times higher than that of traditional manual scanning, which significantly improves the smoothness of scanning. Importantly, there was no significant difference in image quality obtained by robotic automatic scanning and manual scanning. Furthermore, the scanning time for a single person is less than 4 min, which greatly improves the efficiency of screening triage of group COVID-19 diagnosis and suspected patients and reduces the risk of virus exposure and spread.