MicroPort (China)

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway structures remains prohibitively time-consuming. While significant efforts have been made towards enhancing automatic airway modelling, current public-available datasets predominantly concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023′ (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for mortality prediction, a strong airway-derived biomarker (Hazard ratio>1.5, p < 0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.

The present study aimed to identify the optimal design of the unicompartmental femoral component through parameter analysis and stability evaluation. A finite element (FE) analysis was applied to analyse and adjust the parameter combinations of the anterior tilt angle of the posterior condyle resection surface, the position of the peg, the length of the pegand the inclination angle of the peg, resulting in 10 different FE models. Setting three knee flexion angles of 8.4° (maximum load state during walking), 40° (maximum load state during stair climbing)and 90° (maximum load state during squatting exercise), quantitatively analysing the micromotion values of the bone-prosthesis interfaceand defining a weighted scoring formula to evaluate the stability of different FE models. The validity of the FE analysis was verified using the Digital Image Correlation (DIC) device. The errors between the FE analysis and the DIC test at three flexion angles were 5.6%, 1.7%and 11.1%. The 10 different femoral component design models were measured separately. The FE analysis demonstrated that the design with a 0° anterior tilt angle of the posterior condyle resection surface, both pegs placed on the distal resection surface, lengthened 5 mm pegsand a 10° peg inclination angle provided the best stability. The current study proposed a method for evaluating the stability of the femoral component design. The optimal intersurface stability design of the unicompartmental femoral component was achieved with two pegs placed on the distal resection surface, a 5-mm peg length incrementand a 10° peg inclination. These results might provide a reference for the selection of unicompartmental femoral components in clinical practice and therefore improve the survival rate of future unicompartmental knee arthroplasty. Level III.

Abstract As the robot-assisted single-port surgery gets more attention, we proposed a novel single-arm single-port micro-traumatic laparoscopic surgery robot system with multiple innovative techniques. From a mechanical perspective, joints with high rigidity and reliability were used to realize the remote center of motion. The cost of consumables was reduced by adding the support of a rigid endoscope. From the algorithm perspective, a high-precision motion control method and feedback-force protection mechanism were implemented. The effectiveness of the aforementioned characteristics was verified by 10 clinical cholecystectomy experiments. The results showed that the system can reduce the amount of bleeding, accelerate patient recovery, reduce infection risk, and shorten the learning period. Other gynecology and urology clinical trials using the new surgery robot are underway. The proposed surgery robot system had significant clinical application value.

Anomaly detection in fundus images remains challenging due to the fact that fundus images often contain diverse types of lesions with various properties in locations, sizes, shapes, and colors. Current methods achieve anomaly detection mainly through reconstructing or separating the fundus image background from a fundus image under the guidance of a set of normal fundus images. The reconstruction methods, however, ignore the constraint from lesions. The separation methods primarily model the diverse lesions with pixel-based independent and identical distributed (i.i.d.) properties, neglecting the individualized variations of different types of lesions and their structural properties. And hence, these methods may have difficulty to well distinguish lesions from fundus image backgrounds especially with the normal personalized variations (NPV). To address these challenges, we propose a patch-based non-i.i.d. mixture of Gaussian (MoG) to model diverse lesions for adapting to their statistical distribution variations in different fundus images and their patch-like structural properties. Further, we particularly introduce the weighted Schatten p-norm as the metric of low-rank decomposition for enhancing the accuracy of the learned fundus image backgrounds and reducing false-positives caused by NPV. With the individualized modeling of the diverse lesions and the background learning, fundus image backgrounds and NPV are finely learned and subsequently distinguished from diverse lesions, to ultimately improve the anomaly detection. The proposed method is evaluated on two real-world databases and one artificial database, outperforming the state-of-the-art methods.

As the robotic assisted single port surgery arousing attention, a novel single-arm single-port micro-traumatic laparoscopic robotic surgical system is proposed in this study. From the perspective of the mechanics, joints with high rigidity and high reliability were utilized to realize the remote center of motion (RCM). Besides, the cost of consumables was reduced by adding the support of the rigid endoscope. From the perspective of the algorithm, high-precision motion control method and feedback force protection mechanism were implemented. The effectiveness of the aforementioned characteristics were verified by five clinical experiments of cholecystectomy. The results showed that the system is able to reduce the amount of bleeding, accelerate the patient recovery, reduce the infection risk and shorten the learning period. The robotic surgical system had significant clinical application value.

Driven by the evolving role of modern robots in collaboration with human operators, this paper proposes a virtual potential field-based motion planning and control strategy to realize effective human-robot collaboration through kinesthetic guidance via robot teleoperation. The motion of the teleoperated robot is determined by the human operator using a haptic device programmed to replicate movement under the influence of the virtual potential field constructed based on the obstacles and objectives in the environment. This approach facilitates seamless cooperation between the human and collaborative robot. It can also be readily generalized to include moving obstacles and goals in a dynamic environment. The results suggest that the virtual potential field can effectively enhance the safety and accuracy of human-robot cooperation. The developed method has promising applications in human-centric robotics fields such as medical robotics and robot-assisted surgeries.