The international journal of medical robotics + computer assisted surgery : MRCAS最新文献

Background: This study aimed to explore the effect of BMI on intraoperative conditions and postoperative complications (POCs) in robotic GC surgery.

Methods: This is a retrospective analysis conducted on 60 patients who have GC and received robotic radical gastrectomy (RG) in our hospital. The patients were allocated into normal (18.5 kg/m² ≤ BMI < 25 kg/m²) and high-BMI groups (BMI ≥ 25 kg/m²). The effect of BMI on intraoperative conditions and POCs was examined.

Results: The results revealed no statistical differences between both groups in terms of surgical procedure (p = 0.669), time of first postoperative flatus (p = 0.172), in-hospital stay (p = 0.454), Retrieved LNs (Lymph nodes) number (p = 1.000) and POCs (p < 0.05). However, the high BMI group had greater intraoperative bleeding (p = 0.018) and longer operating time (p = 0.016).

Conclusions: To conclude, BMI may not affect the safety of RG for GC. Nevertheless, high BMI was associated with increased blood loss and prolonged operative time.

Background: The foetal head's automatic segmentation from ultrasound imagery is considered a key step in prenatal examination. However, achieving high-quality semi-supervised foetal head image segmentation remains challenging due to low image resolution, unclear boundaries, and inconsistencies between labelled and unlabelled data.

Methods: To overcome these obstacles, we propose MCPNet, a morphological constraint-based copy-paste network for semi-supervised foetal head segmentation, incorporating score-guided morphological refinement (SMR) and copy-paste mixing augmentation (CPMA). SMR employs weighted scores derived from Sobel operators and Euclidean transform to ensure boundary consistency. Additionally, to mitigate the distribution gap between labelled and unlabelled data, we introduce CPMA. This method uses random cropping to swap foreground and background between labelled and unlabelled data.

Results: On the HC18 and PSFH benchmarks, our method achieves Dice scores of 93.72% and 92.31% respectively with 20% labelled data.

Conclusions: The results demonstrate our superior performance and clinical potential.

Background: The use of robotic telesurgery has increased because of its high accuracy, fewer complications, and remote-control capability. To improve the accuracy of robotic arms in these systems, it is essential to have a precise dynamic model.

Methods: In this study, we focus on the Sina_flex robotic telesurgery system and develop a dynamic model for a novel slave robot. Our approach involves deriving and linearising dynamic equations, defining optimal excitation trajectories, and estimating dynamic parameters using least square optimisation. To investigate the parameters' identification accuracy, the joint torques predicted by the model were compared with those actually obtained from the experiments.

Results: The results reveal that the method accurately predicts joint torques with the root mean square ( RMS) ranging from 0.58 to 1.48 Nm.

Conclusions: Using the proposed method in this paper for identifying the robot dynamic parameters leads to more accurate results for robots with complex mechanisms.

Background and aim: Most of the Mixed Reality models used in the surgical telepresence are suffering from the discrepancies in the boundary area and spatial-temporal inconsistency due to the illumination variation in the video frames. The aim behind this work is to propose a new solution that helps produce the composite video by merging the augmented video of the surgery site and virtual hand of the remote expertise surgeon. The purpose of the proposed solution is to decrease the processing time and enhance the accuracy of merged video by decreasing the overlay and visualization error and removing occlusion and artefacts.

Methodology: The proposed system enhanced the mean value cloning algorithm that helps to maintain the spatial-temporal consistency of the final composite video. The enhanced algorithm includes the 3D mean value coordinates and improvised mean value interpolant in the image cloning process, which helps to reduce the sawtooth, smudging and discoloration artefacts around the blending region RESULTS: As compared to the state of art solution, the accuracy in terms of overlay error of the proposed solution is improved from 1.01mm to 0.80mm whereas the accuracy in terms of visualization error is improved from 98.8% to 99.4%. The processing time is reduced to 0.173 seconds from 0.211 seconds CONCLUSION: Our solution helps make the object of interest consistent with the light intensity of the target image by adding the space distance that helps maintain the spatial consistency in the final merged video. This article is protected by copyright. All rights reserved.

Background and aim: Image registration and alignment are the main limitations of augmented reality-based knee replacement surgery. This research aims to decrease the registration error, eliminate outcomes that are trapped in local minima to improve the alignment problems, handle the occlusion and maximize the overlapping parts.

Methodology: markerless image registration method was used for Augmented reality-based knee replacement surgery to guide and visualize the surgical operation. While weight least square algorithm was used to enhance stereo camera-based tracking by filling border occlusion in right to left direction and non-border occlusion from left to right direction.

Results: This study has improved video precision to 0.57 mm ∼ 0.61 mm alignment error. Furthermore, with the use of bidirectional points, i.e. Forwards and backwards directional cloud point, the iteration on image registration was decreased. This has led to improved the processing time as well. The processing time of video frames was improved to 7.4 ∼11.74 fps.

Conclusions: It seems clear that this proposed system has focused on overcoming the misalignment difficulty caused by movement of patient and enhancing the AR visualization during knee replacement surgery. The proposed system was reliable and favourable which helps in eliminating alignment error by ascertaining the optimal rigid transformation between two cloud points and removing the outliers and non-Gaussian noise. The proposed augmented reality system helps in accurate visualization and navigation of anatomy of knee such as femur, tibia, cartilage, blood vessels, etc. This article is protected by copyright. All rights reserved.

Background: Traditional fracture reduction surgery cannot ensure the accuracy of the reduction while consuming the physical strength of the surgeon. Although monitoring the fracture reduction process through radiography can improve the accuracy of the reduction, it will bring radiation harm to both patients and surgeons.

Methods: We proposed a novel fracture reduction solution that parallel robot is used for fracture reduction surgery. The binocular camera indirectly obtains the position and posture of the fragment wrapped by the tissue by measuring the posture of the external markers. According to the clinical experience of fracture reduction, a path is designed for fracture reduction. Then using position-based visual serving control the robot to fracture reduction surgery. The study is approved by the Rehabilitation Hospital, National Research Center for Rehabilitation Technical Aids, Beijing , China.

Results: 10 virtual cases of fracture were used for fracture reduction experiments. The simulation and model bone experiments are designed respectively. In model bone experiments, the fragments are reduction without collision. The angulation error after the reduction of this method is:3.3°±1.8°, and the axial rotation error is 0.8°±0.3°, the transverse stagger error and the axial direction error after reduction is 2mm±0.5mm and 2.5mm±1mm. After the reduction surgery, the external fixator is used to assist the fixing, and the deformity will be completely corrected.

Conclusions: The solution can perform fracture reduction surgery with certain accuracy and effectively reduce the number of radiographic uses during surgery, and the collision between fragments is avoided during surgery. This article is protected by copyright. All rights reserved.

Background: The objective of this study was to evaluate the safety and feasibility of robot-assisted thoracoscopic surgery (RATS).

Methods: From May 2009 to May 2013, 48 patients with intrathoracic lesions underwent RATS with the da Vinci® Surgical System was reported (11 lobectomies, 37 mediastinal tumour resections).

Results: RATS was successfully and safely completed in all 48 patients. Conversion of the operation to open surgery was not needed in any patient. The average operation time was 85.9 min, average blood loss 33 ml, and average hospital stay 3.9 days. No patient required blood transfusion. The only recognized adverse event was the development of a bronchopleural fistula in one patient.

Conclusions: RATS appears feasible and safe in thoracic surgery. More investigation will be needed in order to determine its possible long-term benefits and cost effectiveness.