International Journal of Medical Robotics and Computer Assisted Surgery最新文献

Background

The present study aimed to examine the impact of prior experience of computed tomography-based navigated THA (CTN-THA) on the accuracy of cup placement in robotic arm-assisted total hip arthroplasty via direct anterior approach using the Mako system (Mako-THA).

Methods

We evaluated the first 60 hips that underwent Mako-THA performed by surgeons with and without CTN-THA experience and compared the absolute error of pre-operative and post-operative cup placement using a three-dimensional template between the two groups.

Results

The CTN-user group demonstrated significantly better results in radiographic inclination, y-(sagittal) axis placement, and z-(vertical) axis placement than the non-CTN-user group (CTN-user group: 1.0° ± 0.9°, 1.5 ± 1.4 mm, and 1.4 ± 1.1 mm, respectively; non-CTN-user group: 2.2° ± 1.8°, 2.3 ± 1.2 mm, and 2.4 ± 1.4 mm, respectively). There was no significant difference in radiographic anteversion (CTN-user group: 1.4° ± 1.4° vs. non-CTN-user group: 1.9° ± 1.8°).

Conclusions

Surgeons with prior CTN-THA experience achieved higher accuracy in cup placement than surgeons without CTN-THA experience.

Background

Energy-based cutting tools combine cutting and haemostasis, making them widely utilised. Accurately predicting tissue deformation during energy-based cutting can provide precise navigation information to enhance surgical outcomes, while existing surgical cutting models focussing on blades-based tools are unable to accurately predict energy cutting deformation.

Methods

This paper aims to propose a novel energy cutting model under different cutting trajectories. First, a stratified discontinuity mechanism-based modelling method of energy cutting is proposed. Second, a parameterised impact zone model is developed for describing complex surgical manipulations using intraoperative trajectories. Third, an incremental cutting computation algorithm and a novel void enrichment function are proposed to enhance the computational efficiency.

Results

The mean absolute deformation errors of numerical and experimental results under various of cutting trajectories are less than 1 mm. The computation efficiency and convergence are also validated.

Conclusions

The desired cutting deformation accuracy is achieved robustly while maintaining computation efficiency.

Background

Accurate perception of pedicle geometry during pedicle-screw placement surgery is critically important because the margin-for-error is small.

Method

An assessment algorithm is developed to provide machine-independent MultiPlanar Reconstruction (MiMPR) of the pedicle. The reconstruction is independent of the CT-machine frame and enhances patient data portability. Additionally, the algorithm obtains the pedicle-screw axis with optimum direction, length, and margin using MPRs. A method for the autonomous identification of four body features to form a CT-independent vertebral frame, {V}, in the image space is formulated.

Result

Applied to 200 high-resolution CT images, the approach achieved a 100% success rate in defining the pedicle-medial axis and maximum screw diameter considering the safety margin of 2 mm.

Conclusions

The method eliminates subjective assessment. It provides objective assessment in determining the pedicle-medial axis with optimal direction and margin without human annotation. Additionally, it significantly enhances screw placement accuracy in robot-assisted spinal fusion surgeries, regardless of vertebra orientation.

Background

A minimally invasive spherical osteotomy assistant system (MISOS) based on a multi-axis surgical robot was designed to improve the control and precision of manual spherical osteotomy.

Methods

Based on human sawbone models, the feasibility of MISOS was verified by comparing the osteotomy centre deviation and the section curvature of the acetabulum, femur, and tibia.

Results

Compared with freehand spherical osteotomy, the MISOS system demonstrated superior centre deviation for the acetabulum (1.48 ± 0.93 vs. 11.15 ± 3.97 mm), femur (3.12 ± 0.75 vs. 8.81 ± 3.04 mm), and tibia (1.91 ± 0.84 vs. 7.33 ± 1.53 mm) as well as superior curvature deviation for the acetabulum (1.40 ± 0.08 vs. 3.16 ± 0.56 mm), femur (0.26 ± 0.07 vs. 0.491 ± 0.21 mm), and tibia (0.21 ± 0.02 vs. 0.46 ± 0.18 mm). These results indicate that MISOS can assist surgeons in performing accurate and stable spherical osteotomy.

Conclusion

The MISOS system demonstrates promise as a precise assistive tool for minimally invasive orthopaedic spherical osteotomy, with potential for broader clinical applications.

Background

Assistance tools for intraoperative 2D and 3D imaging to decrease acquisition effort and to improve assessment of 3D image data were evaluated.

Methods

Two automated optimisation procedures were evaluated in a cadaver (Cios Spin, Siemens, Germany): The ScrewScout function for assisted pedicle screw assessment. Then, an algorithm for metal artefact reduction (MAR).

Additionally, a tool for simplified setting of image contrast and brightness was evaluated regarding the result and elapsed time.

Results

The time required without automated assistance was 83s [70–105]. With the computer assistance, this time was significantly lower at 22s [15–32] (p = 0.003). MAR resulted in an improvement in image impression. This improvement became smaller with increasing clinical experience. The time needed for setting of image acquisition parameters was significantly (p = 0.05) lowered from 140s [24–389] to 61s [14–166] using the assistance tool.

Conclusions

Automated assistance tools for image optimisation can provide practical support in the intraoperative setting.

Background

Choledochal cysts are congenital anomalies requiring surgical intervention, typical excision and hepaticojejunostomy. The da Vinci single-port (SP) robotic system offers a minimally invasive approach with potential benefits for paediatric patients.

Methods

This study describes the SP robotic-assisted resection of choledochal cysts and hepaticojejunostomy in two paediatric patients. Surgical techniques, system description, and procedural outcomes were detailed.

Results

Both surgeries were successfully completed with minimal blood loss and no intraoperative complications. Patients transitioned to a soft diet by postoperative day 3 and were discharged by day 6 without complications. Follow-up at 6 months showed normal sonography and laboratory findings.

Conclusions

The da Vinci SP system facilitated precise single-incision surgery with improved manoeuvrability and visualisation, demonstrating safety and feasibility for paediatric choledochal cyst excision and hepaticojejunostomy. Further studies are warranted to confirm these findings across larger paediatric populations.

Background

This study aims to develop an active following technology of the mirror-holding arm of a bedside intelligent surgical robot that enables real-time automatic tracking of surgical instruments.

Methods

The semantic segmentation network was adaptively enhanced, and new datasets were collected with surgical clamps as the target, incorporating scenarios commonly encountered during surgical procedures. In addition, the position of the clamp was determined, and the range and mode of motion of the robot arm were set. Based on the above premise, the control rate was designed and verified using an endoscopic surgery simulation platform.

Results

The average time for the collaborative arm to reach the centre of the field of view was 2.51 s, with a variance of 0.32.

Conclusions

The results of this study validate the effectiveness of replacing manual endoscope holding with a robotic arm, which has great potential to facilitate more efficient endoscopic surgical procedures.

Background

Robot-assisted minimally invasive surgery has effectively addressed the challenges faced by traditional minimally invasive surgery. Well-designed preoperative planning is crucial for robot-assisted minimally invasive surgery.

Methods

This paper proposes a preoperative planning method based on a clinical evaluation system. The particle swarm optimisation algorithm and the evaluation indices including accessibility, visibility, operability, and hand-eye coordination are adopted.

Results

The simulation validation and the experimental verification were conducted to compare the pre-operative planning scheme and the clinical scheme, taking the oesophageal hiatal hernia repair as an example. The preoperative planning scheme demonstrated superior accessibility and hand-eye coordination, achieving shorter surgical time and reduced task load.

Conclusion

The proposed preoperative planning method is feasible and effective through simulation and experimentation. This method has potential applications in various surgical robot systems and procedures, which can provide surgical guidance for surgeons in different departments.

Background

This research aims to use deep learning to create automated systems for better breast cancer detection and categorisation in mammogram images, helping medical professionals overcome challenges such as time consumption, feature extraction issues and limited training models.

Methods

This research introduced a Lightweight Multihead attention Gannet Convolutional Neural Network (LMGCNN) to classify mammogram images effectively. It used wiener filtering, unsharp masking, and adaptive histogram equalisation to enhance images and remove noise, followed by Grey-Level Co-occurrence Matrix (GLCM) for feature extraction. Ideal feature selection is done by a self-adaptive quantum equilibrium optimiser with artificial bee colony.

Results

The research assessed on two datasets, CBIS-DDSM and MIAS, achieving impressive accuracy rates of 98.2% and 99.9%, respectively, which highlight the superior performance of the LMGCNN model while accurately detecting breast cancer compared to previous models.

Conclusion

This method illustrates potential in aiding initial and accurate breast cancer detection, possibly leading to improved patient outcomes.

Background

Accurately identifying lung lesions in CT (Computed Tomography) scans remains crucial during the Coronavirus Disease 2019 (COVID-19) pandemic. Swarm intelligence algorithms offer promising tools for this purpose.

Methods

This study compares four swarm intelligence algorithms Gravitational Search Algorithm (GSA), Bacterial Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for segmenting COVID-19 lung lesions.

Results

GA, GSA, and BFOA achieved accuracies exceeding 90.5%, while the PSO algorithm further improved segmentation accuracy, reaching 91.45%, with an exceptional F1 score of 95.54%. Overall, the approach achieved up to 99% segmentation accuracy.

Conclusions

The findings demonstrate the effectiveness of swarm and evolutionary algorithms in segmenting COVID-19 lesions, contributing to enhanced diagnostic accuracy and treatment efficiency.