Lei Gao, Yuan Xing, Yuan Bian, Jianchang Zhao, Xingchi Liu, Honglei Wang
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.
{"title":"Preoperative Planning and Experimental Validation for Robot-Assisted Minimally Invasive Surgery Based on a Clinical Evaluation System","authors":"Lei Gao, Yuan Xing, Yuan Bian, Jianchang Zhao, Xingchi Liu, Honglei Wang","doi":"10.1002/rcs.70042","DOIUrl":"https://doi.org/10.1002/rcs.70042","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>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.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"21 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143424179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"An Efficient Lightweight Multi Head Attention Gannet Convolutional Neural Network Based Mammograms Classification","authors":"Ramkumar Muthukrishnan, Ashok Balasubramaniam, Vijaipriya Krishnasamy, Sarath Kumar Ravichandran","doi":"10.1002/rcs.70043","DOIUrl":"https://doi.org/10.1002/rcs.70043","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This method illustrates potential in aiding initial and accurate breast cancer detection, possibly leading to improved patient outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"21 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Refining COVID-19 Lesion Segmentation in Lung CT Scans Using Swarm Intelligence and Evolutionary Algorithms","authors":"Wafa Gtifa, Marwa Fradi, Anis Sakly, Mohsen Machhout","doi":"10.1002/rcs.70044","DOIUrl":"https://doi.org/10.1002/rcs.70044","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The findings demonstrate the effectiveness of swarm and evolutionary algorithms in segmenting COVID-19 lesions, contributing to enhanced diagnostic accuracy and treatment efficiency.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"21 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jinhua Li, Chi Zhang, Bo Guan, Haitao Niu, Jianchang Zhao
Background
The inevitable network delay can directly impact the process of remote surgeries and affect the master–slave motion consistency, and sudden changes in delay can compromise surgical safety.
Methods
Firstly, real-time calibration of unidirectional network delays is performed. Subsequently, the network delay is forecasted with a real-time training parallel recurrent neural network for safety warnings, and the real-time forecast of slave manipulator position is performed to enhanced the master–slave motion consistency. Finally, the forecast accuracy across multiple scales is assessed to provide feedback.
Results
The programme can operate on standard computers at distances of at least 630 km. Our forecast method meets the real-time requirement, demonstrates strong generalisation capabilities and reduces the impact of network delay on master–slave motion consistency to approximately 20%–80% of its original level.
Conclusions
The proposed forecast method enables real-time delay forecast for remote surgeries, reducing the impact of delay on master–slave motion consistency.
{"title":"Network Delay Forecast and Master–Slave Consistency Enhancement for Remote Surgical Robots","authors":"Jinhua Li, Chi Zhang, Bo Guan, Haitao Niu, Jianchang Zhao","doi":"10.1002/rcs.70048","DOIUrl":"https://doi.org/10.1002/rcs.70048","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The inevitable network delay can directly impact the process of remote surgeries and affect the master–slave motion consistency, and sudden changes in delay can compromise surgical safety.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Firstly, real-time calibration of unidirectional network delays is performed. Subsequently, the network delay is forecasted with a real-time training parallel recurrent neural network for safety warnings, and the real-time forecast of slave manipulator position is performed to enhanced the master–slave motion consistency. Finally, the forecast accuracy across multiple scales is assessed to provide feedback.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The programme can operate on standard computers at distances of at least 630 km. Our forecast method meets the real-time requirement, demonstrates strong generalisation capabilities and reduces the impact of network delay on master–slave motion consistency to approximately 20%–80% of its original level.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The proposed forecast method enables real-time delay forecast for remote surgeries, reducing the impact of delay on master–slave motion consistency.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"21 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ahmed Sedky, Nader A. Mansour, Ahmed El-Assal, Mahmoud Magdy
Background
Neurosurgery demands high precision, and robotic-assisted systems are increasingly employed to enhance surgical outcomes. This study focuses on a hybrid robotic-assisted system for neurosurgery, addressing forward and inverse kinematics, Jacobian matrices, and system singularities.
Methods
The system is simulated using MATLAB/Simscape Multibody to achieve accurate kinematic and dynamic representations. An inverse kinematics framework was developed for generating and validating a circular trajectory at the end-effector tip. Two control strategies are compared: traditional active joint PID control and combined trajectory feedback plus feedforward control.
Results
The combined control strategy significantly improves performance, reducing the maximum absolute error of each output by an average of 46.5% and the mean square error by 50.31% under optimal conditions.
Conclusion
The findings highlight the potential of trajectory feedback and feedforward control to enhance the precision and reliability of robotic-assisted neurosurgical procedures.
{"title":"Full Study, Model Verification, and Control of a Five Degrees of Freedom Hybrid Robotic-Assisted System for Neurosurgery","authors":"Ahmed Sedky, Nader A. Mansour, Ahmed El-Assal, Mahmoud Magdy","doi":"10.1002/rcs.70047","DOIUrl":"https://doi.org/10.1002/rcs.70047","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Neurosurgery demands high precision, and robotic-assisted systems are increasingly employed to enhance surgical outcomes. This study focuses on a hybrid robotic-assisted system for neurosurgery, addressing forward and inverse kinematics, Jacobian matrices, and system singularities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The system is simulated using MATLAB/Simscape Multibody to achieve accurate kinematic and dynamic representations. An inverse kinematics framework was developed for generating and validating a circular trajectory at the end-effector tip. Two control strategies are compared: traditional active joint PID control and combined trajectory feedback plus feedforward control.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The combined control strategy significantly improves performance, reducing the maximum absolute error of each output by an average of 46.5% and the mean square error by 50.31% under optimal conditions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The findings highlight the potential of trajectory feedback and feedforward control to enhance the precision and reliability of robotic-assisted neurosurgical procedures.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"21 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143111579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jingsheng Xu, Bo Guan, Jianchang Zhao, Bo Yi, Jianmin Li
Background
Bronchoscopy is an essential measure for conducting lung biopsies in clinical practice. It is crucial for advancing the intelligence of bronchoscopy to acquire depth information from bronchoscopic image sequences.
Methods
A self-supervised multi-frame monocular depth estimation approach for bronchoscopy is constructed. Networks are trained by minimising the photometric reprojection error between the target frame and the reconstructed target frame. The adaptive dual attention module and the details emphasis module are introduced to better capture the edge contour and internal details. In addition, the approach is evaluated on a self-made dataset and compared against other established methods.
Results
Experimental results demonstrate that the proposed method outperforms other self-supervised monocular depth estimation approaches in both quantitative measurement and qualitative analysis.
Conclusion
Our monocular depth estimation approach for bronchoscopy achieves superior performance in terms of error and accuracy, and passes physical model validations, which can facilitate further research into intelligent bronchoscopic procedures.
{"title":"LungDepth: Self-Supervised Multi-Frame Monocular Depth Estimation for Bronchoscopy","authors":"Jingsheng Xu, Bo Guan, Jianchang Zhao, Bo Yi, Jianmin Li","doi":"10.1002/rcs.70050","DOIUrl":"10.1002/rcs.70050","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Bronchoscopy is an essential measure for conducting lung biopsies in clinical practice. It is crucial for advancing the intelligence of bronchoscopy to acquire depth information from bronchoscopic image sequences.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A self-supervised multi-frame monocular depth estimation approach for bronchoscopy is constructed. Networks are trained by minimising the photometric reprojection error between the target frame and the reconstructed target frame. The adaptive dual attention module and the details emphasis module are introduced to better capture the edge contour and internal details. In addition, the approach is evaluated on a self-made dataset and compared against other established methods.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Experimental results demonstrate that the proposed method outperforms other self-supervised monocular depth estimation approaches in both quantitative measurement and qualitative analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Our monocular depth estimation approach for bronchoscopy achieves superior performance in terms of error and accuracy, and passes physical model validations, which can facilitate further research into intelligent bronchoscopic procedures.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"21 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143082079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jie Li, Zhenguo Yang, Dingjia Li, Chongyang Wang, Ji Shen, Chao Han, Xing Xin, Weiqun Wang, Jichun Tan, Hao Liu
Background
A surgical robot with force feedback can guarantee precise and gentle manipulation for endometrial repair, ensuring the effectiveness and safety of the manipulation. However, the design of force sensors for surgical robots is challenging due to the limited anatomical space and the requirement for continuous rotation.
Methods
This paper presents a novel force-sensing surgical instrument for endometrial repair, including an inner scraping instrument and an outer force sensing sheath. It utilises the actuation force of the outer flexible sheath to predict real-time tangential and normal forces of the scraping to the endometrium surface. Experiments are conducted to test its force sensing accuracy.
Results
This paper simulated three types of operations with different tangential force thresholds and obtained the mean error with the standard deviation.
Discussion
This sensing method enables real-time and accurate measurement of the dynamic scraping force between the rotating instrument and the tissue.
{"title":"Dynamic Force Sensing for Continuous Scraping Towards Endometrium Repair Surgery","authors":"Jie Li, Zhenguo Yang, Dingjia Li, Chongyang Wang, Ji Shen, Chao Han, Xing Xin, Weiqun Wang, Jichun Tan, Hao Liu","doi":"10.1002/rcs.70045","DOIUrl":"10.1002/rcs.70045","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>A surgical robot with force feedback can guarantee precise and gentle manipulation for endometrial repair, ensuring the effectiveness and safety of the manipulation. However, the design of force sensors for surgical robots is challenging due to the limited anatomical space and the requirement for continuous rotation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This paper presents a novel force-sensing surgical instrument for endometrial repair, including an inner scraping instrument and an outer force sensing sheath. It utilises the actuation force of the outer flexible sheath to predict real-time tangential and normal forces of the scraping to the endometrium surface. Experiments are conducted to test its force sensing accuracy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>This paper simulated three types of operations with different tangential force thresholds and obtained the mean error with the standard deviation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>This sensing method enables real-time and accurate measurement of the dynamic scraping force between the rotating instrument and the tissue.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"21 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sarah Papa, Aleksandar Popovic, Reut Hod Dvorai, Rauf Shahbazov
Background
We aimed to investigate the outcome of patients after RDN at different time points.
Methods
We studied the outcomes of 77 living robotic living donor nephrectomies (RDN). Donors were separated into three groups: learning curve period (LCP), stabilisation period (SP), and teaching period (TP).
Results
There were significant differences in blood loss and hospitalisation times between the three groups. Operative time was significantly shorter in the LCP group compared with the SP and TP groups (282 ± 51.6 min vs. 308 ± 38.7 min vs. 314 ± 28.7, p = 0.02). However, warm ischaemia time was shorter in the TP group compared with the LCP and SP groups (5.0 ± 3.6 min vs. 3.4 ± 3.2 min vs. 1.5 ± 1.3 min, p < 0.01). Complication rates were higher in the LCP group compared with the SP and TP groups (p = 0.04).
Conclusion
This study demonstrated that RDN outcomes improve after the learning period.
背景：我们的目的是研究不同时间点RDN后患者的预后。方法：对77例活体机器人活体供肾切除术（RDN）的预后进行研究。将捐赠者分为学习曲线期（LCP）、稳定期（SP）和教学期（TP）三组。结果：三组患者失血量及住院时间差异有统计学意义。LCP组手术时间明显短于SP组和TP组（282±51.6 min vs 308±38.7 min vs 314±28.7 min, p = 0.02）。然而，与LCP和SP组相比，TP组的热缺血时间更短（5.0±3.6 min vs. 3.4±3.2 min vs. 1.5±1.3 min）， p结论：本研究表明，学习期后RDN结果有所改善。
{"title":"The Impact of Learning Curve on Robotic Living Donor Nephrectomy Outcomes: Retrospective Analysis","authors":"Sarah Papa, Aleksandar Popovic, Reut Hod Dvorai, Rauf Shahbazov","doi":"10.1002/rcs.70041","DOIUrl":"10.1002/rcs.70041","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>We aimed to investigate the outcome of patients after RDN at different time points.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We studied the outcomes of 77 living robotic living donor nephrectomies (RDN). Donors were separated into three groups: learning curve period (LCP), stabilisation period (SP), and teaching period (TP).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>There were significant differences in blood loss and hospitalisation times between the three groups. Operative time was significantly shorter in the LCP group compared with the SP and TP groups (282 ± 51.6 min vs. 308 ± 38.7 min vs. 314 ± 28.7, <i>p</i> = 0.02). However, warm ischaemia time was shorter in the TP group compared with the LCP and SP groups (5.0 ± 3.6 min vs. 3.4 ± 3.2 min vs. 1.5 ± 1.3 min, <i>p</i> < 0.01). Complication rates were higher in the LCP group compared with the SP and TP groups (<i>p</i> = 0.04).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This study demonstrated that RDN outcomes improve after the learning period.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"21 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143018038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ya Li, Da Lu, Xueqing Wu, Jiahao Lu, Yangyang Xu, Shijia Zhang, Le Zhang, Baoqing Pei
Background
The efficacy of laminectomy procedures is contingent on the method of resection. The objective of this study was to investigate the impact of different methods of resection on the surgical safety of automated laminectomy robots, an area that remains uncharted.
Methods
Lamina resection surgeries using both drilling and layer-by-layer methods, are performed on ovine spinal samples. An analysis of the force and lateral deviation at the end of the robotic arm is conducted.
Results
For the drilling and layer-by-layer lamina resection methods, the average peak force for the drilling method is 1.21 N, and deviations are within 1 mm. For the layer-by-layer resection method, the peak force reaches 2.39 N, and deviations are within 1.6 mm.
Conclusion
During the drilling resection, the ultrasonic osteotome experiences less force and minimal lateral deviation. This method demonstrates higher precision and safety in laminar resection surgeries. The drilling method should be the primary choice for robot manufacturers.
{"title":"Effect of Laminectomy Methods on the Surgical Safety of Automatic Laminectomy Robot","authors":"Ya Li, Da Lu, Xueqing Wu, Jiahao Lu, Yangyang Xu, Shijia Zhang, Le Zhang, Baoqing Pei","doi":"10.1002/rcs.70031","DOIUrl":"10.1002/rcs.70031","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The efficacy of laminectomy procedures is contingent on the method of resection. The objective of this study was to investigate the impact of different methods of resection on the surgical safety of automated laminectomy robots, an area that remains uncharted.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Lamina resection surgeries using both drilling and layer-by-layer methods, are performed on ovine spinal samples. An analysis of the force and lateral deviation at the end of the robotic arm is conducted.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>For the drilling and layer-by-layer lamina resection methods, the average peak force for the drilling method is 1.21 N, and deviations are within 1 mm. For the layer-by-layer resection method, the peak force reaches 2.39 N, and deviations are within 1.6 mm.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>During the drilling resection, the ultrasonic osteotome experiences less force and minimal lateral deviation. This method demonstrates higher precision and safety in laminar resection surgeries. The drilling method should be the primary choice for robot manufacturers.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"21 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143018036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号