This study presents the development of a backpropagation neural network-based respiratory motion modelling method (BP-RMM) for precisely tracking arbitrary points within lung tissue throughout free respiration, encompassing deep inspiration and expiration phases.
Methods
Internal and external respiratory data from four-dimensional computed tomography (4DCT) are processed using various artificial intelligence algorithms. Data augmentation through polynomial interpolation is employed to enhance dataset robustness. A BP neural network is then constructed to comprehensively track lung tissue movement.
Results
The BP-RMM demonstrates promising accuracy. In cases from the public 4DCT dataset, the average target registration error (TRE) between authentic deep respiration phases and those forecasted by BP-RMM for 75 marked points is 1.819 mm. Notably, TRE for normal respiration phases is significantly lower, with a minimum error of 0.511 mm.
Conclusions
The proposed method is validated for its high accuracy and robustness, establishing it as a promising tool for surgical navigation within the lung.
{"title":"A back propagation neural network based respiratory motion modelling method","authors":"Shan Jiang, Bowen Li, Zhiyong Yang, Yuhua Li, Zeyang Zhou","doi":"10.1002/rcs.2647","DOIUrl":"10.1002/rcs.2647","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>This study presents the development of a backpropagation neural network-based respiratory motion modelling method (BP-RMM) for precisely tracking arbitrary points within lung tissue throughout free respiration, encompassing deep inspiration and expiration phases.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Internal and external respiratory data from four-dimensional computed tomography (4DCT) are processed using various artificial intelligence algorithms. Data augmentation through polynomial interpolation is employed to enhance dataset robustness. A BP neural network is then constructed to comprehensively track lung tissue movement.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The BP-RMM demonstrates promising accuracy. In cases from the public 4DCT dataset, the average target registration error (TRE) between authentic deep respiration phases and those forecasted by BP-RMM for 75 marked points is 1.819 mm. Notably, TRE for normal respiration phases is significantly lower, with a minimum error of 0.511 mm.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The proposed method is validated for its high accuracy and robustness, establishing it as a promising tool for surgical navigation within the lung.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"20 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141158124","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 estimating the 6D pose of snake-like wrist-type surgical instruments is challenging due to their complex kinematics and flexible design.
Methods
We propose ERegPose, a comprehensive strategy for precise 6D pose estimation. The strategy consists of two components: ERegPoseNet, an original deep neural network model designed for explicit regression of the instrument's 6D pose, and an annotated in-house dataset of simulated surgical operations. To capture rotational features, we employ an Single Shot multibox Detector (SSD)-like detector to generate bounding boxes of the instrument tip.
Results
ERegPoseNet achieves an error of 1.056 mm in 3D translation, 0.073 rad in 3D rotation, and an average distance (ADD) metric of 3.974 mm, indicating an overall spatial transformation error. The necessity of the SSD-like detector and L1 loss is validated through experiments.
Conclusions
ERegPose outperforms existing approaches, providing accurate 6D pose estimation for snake-like wrist-type surgical instruments. Its practical applications in various surgical tasks hold great promise.
{"title":"ERegPose: An explicit regression based 6D pose estimation for snake-like wrist-type surgical instruments","authors":"Jinhua Li, Zhengyang Ma, Xinan Sun, He Su","doi":"10.1002/rcs.2640","DOIUrl":"10.1002/rcs.2640","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Accurately estimating the 6D pose of snake-like wrist-type surgical instruments is challenging due to their complex kinematics and flexible design.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We propose ERegPose, a comprehensive strategy for precise 6D pose estimation. The strategy consists of two components: ERegPoseNet, an original deep neural network model designed for explicit regression of the instrument's 6D pose, and an annotated in-house dataset of simulated surgical operations. To capture rotational features, we employ an Single Shot multibox Detector (SSD)-like detector to generate bounding boxes of the instrument tip.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>ERegPoseNet achieves an error of 1.056 mm in 3D translation, 0.073 rad in 3D rotation, and an average distance (ADD) metric of 3.974 mm, indicating an overall spatial transformation error. The necessity of the SSD-like detector and L1 loss is validated through experiments.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>ERegPose outperforms existing approaches, providing accurate 6D pose estimation for snake-like wrist-type surgical instruments. Its practical applications in various surgical tasks hold great promise.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"20 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141094787","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}
In the field of orthopaedics, external fixators are commonly employed for treating extremity fractures and deformities. Computer-assisted systems offer a promising and less error-prone treatment alternative to manual fixation by utilising a software to plan treatments based on radiological and clinical data. Nevertheless, existing computer-assisted systems have limitations and constraints.
Methods
This work represents the culmination of a project aimed at developing a new automatised fixation system and a corresponding software to minimise human intervention and associated errors, and the developed system incorporates enhanced functionalities and has fewer constraints compared to existing systems.
Results
The automatised fixation system and its graphical user interface (GUI) demonstrate promising results in terms of accuracy, efficiency, and reliability.
Conclusion
The developed fixation system and its accompanying GUI represent an improvement in computer-assisted fixation systems. Future research may focus on further refining the system and conducting clinical trials.
{"title":"A new ring fixator system for automated bone fixation","authors":"Ahmet Aydi(ı)n, M. Kerem U(Ü)n","doi":"10.1002/rcs.2637","DOIUrl":"10.1002/rcs.2637","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>In the field of orthopaedics, external fixators are commonly employed for treating extremity fractures and deformities. Computer-assisted systems offer a promising and less error-prone treatment alternative to manual fixation by utilising a software to plan treatments based on radiological and clinical data. Nevertheless, existing computer-assisted systems have limitations and constraints.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This work represents the culmination of a project aimed at developing a new automatised fixation system and a corresponding software to minimise human intervention and associated errors, and the developed system incorporates enhanced functionalities and has fewer constraints compared to existing systems.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The automatised fixation system and its graphical user interface (GUI) demonstrate promising results in terms of accuracy, efficiency, and reliability.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The developed fixation system and its accompanying GUI represent an improvement in computer-assisted fixation systems. Future research may focus on further refining the system and conducting clinical trials.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"20 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rcs.2637","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141088785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reducing the annotation burden is an active and meaningful area of artificial intelligence (AI) research.
Methods
Multiple datasets for the segmentation of two landmarks were constructed based on 41 257 labelled images and 6 different microsurgical scenarios. These datasets were trained using the multi-stage transfer learning (TL) methodology.
Results
The multi-stage TL enhanced segmentation performance over baseline (mIOU 0.6892 vs. 0.8869). Besides, Convolutional Neural Networks (CNNs) achieved a robust performance (mIOU 0.8917 vs. 0.8603) even when the training dataset size was reduced from 90% (30 078 images) to 10% (3342 images). When directly applying the weight from one certain surgical scenario to recognise the same target in images of other scenarios without training, CNNs still obtained an optimal mIOU of 0.6190 ± 0.0789.
Conclusions
Model performance can be improved with TL in datasets with reduced size and increased complexity. It is feasible for data-based domain adaptation among different microsurgical fields.
{"title":"Transfer learning for anatomical structure segmentation in otorhinolaryngology microsurgery","authors":"Xin Ding, Yu Huang, Yang Zhao, Xu Tian, Guodong Feng, Zhiqiang Gao","doi":"10.1002/rcs.2634","DOIUrl":"10.1002/rcs.2634","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Reducing the annotation burden is an active and meaningful area of artificial intelligence (AI) research.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Multiple datasets for the segmentation of two landmarks were constructed based on 41 257 labelled images and 6 different microsurgical scenarios. These datasets were trained using the multi-stage transfer learning (TL) methodology.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The multi-stage TL enhanced segmentation performance over baseline (mIOU 0.6892 vs. 0.8869). Besides, Convolutional Neural Networks (CNNs) achieved a robust performance (mIOU 0.8917 vs. 0.8603) even when the training dataset size was reduced from 90% (30 078 images) to 10% (3342 images). When directly applying the weight from one certain surgical scenario to recognise the same target in images of other scenarios without training, CNNs still obtained an optimal mIOU of 0.6190 ± 0.0789.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Model performance can be improved with TL in datasets with reduced size and increased complexity. It is feasible for data-based domain adaptation among different microsurgical fields.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"20 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141066207","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}
Luis E. Delgadillo Chabolla, Linda A. Alpuing Radilla, Tamisa Koythong, Sowmya Sunkara, Yamely Mendez, Qianqing Wang, Xiaoming Guan
Background
To assess the feasibility of use of indocyanine green (ICG) in identifying and minimising urinary tract injury during surgical resection of endometriosis through robotic transvaginal natural orifice transluminal endoscopy surgery (RvNOTES).
Methods
We conducted a retrospective case series in two academic tertiary care hospitals. We examined 53 patients who underwent RvNOTES hysterectomy with planned endometriosis resection.
Results
The study involved 53 patients undergoing RvNOTES with ICG fluorescence for endometriosis resection. Mean patient age was 37.98 ± 6.65 years. Operative time averaged 181.32 ± 53.94 min, with estimated blood loss at 45.57 ± 33.62 mL. Postoperative stay averaged 0.23 ± 0.47 days. No ICG-related complications occurred.
Conclusion
No complications occurred with ICG fluorescence in RvNOTES. It appears to be a safe option for ureteral localisation and preservation. ICG fluorescence is widely used in diverse medical specialities for identifying ureters during complex surgeries. Larger studies are needed to firmly establish its advantages in intraoperative ureteral visualisation during RvNOTES for deep infiltrative endometriosis.
{"title":"Exploring the feasibility of indocyanine green fluorescence for intraoperative ureteral visualisation in robotic transvaginal natural orifice transluminal endoscopy surgery during endometriosis resection","authors":"Luis E. Delgadillo Chabolla, Linda A. Alpuing Radilla, Tamisa Koythong, Sowmya Sunkara, Yamely Mendez, Qianqing Wang, Xiaoming Guan","doi":"10.1002/rcs.2636","DOIUrl":"https://doi.org/10.1002/rcs.2636","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>To assess the feasibility of use of indocyanine green (ICG) in identifying and minimising urinary tract injury during surgical resection of endometriosis through robotic transvaginal natural orifice transluminal endoscopy surgery (RvNOTES).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We conducted a retrospective case series in two academic tertiary care hospitals. We examined 53 patients who underwent RvNOTES hysterectomy with planned endometriosis resection.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The study involved 53 patients undergoing RvNOTES with ICG fluorescence for endometriosis resection. Mean patient age was 37.98 ± 6.65 years. Operative time averaged 181.32 ± 53.94 min, with estimated blood loss at 45.57 ± 33.62 mL. Postoperative stay averaged 0.23 ± 0.47 days. No ICG-related complications occurred.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>No complications occurred with ICG fluorescence in RvNOTES. It appears to be a safe option for ureteral localisation and preservation. ICG fluorescence is widely used in diverse medical specialities for identifying ureters during complex surgeries. Larger studies are needed to firmly establish its advantages in intraoperative ureteral visualisation during RvNOTES for deep infiltrative endometriosis.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"20 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140952999","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}
Peng Yan, Xudong Duan, Yutian Lei, Fangze Xing, Ruomu Cao, Sen Luo, Yang Chen, Zeyu Liu, Kunzheng Wang, Pei Yang, Run Tian
Background
Rotational alignment in total knee arthroplasty (TKA) is a crucial technical point that needs attention. We conducted a retrospective study to investigate whether a new robot-assisted TKA (RA-TKA) could improve the accuracy of rotational alignment and whether rotational alignment affects postoperative pain and functional evaluation of the knee.
Methods
A total of 136 consecutive patients who underwent TKA were included in this study. Half of the patients underwent RA-TKA and the other half underwent conventional TKA (CON-TKA) by the same group of surgeons. Collect the relevant parameters.
Results
The postoperative femoral rotation angle (FRA) was −0.72 ± 2.59° in the robot-assisted group and 1.13 ± 2.73° in the conventional group, and were statistically significantly different (p < 0.001).
Conclusion
This study provides preliminary evidence that the RA-TKA provides more precise control of FRA than CON-TKA, and verifies that tibial rotation angle and combined rotation angle affect postoperative knee pain and functional evaluation.
{"title":"Robot-assisted total knee arthroplasty system provides more precise control of the femoral rotation angle: A retrospective study","authors":"Peng Yan, Xudong Duan, Yutian Lei, Fangze Xing, Ruomu Cao, Sen Luo, Yang Chen, Zeyu Liu, Kunzheng Wang, Pei Yang, Run Tian","doi":"10.1002/rcs.2635","DOIUrl":"10.1002/rcs.2635","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Rotational alignment in total knee arthroplasty (TKA) is a crucial technical point that needs attention. We conducted a retrospective study to investigate whether a new robot-assisted TKA (RA-TKA) could improve the accuracy of rotational alignment and whether rotational alignment affects postoperative pain and functional evaluation of the knee.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A total of 136 consecutive patients who underwent TKA were included in this study. Half of the patients underwent RA-TKA and the other half underwent conventional TKA (CON-TKA) by the same group of surgeons. Collect the relevant parameters.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The postoperative femoral rotation angle (FRA) was −0.72 ± 2.59° in the robot-assisted group and 1.13 ± 2.73° in the conventional group, and were statistically significantly different (<i>p</i> < 0.001).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This study provides preliminary evidence that the RA-TKA provides more precise control of FRA than CON-TKA, and verifies that tibial rotation angle and combined rotation angle affect postoperative knee pain and functional evaluation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"20 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140909695","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}
Allergic rhinitis constitutes a widespread health concern, with traditional treatments often proving to be painful and ineffective. Acupuncture targeting the pterygopalatine fossa proves effective but is complicated due to the intricate nearby anatomy.
Methods
To enhance the safety and precision in targeting the pterygopalatine fossa, we introduce a deep learning-based model to refine the segmentation of the pterygopalatine fossa. Our model expands the U-Net framework with DenseASPP and integrates an attention mechanism for enhanced precision in the localisation and segmentation of the pterygopalatine fossa.
Results
The model achieves Dice Similarity Coefficient of 93.89% and 95% Hausdorff Distance of 2.53 mm with significant precision. Remarkably, it only uses 1.98 M parameters.
Conclusions
Our deep learning approach yields significant advancements in localising and segmenting the pterygopalatine fossa, providing a reliable basis for guiding pterygopalatine fossa-assisted punctures.
{"title":"Automatic pterygopalatine fossa segmentation and localisation based on DenseASPP","authors":"Bing Wang, Weili Shi","doi":"10.1002/rcs.2633","DOIUrl":"https://doi.org/10.1002/rcs.2633","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Allergic rhinitis constitutes a widespread health concern, with traditional treatments often proving to be painful and ineffective. Acupuncture targeting the pterygopalatine fossa proves effective but is complicated due to the intricate nearby anatomy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>To enhance the safety and precision in targeting the pterygopalatine fossa, we introduce a deep learning-based model to refine the segmentation of the pterygopalatine fossa. Our model expands the U-Net framework with DenseASPP and integrates an attention mechanism for enhanced precision in the localisation and segmentation of the pterygopalatine fossa.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The model achieves Dice Similarity Coefficient of 93.89% and 95% Hausdorff Distance of 2.53 mm with significant precision. Remarkably, it only uses 1.98 M parameters.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our deep learning approach yields significant advancements in localising and segmenting the pterygopalatine fossa, providing a reliable basis for guiding pterygopalatine fossa-assisted punctures.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"20 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140639573","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}
Anjelica Alfonso, Kimberly N. McFarland, Kush Savsani, Seung Lee, Daisuke Imai, Aamir Khan, Amit Sharma, Muhammad Saeed, Vinay Kumaran, Adrian Cotterell, David Bruno, Marlon Levy
Background
Cholecystoduodenal fistula (CDF) arises from persistent biliary tree disorders, causing fusion between the gallbladder and duodenum. Initially, open resection was common until laparoscopic fistula closure gained popularity. However, complexities within the gallbladder fossa yielded inconsistent outcomes. Advanced imaging and robotic surgery now enhance precision and detection.
Method
A 62-year-old woman with chronic cholangitis attributed to cholecystoduodenal fistula underwent successful robotic cholecystectomy and fistula closure.
Results
Postoperatively, the symptoms subsided with no complications during the robotic procedure. Existing studies report favourable outcomes for robotic cholecystectomy and fistula closure.
Conclusions
Our case report showcases a rare instance of successful robotic cholecystectomy with CDF closure. This case, along with a review of previous cases, suggests the potential of robotic surgery as the preferred approach, especially for patients anticipated to face significant laparoscopic morbidity.
{"title":"Robotic management of primary cholecystoduodenal fistula: A case report and brief literature review","authors":"Anjelica Alfonso, Kimberly N. McFarland, Kush Savsani, Seung Lee, Daisuke Imai, Aamir Khan, Amit Sharma, Muhammad Saeed, Vinay Kumaran, Adrian Cotterell, David Bruno, Marlon Levy","doi":"10.1002/rcs.2629","DOIUrl":"https://doi.org/10.1002/rcs.2629","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Cholecystoduodenal fistula (CDF) arises from persistent biliary tree disorders, causing fusion between the gallbladder and duodenum. Initially, open resection was common until laparoscopic fistula closure gained popularity. However, complexities within the gallbladder fossa yielded inconsistent outcomes. Advanced imaging and robotic surgery now enhance precision and detection.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>A 62-year-old woman with chronic cholangitis attributed to cholecystoduodenal fistula underwent successful robotic cholecystectomy and fistula closure.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Postoperatively, the symptoms subsided with no complications during the robotic procedure. Existing studies report favourable outcomes for robotic cholecystectomy and fistula closure.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our case report showcases a rare instance of successful robotic cholecystectomy with CDF closure. This case, along with a review of previous cases, suggests the potential of robotic surgery as the preferred approach, especially for patients anticipated to face significant laparoscopic morbidity.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"20 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rcs.2629","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140622647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuzuru Sambommatsu, Seung Duk Lee, Daisuke Imai, Kush Savsani, Aamir A. Khan, Amit Sharma, Muhammad Saeed, Adrian H. Cotterell, Vinay Kumaran, Marlon F. Levy, David A. Bruno
Background
Liver parenchymal transection during robotic liver resection (RLR) remains a significant challenge due to the limited range of specialised instruments. This study introduces our ‘Burn and Push’ technique as a novel approach to address these challenges.
Methods
A retrospective analysis was conducted on 20 patients who underwent RLR using the ‘Burn and Push’ technique at Virginia Commonwealth University Health System from November 2021 to August 2023. The study evaluated peri- and post-operative outcomes.
Results
The median operation time was 241.5 min (range, 90–620 min), and the median blood loss was 100 mL (range, 10–600 mL). Major complications occurred in one case, with no instances of postoperative bleeding, bile leak, or liver failure.
Conclusions
The ‘Burn and Push’ technique is a viable and efficient alternative for liver parenchymal transection in RLR. Further research with larger sample sizes and consideration of the learning curve is necessary to validate these findings.
{"title":"‘Burn and Push’ technique: A novel robotic liver parenchymal transection technique","authors":"Yuzuru Sambommatsu, Seung Duk Lee, Daisuke Imai, Kush Savsani, Aamir A. Khan, Amit Sharma, Muhammad Saeed, Adrian H. Cotterell, Vinay Kumaran, Marlon F. Levy, David A. Bruno","doi":"10.1002/rcs.2631","DOIUrl":"https://doi.org/10.1002/rcs.2631","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Liver parenchymal transection during robotic liver resection (RLR) remains a significant challenge due to the limited range of specialised instruments. This study introduces our ‘Burn and Push’ technique as a novel approach to address these challenges.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A retrospective analysis was conducted on 20 patients who underwent RLR using the ‘Burn and Push’ technique at Virginia Commonwealth University Health System from November 2021 to August 2023. The study evaluated peri- and post-operative outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The median operation time was 241.5 min (range, 90–620 min), and the median blood loss was 100 mL (range, 10–600 mL). Major complications occurred in one case, with no instances of postoperative bleeding, bile leak, or liver failure.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The ‘Burn and Push’ technique is a viable and efficient alternative for liver parenchymal transection in RLR. Further research with larger sample sizes and consideration of the learning curve is necessary to validate these findings.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"20 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rcs.2631","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140622678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Constantinos Loukas, Ioannis Seimenis, Konstantina Prevezanou, Dimitrios Schizas
Background
Real-time prediction of the remaining surgery duration (RSD) is important for optimal scheduling of resources in the operating room.
Methods
We focus on the intraoperative prediction of RSD from laparoscopic video. An extensive evaluation of seven common deep learning models, a proposed one based on the Transformer architecture (TransLocal) and four baseline approaches, is presented. The proposed pipeline includes a CNN-LSTM for feature extraction from salient regions within short video segments and a Transformer with local attention mechanisms.
Results
Using the Cholec80 dataset, TransLocal yielded the best performance (mean absolute error (MAE) = 7.1 min). For long and short surgeries, the MAE was 10.6 and 4.4 min, respectively. Thirty minutes before the end of surgery MAE = 6.2 min, 7.2 and 5.5 min for all long and short surgeries, respectively.
Conclusions
The proposed technique achieves state-of-the-art results. In the future, we aim to incorporate intraoperative indicators and pre-operative data.
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