Laparoscopic transgastric circumferential stapler-assisted vs. endoscopic esophageal mucosectomy in a porcine model

Background and study aims Extensive endoscopic mucosal resection (EMR) for Barrett's esophagus (BE) may lead to stenosis. Laparoscopic, transgastric, stapler-assisted mucosectomy (SAM) with the retrieval of a circumferential specimen is proposed. Methods SAM was evaluated in two phases. The feasibility of SAM and the quality of specimens were assessed in eight animals. The mucosal healing was evaluated in a 6-week survival experiment comparing SAM (n = 6) with EMR (n = 6). The ratio of the esophageal lumen width (REL) at the resection level measured on fluoroscopy at 6 weeks divided by the width immediately after resection was compared. Results In all animals, a circular mucosectomy specimen was successfully obtained, with a median area of 492 mm2 (interquartile range [IQR] 426 - 573 mm2) and 941 mm2 (IQR 813 - 1209 mm2) using a 21 mm and 25 mm stapler, respectively. In the survival experiments, symptomatic stenosis developed in two animals after EMR and in none after SAM. The REL was 0.27 (0.18 - 0.39) and 0.96 (0.9 - 1.04; P < 0.0001) for EMR and SAM, respectively. Conclusions SAM provides a novel technique for en bloc mucosectomy in BE. In contrast to EMR, mucosal healing after SAM was not associated with stenosis up to 6 weeks after intervention

LapSeg3D: Weakly Supervised Semantic Segmentation of Point Clouds Representing Laparoscopic Scenes

The semantic segmentation of surgical scenes is a prerequisite for task automation in robot assisted interventions. We propose LapSeg3D, a novel DNN-based approach for the voxel-wise annotation of point clouds representing surgical scenes. As the manual annotation of training data is highly time consuming, we introduce a semi-autonomous clustering-based pipeline for the annotation of the gallbladder, which is used to generate segmented labels for the DNN. When evaluated against manually annotated data, LapSeg3D achieves an F1 score of 0.94 for gallbladder segmentation on various datasets of ex-vivo porcine livers. We show LapSeg3D to generalize accurately across different gallbladders and datasets recorded with different RGB-D camera systems.Comment: 6 pages, 5 figures, accepted at the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japa

Augmented Reality-based Robot Control for Laparoscopic Surgery

Minimally invasive surgery is the standard formany abdominal interventions, with an increasing use of tele-manipulated robots. As collaborative robots enter the field ofmedical interventions, their intuitive control needs to be ad-dressed. Augmented reality can thereby support a surgeonby representing the surgical scene in a natural way. In thiswork, an augmented reality based robot control for laparo-scopic cholecystectomy is presented. A user can interact withthe virtual scene to clip the cystic duct and artery as well asto manipulate the deformable gallbladder. An evaluation wasperformed based on the SurgTLX and system usability scale

Semantic segmentation of surgical hyperspectral images under geometric domain shifts

Robust semantic segmentation of intraoperative image data could pave the way for automatic surgical scene understanding and autonomous robotic surgery. Geometric domain shifts, however, although common in real-world open surgeries due to variations in surgical procedures or situs occlusions, remain a topic largely unaddressed in the field. To address this gap in the literature, we (1) present the first analysis of state-of-the-art (SOA) semantic segmentation networks in the presence of geometric out-of-distribution (OOD) data, and (2) address generalizability with a dedicated augmentation technique termed "Organ Transplantation" that we adapted from the general computer vision community. According to a comprehensive validation on six different OOD data sets comprising 600 RGB and hyperspectral imaging (HSI) cubes from 33 pigs semantically annotated with 19 classes, we demonstrate a large performance drop of SOA organ segmentation networks applied to geometric OOD data. Surprisingly, this holds true not only for conventional RGB data (drop of Dice similarity coefficient (DSC) by 46 %) but also for HSI data (drop by 45 %), despite the latter's rich information content per pixel. Using our augmentation scheme improves on the SOA DSC by up to 67 % (RGB) and 90 % (HSI) and renders performance on par with in-distribution performance on real OOD test data. The simplicity and effectiveness of our augmentation scheme makes it a valuable network-independent tool for addressing geometric domain shifts in semantic scene segmentation of intraoperative data. Our code and pre-trained models are available at https://github.com/IMSY-DKFZ/htc.Comment: The first two authors (Jan Sellner and Silvia Seidlitz) contributed equally to this pape

Patient Perspective in Obesity Surgery: Goals for Weight Loss and Improvement of Body Shape in a Prospective Cohort Study

Objective: Obesity surgery provides sustainable weight loss, improvement of comorbidities, and improved quality of life (QOL). There is few evidence on the patient perspective and goals. This study compared expected and achieved weight loss, body shape, and QOL. Methods: Patients completed the Moorehead-Ardelt QOL questionnaire (MAQOL) and questionnaires on actual and expected weight loss and body shape, comorbidities, and goals of obesity surgery preoperatively and within 24 months postoperatively. Results: 44 patients completed questionnaires pre- and postoperatively. BMI, MAQOL and comorbidities significantly improved postoperatively. Patients’ expected weight loss goal corresponded to a postoperative BMI of 32.6 ± 5.6 kg/m2 and was not different from their achieved BMI within 24 months after surgery (33.9 ± 6.3 kg/m2, p = 0.276). Self-reported body shape improved but did not reach preoperatively expected goals. During the weight loss period, patients adapted their weight loss and body shape goals to higher levels. Patients attributed a higher part of their success in weight loss to surgery postoperatively (79.5 ± 22.0 vs. 89.1 ± 18.4%, p = 0.028). Conclusion: Patients lost as much weight as they had expected and later modified the goals to even greater weight loss. Body shape improved but did not reach expected levels. QOL improved independently from weight loss and body shape. Patients attributed successful weight loss predominantly to surgery

Does rating the operation videos with a checklist score improve the effect of E-learning for bariatric surgical training? Study protocol for a randomized controlled trial

Background: Laparoscopic training has become an important part of surgical education. Laparoscopic Roux-en-Y gastric bypass (RYGB) is the most common bariatric procedure performed. Surgeons must be well trained prior to operating on a patient. Multimodality training is vital for bariatric surgery. E-learning with videos is a standard approach for training. The present study investigates whether scoring the operation videos with performance checklists improves learning effects and transfer to a simulated operation. Methods/design: This is a monocentric, two-arm, randomized controlled trial. The trainees are medical students from the University of Heidelberg in their clinical years with no prior laparoscopic experience. After a laparoscopic basic virtual reality (VR) training, 80 students are randomized into one of two arms in a 1:1 ratio to the checklist group (group A) and control group without a checklist (group B). After all students are given an introduction of the training center, VR trainer and laparoscopic instruments, they start with E-learning while watching explanations and videos of RYGB. Only group A will perform ratings with a modified Bariatric Objective Structured Assessment of Technical Skill (BOSATS) scale checklist for all videos watched. Group B watches the same videos without rating. Both groups will then perform an RYGB in the VR trainer as a primary endpoint and small bowel suturing as an additional test in the box trainer for evaluation. Discussion: This study aims to assess if E-learning and rating bariatric surgical videos with a modified BOSATS checklist will improve the learning curve for medical students in an RYGB VR performance. This study may help in future laparoscopic and bariatric training courses. Trial registration: German Clinical Trials Register, DRKS00010493. Registered on 20 May 2016

Virtual reality training versus blended learning of laparoscopic cholecystectomy:a randomized controlled trial with laparoscopic novices

This study compared virtual reality (VR) training with low cost-blended learning (BL) in a structured training program. Training of laparoscopic skills outside the operating room is mandatory to reduce operative times and risks. Laparoscopy-naïve medical students were randomized in 2 groups stratified for sex. The BL group (n = 42) used E-learning for laparoscopic cholecystectomy (LC) and practiced basic skills with box trainers. The VR group (n = 42) trained basic skills and LC on the LAP Mentor II (Simbionix, Cleveland, OH). Each group trained 3 × 4 hours followed by a knowledge test concerning LC. Blinded raters assessed the operative performance of cadaveric porcine LC using the Objective Structured Assessment of Technical Skills (OSATS). The LC was discontinued when it was not completed within 80 min. Students evaluated their training modality with questionnaires. The VR group completed the LC significantly faster and more often within 80 min than BL (45% v 21%, P = .02). The BL group scored higher than the VR group in the knowledge test (13.3 ± 1.3 vs 11.0 ± 1.7, P < 0.001). Both groups showed equal operative performance of LC in the OSATS score (49.4 ± 10.5 vs 49.7 ± 12.0, P = 0.90). Students generally liked training and felt well prepared for assisting in laparoscopic surgery. The efficiency of the training was judged higher by the VR group than by the BL group. VR and BL can both be applied for training the basics of LC. Multimodality training programs should be developed that combine the advantages of both approaches

App-based serious gaming for training of chest tube insertion: study protocol for a randomized controlled trial

Background: Chest tube insertion is a standard intervention for management of various injuries of the thorax. Quick and accurate execution facilitates efficient therapy without further complications. Here, we propose a new training concept comprised of e-learning elements as well as continuous rating using an objective structured assessment of technical skills (OSATS) tool. The study protocol is presented for a randomized trial to evaluate e-learning with app-based serious gaming for chest drain insertion. Methods: The proposed randomized trial will be carried out at the Department of Orthopedics and Traumatology at Heidelberg University in the context of regular curricular teaching for medical students (n = 90, 3rd to 6th year). The intervention group will use e-learning with the serious gaming app Touch Surgery (TM) for chest drain insertion, whereas the control group uses serious gaming for an unrelated procedure. Primary endpoint is operative performance of chest drain insertion in a porcine cadaveric model according to OSATS. Discussion: The randomized trial will help determine the value of e-learning with the serious gaming app Touch Surgery (TM) for chest drain insertion by using the OSATS score. The study will improve surgical training for trauma situations. Trial registration: Trial Registration Number, DRKS00009994. Registered on 27 May 2016