Intraoperative liver deformation and organ motion caused by ventilation, laparotomy, and pneumoperitoneum in a porcine model for image-guided liver surgery.

BACKGROUND: Image-guidance promises to make complex situations in liver interventions safer. Clinical success is limited by intraoperative organ motion due to ventilation and surgical manipulation. The aim was to assess influence of different ventilatory and operative states on liver motion in an experimental model. METHODS: Liver motion due to ventilation (expiration, middle, and full inspiration) and operative state (native, laparotomy, and pneumoperitoneum) was assessed in a live porcine model (n = 10). Computed tomography (CT)-scans were taken for each pig for each possible combination of factors. Liver motion was measured by the vectors between predefined landmarks along the hepatic vein tree between CT scans after image segmentation. RESULTS: Liver position changed significantly with ventilation. Peripheral regions of the liver showed significantly higher motion (maximal Euclidean motion 17.9 ± 2.7 mm) than central regions (maximal Euclidean motion 12.6 ± 2.1 mm, p < 0.001) across all operative states. The total average motion measured 11.6 ± 0.7 mm (p < 0.001). Between the operative states, the position of the liver changed the most from native state to pneumoperitoneum (14.6 ± 0.9 mm, p < 0.001). From native state to laparotomy comparatively, the displacement averaged 9.8 ± 1.2 mm (p < 0.001). With pneumoperitoneum, the breath-dependent liver motion was significantly reduced when compared to other modalities. Liver motion due to ventilation was 7.7 ± 0.6 mm during pneumoperitoneum, 13.9 ± 1.1 mm with laparotomy, and 13.5 ± 1.4 mm in the native state (p < 0.001 in all cases). CONCLUSIONS: Ventilation and application of pneumoperitoneum caused significant changes in liver position. Liver motion was reduced but clearly measurable during pneumoperitoneum. Intraoperative guidance/navigation systems should therefore account for ventilation and intraoperative changes of liver position and peripheral deformation.

Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark.

PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multicenter setting including more difficult recognition tasks such as surgical action and surgical skill. METHODS: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 h was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. RESULTS: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n = 9 teams), for instrument presence detection between 38.5% and 63.8% (n = 8 teams), but for action recognition only between 21.8% and 23.3% (n = 5 teams). The average absolute error for skill assessment was 0.78 (n = 1 team). CONCLUSION: Surgical workflow and skill analysis are promising technologies to support the surgical team, but there is still room for improvement, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost importance to create more open, high-quality datasets in order to allow the development of artificial intelligence and cognitive robotics in surgery.

Development of biotissue training models for anastomotic suturing in pancreatic surgery.

BACKGROUND: Anastomotic suturing is the Achilles heel of pancreatic surgery. Especially in laparoscopic and robotically assisted surgery, the pancreatic anastomosis should first be trained outside the operating room. Realistic training models are therefore needed. METHODS: Models of the pancreas, small bowel, stomach, bile duct, and a realistic training torso were developed for training of anastomoses in pancreatic surgery. Pancreas models with soft and hard textures, small and large ducts were incrementally developed and evaluated. Experienced pancreatic surgeons (n = 44) evaluated haptic realism, rigidity, fragility of tissues, and realism of suturing and knot tying. RESULTS: In the iterative development process the pancreas models showed high haptic realism and highest realism in suturing (4.6 ± 0.7 and 4.9 ± 0.5 on 1-5 Likert scale, soft pancreas). The small bowel model showed highest haptic realism (4.8 ± 0.4) and optimal wall thickness (0.1 ± 0.4 on -2 to +2 Likert scale) and suturing behavior (0.1 ± 0.4). The bile duct models showed optimal wall thickness (0.3 ± 0.8 and 0.4 ± 0.8 on -2 to +2 Likert scale) and optimal tissue fragility (0 ± 0.9 and 0.3 ± 0.7). CONCLUSION: The biotissue training models showed high haptic realism and realistic suturing behavior. They are suitable for realistic training of anastomoses in pancreatic surgery which may improve patient outcomes.

Robust deep learning-based semantic organ segmentation in hyperspectral images.

Semantic image segmentation is an important prerequisite for context-awareness and autonomous robotics in surgery. The state of the art has focused on conventional RGB video data acquired during minimally invasive surgery, but full-scene semantic segmentation based on spectral imaging data and obtained during open surgery has received almost no attention to date. To address this gap in the literature, we are investigating the following research questions based on hyperspectral imaging (HSI) data of pigs acquired in an open surgery setting: (1) What is an adequate representation of HSI data for neural network-based fully automated organ segmentation, especially with respect to the spatial granularity of the data (pixels vs. superpixels vs. patches vs. full images)? (2) Is there a benefit of using HSI data compared to other modalities, namely RGB data and processed HSI data (e.g. tissue parameters like oxygenation), when performing semantic organ segmentation? According to a comprehensive validation study based on 506 HSI images from 20 pigs, annotated with a total of 19 classes, deep learning-based segmentation performance increases - consistently across modalities - with the spatial context of the input data. Unprocessed HSI data offers an advantage over RGB data or processed data from the camera provider, with the advantage increasing with decreasing size of the input to the neural network. Maximum performance (HSI applied to whole images) yielded a mean DSC of 0.90 ((standard deviation (SD)) 0.04), which is in the range of the inter-rater variability (DSC of 0.89 ((standard deviation (SD)) 0.07)). We conclude that HSI could become a powerful image modality for fully-automatic surgical scene understanding with many advantages over traditional imaging, including the ability to recover additional functional tissue information. Our code and pre-trained models are available at https://github.com/IMSY-DKFZ/htc.

Toward 3D-bioprinting of an endocrine pancreas: A building-block concept for bioartificial insulin-secreting tissue.

Three-dimensional bioprinting of an endocrine pancreas is a promising future curative treatment for patients with insulin secretion deficiency. In this study, we present an end-to-end concept from the molecular to the macroscopic level. Building-blocks for a hybrid scaffold device of hydrogel and functionalized polycaprolactone were manufactured by 3D-(bio)printing. Pseudoislet formation from INS-1 cells after bioprinting resulted in a viable and proliferative experimental model. Transcriptomics showed an upregulation of proliferative and ß-cell-specific signaling cascades, downregulation of apoptotic pathways, overexpression of extracellular matrix proteins, and VEGF induced by pseudoislet formation and 3D-culture. Co-culture with endothelial cells created a natural cellular niche with enhanced insulin secretion after glucose stimulation. Survival and function of pseudoislets after explantation and extensive scaffold vascularization of both hydrogel and heparinized polycaprolactone were demonstrated in vivo. Computer simulations of oxygen, glucose and insulin flows were used to evaluate scaffold architectures and Langerhans islets at a future perivascular transplantation site.

Surgical data science - from concepts toward clinical translation.

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.

Comparison of Conventional Methods for Bowel Length Measurement in Laparoscopic Surgery to a Novel Computer-Assisted 3D Measurement System.

PURPOSE: Accurate laparoscopic bowel length measurement (LBLM), which is used primarily in metabolic surgery, remains a challenge. This study aims to three conventional methods for LBLM, namely using visual judgment (VJ), instrument markings (IM), or premeasured tape (PT) to a novel computer-assisted 3D measurement system (BMS). MATERIALS AND METHODS: LBLM methods were compared using a 3D laparoscope on bowel phantoms regarding accuracy (relative error in percent, %), time in seconds (s), and number of bowel grasps. Seventy centimeters were measured seven times. As a control, the first, third, fifth, and seventh measurements were performed with VJ. The interventions IM, PT, and BMS were performed following a randomized order as the second, fourth, and sixth measurements. RESULTS: In total, 63 people participated. BMS showed better accuracy (2.1±3.7%) compared to VJ (8.7±13.7%, p=0.001), PT (4.3±6.8%, p=0.002), and IM (11±15.3%, p<0.001). Participants performed LBLM in a similar amount of time with BMS (175.7±59.7s) and PT (166.5±63.6s, p=0.35), but VJ (64.0±24.0s, p<0.001) and IM (144.9±55.4s, p=0.002) were faster. Number of bowel grasps as a measure for the risk of bowel lesions was similar for BMS (15.8±3.0) and PT (15.9±4.6, p=0.861), whereas VJ required less (14.1±3.4, p=0.004) and IM required more than BMS (22.2±6.9, p<0.001). CONCLUSIONS: PT had higher accuracy than VJ and IM, and lower number of bowel grasps than IM. BMS shows great potential for more reliable LBLM. Until BMS is available in clinical routine, PT should be preferred for LBLM.

Evidence Map of Pancreatic Surgery-A living systematic review with meta-analyses by the International Study Group of Pancreatic Surgery (ISGPS).

BACKGROUND: Pancreatic surgery is associated with considerable morbidity and, consequently, offers a large and complex field for research. To prioritize relevant future scientific projects, it is of utmost importance to identify existing evidence and uncover research gaps. Thus, the aim of this project was to create a systematic and living Evidence Map of Pancreatic Surgery. METHODS: PubMed, the Cochrane Central Register of Controlled Trials, and Web of Science were systematically searched for all randomized controlled trials and systematic reviews on pancreatic surgery. Outcomes from every existing randomized controlled trial were extracted, and trial quality was assessed. Systematic reviews were used to identify an absence of randomized controlled trials. Randomized controlled trials and systematic reviews on identical subjects were grouped according to research topics. A web-based evidence map modeled after a mind map was created to visualize existing evidence. Meta-analyses of specific outcomes of pancreatic surgery were performed for all research topics with more than 3 randomized controlled trials. For partial pancreatoduodenectomy and distal pancreatectomy, pooled benchmarks for outcomes were calculated with a 99% confidence interval. The evidence map undergoes regular updates. RESULTS: Out of 30,860 articles reviewed, 328 randomized controlled trials on 35,600 patients and 332 systematic reviews were included and grouped into 76 research topics. Most randomized controlled trials were from Europe (46%) and most systematic reviews were from Asia (51%). A living meta-analysis of 21 out of 76 research topics (28%) was performed and included in the web-based evidence map. Evidence gaps were identified in 11 out of 76 research topics (14%). The benchmark for mortality was 2% (99% confidence interval: 1%-2%) for partial pancreatoduodenectomy and <1% (99% confidence interval: 0%-1%) for distal pancreatectomy. The benchmark for overall complications was 53% (99%confidence interval: 46%-61%) for partial pancreatoduodenectomy and 59% (99% confidence interval: 44%-80%) for distal pancreatectomy. CONCLUSION: The International Study Group of Pancreatic Surgery Evidence Map of Pancreatic Surgery, which is freely accessible via www.evidencemap.surgery and as a mobile phone app, provides a regularly updated overview of the available literature displayed in an intuitive fashion. Clinical decision making and evidence-based patient information are supported by the primary data provided, as well as by living meta-analyses. Researchers can use the systematic literature search and processed data for their own projects, and funding bodies can base their research priorities on evidence gaps that the map uncovers.

Heidelberg colorectal data set for surgical data science in the sensor operating room.

Image-based tracking of medical instruments is an integral part of surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracking medical instruments based on laparoscopic video data. However, the proposed methods still tend to fail when applied to challenging images and do not generalize well to data they have not been trained on. This paper introduces the Heidelberg Colorectal (HeiCo) data set - the first publicly available data set enabling comprehensive benchmarking of medical instrument detection and segmentation algorithms with a specific emphasis on method robustness and generalization capabilities. Our data set comprises 30 laparoscopic videos and corresponding sensor data from medical devices in the operating room for three different types of laparoscopic surgery. Annotations include surgical phase labels for all video frames as well as information on instrument presence and corresponding instance-wise segmentation masks for surgical instruments (if any) in more than 10,000 individual frames. The data has successfully been used to organize international competitions within the Endoscopic Vision Challenges 2017 and 2019.

Effects of laparoscopy, laparotomy, and respiratory phase on liver volume in a live porcine model for liver resection.

BACKGROUND: Hepatectomy, living donor liver transplantations and other major hepatic interventions rely on precise calculation of the total, remnant and graft liver volume. However, liver volume might differ between the pre- and intraoperative situation. To model liver volume changes and develop and validate such pre- and intraoperative assistance systems, exact information about the influence of lung ventilation and intraoperative surgical state on liver volume is essential. METHODS: This study assessed the effects of respiratory phase, pneumoperitoneum for laparoscopy, and laparotomy on liver volume in a live porcine model. Nine CT scans were conducted per pig (N = 10), each for all possible combinations of the three operative (native, pneumoperitoneum and laparotomy) and respiratory states (expiration, middle inspiration and deep inspiration). Manual segmentations of the liver were generated and converted to a mesh model, and the corresponding liver volumes were calculated. RESULTS: With pneumoperitoneum the liver volume decreased on average by 13.2% (112.7 ml ± 63.8 ml, p < 0.0001) and after laparotomy by 7.3% (62.0 ml ± 65.7 ml, p = 0.0001) compared to native state. From expiration to middle inspiration the liver volume increased on average by 4.1% (31.1 ml ± 55.8 ml, p = 0.166) and from expiration to deep inspiration by 7.2% (54.7 ml ± 51.8 ml, p = 0.007). CONCLUSIONS: Considerable changes in liver volume change were caused by pneumoperitoneum, laparotomy and respiration. These findings provide knowledge for the refinement of available preoperative simulation and operation planning and help to adjust preoperative imaging parameters to best suit the intraoperative situation.

Machine Learning for Surgical Phase Recognition: A Systematic Review.

OBJECTIVE: To provide an overview of ML models and data streams utilized for automated surgical phase recognition. BACKGROUND: Phase recognition identifies different steps and phases of an operation. ML is an evolving technology that allows analysis and interpretation of huge data sets. Automation of phase recognition based on data inputs is essential for optimization of workflow, surgical training, intraoperative assistance, patient safety, and efficiency. METHODS: A systematic review was performed according to the Cochrane recommendations and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. PubMed, Web of Science, IEEExplore, GoogleScholar, and CiteSeerX were searched. Literature describing phase recognition based on ML models and the capture of intraoperative signals during general surgery procedures was included. RESULTS: A total of 2254 titles/abstracts were screened, and 35 full-texts were included. Most commonly used ML models were Hidden Markov Models and Artificial Neural Networks with a trend towards higher complexity over time. Most frequently used data types were feature learning from surgical videos and manual annotation of instrument use. Laparoscopic cholecystectomy was used most commonly, often achieving accuracy rates over 90%, though there was no consistent standardization of defined phases. CONCLUSIONS: ML for surgical phase recognition can be performed with high accuracy, depending on the model, data type, and complexity of surgery. Different intraoperative data inputs such as video and instrument type can successfully be used. Most ML models still require significant amounts of manual expert annotations for training. The ML models may drive surgical workflow towards standardization, efficiency, and objectiveness to improve patient outcome in the future. REGISTRATION PROSPERO: CRD42018108907.

Evidence map of pancreatic surgery: protocol for a living systematic review and meta-analysis.

INTRODUCTION: Pancreatic surgery is a large and complex field of research. Several evidence gaps exist for specific diseases or surgical procedures. An overview on existing knowledge is needed to plan and prioritise future research. The aim of this project is to create a systematic and living evidence map of pancreatic surgery. METHODS AND ANALYSIS: A systematic literature search in MEDLINE (via PubMed), Web of Science and Cochrane Central Register of Controlled Trials will be performed searching for all randomised controlled trials (RCT) and systematic reviews (SR) on pancreatic surgery. RCT and SR will be grouped in research topics. Baseline and outcome data from RCT will be extracted, presented and effect sizes meta-analysed. Data from SR will be used to identify evidence gaps. A freely accessible web-based evidence map in the format of a mind map will be created. The evidence map and meta-analyses will be updated periodically. DISSEMINATION: After completion of the project, a permanently updated evidence map of pancreatic surgery will be available to patients, physicians, researchers and funding bodies via www.evidencemap.surgery. Its use will allow clinical decision-making based on primary data and prioritisation of future research endeavours. PROSPERO REGISTRATION NUMBER: CRD42019133444.

Predictors of Risk and Success of Obesity Surgery.

BACKGROUND: Obesity surgery has proven successful for weight loss and the resolution of comorbidities. There is, however, little evidence on its success and the risk of complications when considering age of onset of obesity (AOO), years of obesity (YOO), preoperative body mass index (BMI), Edmonton obesity staging system (EOSS) score, and age as possible predictors of weight loss, the resolution of comorbidities, and the risk of complications. METHODS: Patients who underwent Roux-en-Y gastric bypass (RYGB) and laparoscopic sleeve gastrectomy (LSG) from a prospective database were analyzed. Multiple regression analyses were used to predict comorbidities and their resolution, percentage excess weight loss (%EWL) and total weight loss (%TWL) 12 months after surgery, and the risk of complications using the predictors AOO, YOO, age, EOSS, and BMI. RESULTS: 180 patients aged 46.8 ± 11.1 years with a preoperative BMI 49.5 ± 7.5 were included. The number of preoperative comorbidities was higher with older age (ß = 0.054; p = 0.023) and a greater BMI (ß = 0.040; p = 0.036) but was not related to AOO and YOO. Patients with AOO as a child or adolescent were more likely to have an EOSS score of ≥2. Greater preoperative BMI was negatively associated with %EWL (ß = -1.236; p < 0.001) and older age was negatively associated with %TWL (ß = -0.344; p = 0.020). Postoperative complications were positively associated with EOSS score (odds ratio [OR] 1.147; p = 0.042) and BMI (OR 1.010; p = 0.020), but not with age. AOO and YOO were not related to postoperative outcome. CONCLUSION: Greater BMI was associated with a lower %EWL and age was associated with a low %TWL. YOO and AOO did not influence outcome. Age, BMI, and EOSS score were the most important predictors for risk and success after obesity surgery. Surgery should be performed early enough for optimal outcomes.

Does rating with a checklist improve the effect of E-learning for cognitive and practical skills in bariatric surgery? A rater-blinded, randomized-controlled trial.

BACKGROUND: Mental training of laparoscopic procedures with E-learning has been shown to translate to the operating room. The present study aims to explore whether the use of checklists during E-learning improves transfer of skills to the simulated OR on a Virtual Reality (VR) trainer for Roux-en-Y gastric bypass (RYGB). METHODS: Laparoscopy naive medical students (n = 80) were randomized in two groups. After an E-learning introduction to RYGB, checklist group rated RYGB videos using the validated Bariatric Objective Structured Assessment of Technical Skills (BOSATS) checklist while group without checklist only observed the videos. Participants then performed RYGB on a VR-trainer twice and were evaluated by a blinded expert rater using BOSATS. A multiple choice (MC) knowledge test on RYGB was performed. Suturing on a cadaveric porcine small bowel was evaluated using objective structured assessment of technical skill (OSATS). RESULTS: Checklist group was better in the knowledge test (A 8.3 ± 1.1 vs. B 7.1 ± 1.3; p ≤ 0.001) and there was a trend towards better VR RYGB performance (BOSATS) on the first try (85.9 ± 10.2 vs. 81.1 ± 11.5; p = 0.058), but not on the second try (92.0 ± 9.7 vs. 89.3 ± 10.5; p = 0.251). Suturing as measured by OSATS was not different (29.5 ± 3.0 vs. 29.0 ± 3.5; p = 0.472). CONCLUSION: This study presents evidence that the use of a BOSATS checklist during E-learning helps trainees to improve their knowledge acquisition with E-learning. The transfer from mental training to the simulated OR environment seems to be partially enhanced by use of the BOSATS checklist. However, more research is required to investigate potential benefits.

2-octyl cyanoacrylate sealing of the pancreatic remnant after distal pancreatectomy - A prospective pilot study.

BACKGROUND: Postoperative pancreatic fistula (POPF) remains a frequent problem especially after distal pancreatectomy. The application of 2-octyl cyanoacrylate showed promising results in the reduction of POPF after pancreatoduodenectomy prompting an expansion of this technique to distal pancreatectomy. Thus, the objective of the current study was to assess safety, feasibility and preliminary efficacy of an intraoperative 2-octyl cyanoacrylate application after distal pancreatectomy. METHODS: Between April 2015 and June 2016 adult patients scheduled for elective distal pancreatectomy were considered eligible for the study. It was planned to include a total of 35 patients. After distal pancreatectomy with hand-sewn closure of the pancreatic remnant, a 2-octyl cyanoacrylate surgical glue was applied to the cut surface of the pancreas. Patients were followed up for three months with main focus on safety in terms of (serious) adverse events. Further endpoints included POPF, other pancreas-specific and surgical complications. RESULTS: 15 patients were included in the study because the manufacturer stopped production and distribution of the investigational device thereafter. There was a total of ten serious adverse events but no device-related events and no mortality. The serious adverse events depicted a typical safety profile after distal pancreatectomy. POPF occurred in five cases (33.3%), delayed gastric emptying and post-pancreatectomy haemorrhage in two cases respectively (13.3%). CONCLUSIONS: Application of 2-octyl cyanoacrylate to the pancreatic remnant after distal pancreatectomy seems feasible and safe. The planned evaluation of preliminary efficacy was not possible due to the inadvertent early termination and subsequent small sample size of the study. Novel techniques for prevention and therapy of POPF should be evaluated in future trials.

Computer tomographic analysis of organ motion caused by respiration and intraoperative pneumoperitoneum in a porcine model for navigated minimally invasive esophagectomy.

BACKGROUND: Navigation systems have the potential to facilitate intraoperative orientation and recognition of anatomical structures. Intraoperative accuracy of navigation in thoracoabdominal surgery depends on soft tissue deformation. We evaluated esophageal motion caused by respiration and pneumoperitoneum in a porcine model for minimally invasive esophagectomy. METHODS: In ten pigs (20-34 kg) under general anesthesia, gastroscopic hemoclips were applied to the cervical (CE), high (T1), middle (T2), and lower thoracic (T3) level, and to the gastroesophageal junction (GEJ) of the esophagus. Furthermore, skin markers were applied. Three-dimensional (3D) and four-dimensional (4D) computed tomography (CT) scans were acquired before and after creation of pneumoperitoneum. Marker positions and lung volumes were analyzed with open source image segmentation software. RESULTS: Respiratory motion of the esophagus was higher at T3 (7.0 ± 3.3 mm, mean ± SD) and GEJ (6.9 ± 2.8 mm) than on T2 (4.5 ± 1.8 mm), T1 (3.1 ± 1.8 mm), and CE (1.3 ± 1.1 mm). There was significant motion correlation in between the esophageal levels. T1 motion correlated with all other esophagus levels (r = 0.51, p = 0.003). Esophageal motion correlated with ventilation volume (419 ± 148 ml) on T1 (r = 0.29), T2 (r = 0.44), T3 (r = 0.54), and GEJ (r = 0.58) but not on CE (r = - 0.04). Motion correlation of the esophagus with skin markers was moderate to high for T1, T2, T3, GEJ, but not evident for CE. Pneumoperitoneum led to considerable displacement of the esophagus (8.2 ± 3.4 mm) and had a level-specific influence on respiratory motion. CONCLUSIONS: The position and motion of the esophagus was considerably influenced by respiration and creation of pneumoperitoneum. Esophageal motion correlated with respiration and skin motion. Possible compensation mechanisms for soft tissue deformation were successfully identified. The porcine model is similar to humans for respiratory esophageal motion and can thus help to develop navigation systems with compensation for soft tissue deformation.

An ad hoc three dimensionally printed tool facilitates intraesophageal suturing in experimental surgery.

BACKGROUND: Three-dimensional printing (3DP) has become popular for development of anatomic models, preoperative planning, and production of tailored implants. A novel laparoscopic, transgastric procedure for distal esophageal mucosectomy was developed. During this procedure, a space holder had to be introduced into the distal esophagus for exposure during suturing. The production process and evaluation of a 3DP space holder are described herein. MATERIALS AND METHODS: Computer-aided design software was used to develop models printed from polylactic acid. The prototype was adapted after testing in a cadaveric model. Subsequently, the device was evaluated in a nonsurvival porcine model. A mucosal purse-string suture was placed as orally as possible in the esophagus, in the intervention group with and in the control group without use of the tool (n = 8 each). The distance of the stitches from the Z-line was measured. The variability of stitches indicated the suture quality. RESULTS: The median maximum distance from the Z-line to purse-string suture was larger in the intervention group (5.0 [3.3-6.4] versus 2.4 [2.0-4.1] cm; P = 0.013). The time taken to place the sutures was shorter in the control group (P < 0.001). Stitch variance tended to be greater in the intervention group (2.3 [0.9-2.5] versus 0.7 [0.2-0.4] cm; P = 0.051). The time required for design and production of a tailored tool was less than 24 h. CONCLUSIONS: 3DP in experimental surgery enables rapid production, permits repeated adaptation until a tailored tool is obtained, and ensures independence from industrial partners. With the aid of the space holder more orally located esophageal lesions came within reach.

Skills in minimally invasive and open surgery show limited transferability to robotic surgery: results from a prospective study.

BACKGROUND: There is limited evidence on the transferability of conventional laparoscopic and open surgical skills to robotic-assisted surgery. The primary aim of this study was to evaluate the transferability of expertise in conventional laparoscopy and open surgery to robotic-assisted surgery using the da Vinci Skills Simulator (dVSS). Secondary aims included evaluating the influence of individual participants' characteristics. METHODS: Participants performed four tasks on the dVSS: Peg Board 1 (PB), Pick and Place (PP), Thread the Rings (TR), and Suture Sponge 1 (SS). Participants were classified into three groups (Novice, Intermediate, Experts) according to experience in laparoscopic and open surgery. All tasks were performed twice except for SS. Performance was assessed using the built-in scoring system. RESULTS: 37 medical students and 25 surgeons participated. Experts did not perform significantly better than less experienced participants on the dVSS. Specifically, with regard to laparoscopic experience, total simulator scores were: Novices 68.2 ± 28.8; Intermediates 65.1 ± 31.2; Experts 65.1 ± 30.0; p = 0.611. Regarding open surgical experience, scores were: Novices 68.6 ± 28.7; Intermediates 68.2 ± 30.8; Experts 63.2 ± 30.3; p = 0.305. Although there were some significant differences among groups for single parameters in specific tasks, there was no constant superiority of one group. Laparoscopic and open surgical Novices improved significantly in overall score and time for all three tasks (p < 0.05). Laparoscopic intermediates improved only in PP time (4.64 ± 3.42; p = 0.006), open Intermediates in PB score (11.98 ± 13.01; p = 0.025), and open Experts in PP score (6.69 ± 11.48; p = 0.048). Laparoscopic experts showed no improvement. Participants with gaming experience had better overall scores than non-gamers when comparing all second attempts (Gamer 83.62 ± 7.57; Non-Gamer 76.31 ± 12.78; p = 0.008) as well as first and second attempts together (Gamer 72.08 ± 8.86; Non-Gamer 65.45 ± 11.68; p = 0.039). Musical and sports experience showed no correlation with robotic performance. CONCLUSIONS: Robotic-assisted surgery requires skills distinct from conventional laparoscopy or open surgery. Basic robotic skills training prior to patient contact should be required.

Bariatric Surgery as an Efficient Treatment for Non-Alcoholic Fatty Liver Disease in a Prospective Study with 1-Year Follow-up : BariScan Study.

BACKGROUND: Bariatric surgery gains attention as a potential treatment for non-alcoholic fatty liver disease (NAFLD). The present study aimed to evaluate improvement of NAFLD after the two most common bariatric procedures with validated non-invasive instruments. MATERIAL AND METHODS: N = 100 patients scheduled for laparoscopic sleeve gastrectomy (LSG) or Roux-en-Y gastric bypass (RYGB) were included. NAFLD was evaluated preoperatively and postoperatively with liver stiffness measurement by transient elastography and laboratory-based fibrosis scores. Clinical data included body mass index (BMI), total weight loss (%TWL), excess weight loss (%EWL), age, gender, comorbidities, and the Edmonton obesity staging system (EOSS). RESULTS: There were significant improvements of BMI, %TWL, %EWL, and EOSS after bariatric surgery. Liver stiffness was significantly improved from pre- to postoperative (12.9 ± 10.4 vs. 7.1 ± 3.7 kPa, p < 0.001) at median follow-up of 12.5 months. Additionally, there were significant improvements of liver fibrosis scores (aspartate aminotransferase (AST)/alanine aminotransferase (ALT) ratio 0.8 ± 0.3 vs. 1.1 ± 0.4, p < 0.001; NAFLD fibrosis score - 1.0 ± 1.8 vs. - 1.7 ± 1.3, p < 0.001; APRI score 0.3 ± 0.2 vs. 0.3 ± 0.1, p = 0.009; BARD score 2.3 ± 1.2 vs. 2.8 ± 1.1, p = 0.008) and laboratory parameters (ALT, AST, and GGT). After adjustment for baseline liver stiffness, RYGB showed higher improvements than LSG, and there was no gender difference. Improvement of liver stiffness was not correlated to improvement of BMI, %TWL, %EWL, or EOSS. CONCLUSIONS: NAFLD seems to be improved by bariatric surgery as measured by validated non-invasive instruments. Furthermore, it appears that RYGB is more effective than LSG. No correlation could be detected between NAFLD and weight loss. The present study highlights the potential of bariatric surgery for successful treatment of NAFLD. Further research is required to understand the exact mechanisms.

Impact of visual-spatial ability on laparoscopic camera navigation training.

BACKGROUND: Technical limitations of minimally invasive surgery challenge both surgeons and camera assistants. Current research indicates that visual-spatial ability (VSA) has impact on learning of laparoscopic camera navigation (LCN). However, it remains unclear if complexity of LCN tasks influences the impact of VSA. The aim of this study was to examine the influence of VSA on LCN training within tasks of different complexity levels. METHODS: The present study was conducted as a monocentric prospective trial. VSA was assessed with a cube comparison test before participants underwent LCN training. LCN training consisted of three tasks with increasing complexity. Each task was performed four times and performance was assessed each time. Correlations and multivariate regression analysis were used to assess the influence of VSA on LCN skills. RESULTS: Seventy-one participants were included (35 males). Significant performance improvement and faster completion times were observed from the first to fourth trial of all three LCN training tasks. Significant positive correlations between VSA and performance on LCN task 3 were found (performance: r s = 0.47; p < 0.001, time: r s = -0.43; p < 0.001). Multivariate regression revealed that higher VSA resulted in greater reduction of time between the first trials of LCN training task 3 (B = -1.67, p = 0.031). CONCLUSION: In the present study, all trainees improved LCN performance during the training. VSA seems to have impact on LCN performance and training progress particularly for complex LCN tasks. The relation of VSA and LCN performance was stronger for less experienced participants and in the beginning of the learning phase.