Comparing a virtual reality head-mounted display to on-screen three-dimensional visualization and two-dimensional computed tomography data for training in decision making in hepatic surgery: a randomized controlled study.

OBJECTIVE: Evaluation of the benefits of a virtual reality (VR) environment with a head-mounted display (HMD) for decision-making in liver surgery. BACKGROUND: Training in liver surgery involves appraising radiologic images and considering the patient's clinical information. Accurate assessment of 2D-tomography images is complex and requires considerable experience, and often the images are divorced from the clinical information. We present a comprehensive and interactive tool for visualizing operation planning data in a VR environment using a head-mounted-display and compare it to 3D visualization and 2D-tomography. METHODS: Ninety medical students were randomized into three groups (1:1:1 ratio). All participants analyzed three liver surgery patient cases with increasing difficulty. The cases were analyzed using 2D-tomography data (group "2D"), a 3D visualization on a 2D display (group "3D") or within a VR environment (group "VR"). The VR environment was displayed using the "Oculus Rift ™" HMD technology. Participants answered 11 questions on anatomy, tumor involvement and surgical decision-making and 18 evaluative questions (Likert scale). RESULTS: Sum of correct answers were significantly higher in the 3D (7.1 ± 1.4, p < 0.001) and VR (7.1 ± 1.4, p < 0.001) groups than the 2D group (5.4 ± 1.4) while there was no difference between 3D and VR (p = 0.987). Times to answer in the 3D (6:44 ± 02:22 min, p < 0.001) and VR (6:24 ± 02:43 min, p < 0.001) groups were significantly faster than the 2D group (09:13 ± 03:10 min) while there was no difference between 3D and VR (p = 0.419). The VR environment was evaluated as most useful for identification of anatomic anomalies, risk and target structures and for the transfer of anatomical and pathological information to the intraoperative situation in the questionnaire. CONCLUSIONS: A VR environment with 3D visualization using a HMD is useful as a surgical training tool to accurately and quickly determine liver anatomy and tumor involvement in surgery.

ICG-augmented hyperspectral imaging for visualization of intestinal perfusion compared to conventional ICG fluorescence imaging: an experimental study.

BACKGROUND: Small bowel malperfusion (SBM) can cause high morbidity and severe surgical consequences. However, there is no standardized objective measuring tool for the quantification of SBM. Indocyanine green (ICG) imaging can be used for visualization, but lacks standardization and objectivity. Hyperspectral imaging (HSI) as a newly emerging technology in medicine might present advantages over conventional ICG fluorescence or in combination with it. METHODS: HSI baseline data from physiological small bowel, avascular small bowel and small bowel after intravenous application of ICG was recorded in a total number of 54 in-vivo pig models. Visualizations of avascular small bowel after mesotomy were compared between HSI only (1), ICG-augmented HSI (IA-HSI) (2), clinical evaluation through the eyes of the surgeon (3) and conventional ICG imaging (4). The primary research focus was the localization of resection borders as suggested by each of the four methods. Distances between these borders were measured and histological samples were obtained from the regions in between in order to quantify necrotic changes 6 h after mesotomy for every region. RESULTS: StO2 images (1) were capable of visualizing areas of physiological perfusion and areas of clearly impaired perfusion. However, exact borders where physiological perfusion started to decrease could not be clearly identified. Instead, IA-HSI (2) suggested a sharp-resection line where StO2 values started to decrease. Clinical evaluation (3) suggested a resection line 23 mm (±7 mm) and conventional ICG imaging (4) even suggested a resection line 53 mm (±13 mm) closer towards the malperfused region. Histopathological evaluation of the region that was sufficiently perfused only according to conventional ICG (R3) already revealed a significant increase in pre-necrotic changes in 27% (±9%) of surface area. Therefore, conventional ICG seems less sensitive than IA-HSI with regards to detection of insufficient tissue perfusion. CONCLUSIONS: In this experimental animal study, IA-HSI (2) was superior for the visualization of segmental SBM compared to conventional HSI imaging (1), clinical evaluation (3) or conventional ICG imaging (4) regarding histopathological safety. ICG application caused visual artifacts in the StO2 values of the HSI camera as values significantly increase. This is caused by optical properties of systemic ICG and does not resemble a true increase in oxygenation levels. However, this empirical finding can be used to visualize segmental SBM utilizing ICG as contrast agent in an approach for IA-HSI. Clinical applicability and relevance will have to be explored in clinical trials. LEVEL OF EVIDENCE: Not applicable. Translational animal science. Original article.

Spectral organ fingerprints for machine learning-based intraoperative tissue classification with hyperspectral imaging in a porcine model.

Visual discrimination of tissue during surgery can be challenging since different tissues appear similar to the human eye. Hyperspectral imaging (HSI) removes this limitation by associating each pixel with high-dimensional spectral information. While previous work has shown its general potential to discriminate tissue, clinical translation has been limited due to the method's current lack of robustness and generalizability. Specifically, the scientific community is lacking a comprehensive spectral tissue atlas, and it is unknown whether variability in spectral reflectance is primarily explained by tissue type rather than the recorded individual or specific acquisition conditions. The contribution of this work is threefold: (1) Based on an annotated medical HSI data set (9059 images from 46 pigs), we present a tissue atlas featuring spectral fingerprints of 20 different porcine organs and tissue types. (2) Using the principle of mixed model analysis, we show that the greatest source of variability related to HSI images is the organ under observation. (3) We show that HSI-based fully-automatic tissue differentiation of 20 organ classes with deep neural networks is possible with high accuracy (> 95%). We conclude from our study that automatic tissue discrimination based on HSI data is feasible and could thus aid in intraoperative decisionmaking and pave the way for context-aware computer-assisted surgery systems and autonomous robotics.

IMHOTEP: cross-professional evaluation of a three-dimensional virtual reality system for interactive surgical operation planning, tumor board discussion and immersive training for complex liver surgery in a head-mounted display.

BACKGROUND: Virtual reality (VR) with head-mounted displays (HMD) may improve medical training and patient care by improving display and integration of different types of information. The aim of this study was to evaluate among different healthcare professions the potential of an interactive and immersive VR environment for liver surgery that integrates all relevant patient data from different sources needed for planning and training of procedures. METHODS: 3D-models of the liver, other abdominal organs, vessels, and tumors of a sample patient with multiple hepatic masses were created. 3D-models, clinical patient data, and other imaging data were visualized in a dedicated VR environment with an HMD (IMHOTEP). Users could interact with the data using head movements and a computer mouse. Structures of interest could be selected and viewed individually or grouped. IMHOTEP was evaluated in the context of preoperative planning and training of liver surgery and for the potential of broader surgical application. A standardized questionnaire was voluntarily answered by four groups (students, nurses, resident and attending surgeons). RESULTS: In the evaluation by 158 participants (57 medical students, 35 resident surgeons, 13 attending surgeons and 53 nurses), 89.9% found the VR system agreeable to work with. Participants generally agreed that complex cases in particular could be assessed better (94.3%) and faster (84.8%) with VR than with traditional 2D display methods. The highest potential was seen in student training (87.3%), resident training (84.6%), and clinical routine use (80.3%). Least potential was seen in nursing training (54.8%). CONCLUSIONS: The present study demonstrates that using VR with HMD to integrate all available patient data for the preoperative planning of hepatic resections is a viable concept. VR with HMD promises great potential to improve medical training and operation planning and thereby to achieve improvement in patient care.

A learning robot for cognitive camera control in minimally invasive surgery.

BACKGROUND: We demonstrate the first self-learning, context-sensitive, autonomous camera-guiding robot applicable to minimally invasive surgery. The majority of surgical robots nowadays are telemanipulators without autonomous capabilities. Autonomous systems have been developed for laparoscopic camera guidance, however following simple rules and not adapting their behavior to specific tasks, procedures, or surgeons. METHODS: The herein presented methodology allows different robot kinematics to perceive their environment, interpret it according to a knowledge base and perform context-aware actions. For training, twenty operations were conducted with human camera guidance by a single surgeon. Subsequently, we experimentally evaluated the cognitive robotic camera control. A VIKY EP system and a KUKA LWR 4 robot were trained on data from manual camera guidance after completion of the surgeon's learning curve. Second, only data from VIKY EP were used to train the LWR and finally data from training with the LWR were used to re-train the LWR. RESULTS: The duration of each operation decreased with the robot's increasing experience from 1704 s ± 244 s to 1406 s ± 112 s, and 1197 s. Camera guidance quality (good/neutral/poor) improved from 38.6/53.4/7.9 to 49.4/46.3/4.1% and 56.2/41.0/2.8%. CONCLUSIONS: The cognitive camera robot improved its performance with experience, laying the foundation for a new generation of cognitive surgical robots that adapt to a surgeon's needs.

Surgical Performance Is Not Negatively Impacted by Wearing a Commercial Full-Face Mask with Ad Hoc 3D-Printed Filter Connection as a Substitute for Personal Protective Equipment during the COVID-19 Pandemic: A Randomized Controlled Cross-Over Trial.

(1) Background: During the COVID-19 pandemic, shortages in the supply of personal protective equipment (PPE) have become apparent. The idea of using commonly available full-face diving (FFD) masks as a temporary solution was quickly spread across social media. However, it was unknown whether an FFD mask would considerably impair complex surgical tasks. Thus, we aimed to assess laparoscopic surgical performance while wearing an FFD mask as PPE. (2) Methods: In a randomized-controlled cross-over trial, 40 laparoscopically naive medical students performed laparoscopic procedures while wearing an FFD mask with ad hoc 3D-printed connections to heat and moisture exchange (HME) filters vs. wearing a common surgical face mask. The performance was evaluated using global and specific Objective Structured Assessment of Technical Skills (OSATS) checklists for suturing and cholecystectomy. (3) Results: For the laparoscopic cholecystectomy, both global OSATS scores and specific OSATS scores for the quality of procedure were similar (Group 1: 25 ± 4.3 and 45.7 ± 12.9, p = 0.485, vs. Group 2: 24.1 ± 3.7 and 43.3 ± 7.6, p = 0.485). For the laparoscopic suturing task, the FFD mask group needed similar times to the surgical mask group (3009 ± 1694 s vs. 2443 ± 949 s; p = 0.200). Some participants reported impaired verbal communication while wearing the FFD mask, as it muffled the sound of speech, as well as discomfort in breathing. (4) Conclusions: FFD masks do not affect the quality of laparoscopic surgical performance, despite being uncomfortable, and may therefore be used as a substitute for conventional PPE in times of shortage-i.e., the global COVID-19 pandemic.

The emerging field of pancreatic tissue engineering: A systematic review and evidence map of scaffold materials and scaffolding techniques for insulin-secreting cells.

A bioartificial endocrine pancreas is proposed as a future alternative to current treatment options. Patients with insulin-secretion deficiency might benefit. This is the first systematic review that provides an overview of scaffold materials and techniques for insulin-secreting cells or cells to be differentiated into insulin-secreting cells. An electronic literature survey was conducted in PubMed/MEDLINE and Web of Science, limited to the past 10 years. A total of 197 articles investigating 60 different materials met the inclusion criteria. The extracted data on materials, cell types, study design, and transplantation sites were plotted into two evidence gap maps. Integral parts of the tissue engineering network such as fabrication technique, extracellular matrix, vascularization, immunoprotection, suitable transplantation sites, and the use of stem cells are highlighted. This systematic review provides an evidence-based structure for future studies. Accumulating evidence shows that scaffold-based tissue engineering can enhance the viability and function or differentiation of insulin-secreting cells both in vitro and in vivo.

Weight Loss and Changes in Adipose Tissue and Skeletal Muscle Volume after Laparoscopic Sleeve Gastrectomy and Roux-en-Y Gastric Bypass: a Prospective Study with 12-Month Follow-Up.

BACKGROUND: This study aimed to evaluate changes in body tissue composition with obesity surgery regarding visceral fat, subcutaneous fat, and skeletal muscle. DESIGN: Prospective non-randomized single-center cohort study METHODS: Whole-body magnetic resonance imaging (MRI) measured volumes of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle (SM) in 31 patients with laparoscopic sleeve gastrectomy (LSG, 20) or Roux-en-Y gastric bypass (RYGB, 11) preoperatively, at three- and 12-months follow-up. RESULTS: Body mass index (BMI) went down from 45.2 ± 6.5 preoperatively to 37.2 ± 5.6 (p < 0.001) at three months and 32.2 ± 5.3 kg/m2 (p < 0.001) at 12 months. SAT went down from 55.0 ± 14.0 L (liter) to 42.2 ± 13.3 L (p < 0.001) at three months and 31.7 ± 10.5 L (p < 0.001) at 12 months (- 42.3%). VAT went down from 6.5 ± 2.3 to 4.5 ± 1.7 (p < 0.001) at three months and 3.1 ± 1.7 L (p < 0.001) at 12 months (- 52.3%). SM went down from 22.7 ± 4.8 to 20.4 ± 3.6 (p = 0.008) at three months and remained 20.2 ± 4.6 L at 12 months (p = 0.17 relative three-month; p = 0.04 relative preop, - 11.1%). Relative loss was higher for VAT than that for SAT (52.3 ± 18.2% vs. 42.3 ± 13.8%; p = 0.03). At 12 months, there was no difference between LSG and RYGB for relative changes in BMI or body tissue composition. CONCLUSION: Postoperatively, there was higher net loss of SAT but higher relative loss of VAT with weight loss. SM was lost only during the first three months. MRI provides accurate evaluation of surgeries' effect on individual patients' tissue composition. This can benefit risk assessment for related cardiovascular and metabolic health but cost-related factors will likely reserve the used methods for research.

One or two trainees per workplace for laparoscopic surgery training courses: results from a randomized controlled trial.

BACKGROUND: There are no standards for optimal utilization of workplaces in laparoscopic training. This study aimed to define whether laparoscopy training should be done alone or in pairs (known as dyad training). METHODS: This was a three-arm randomized controlled trial with laparoscopically naïve medical students (n = 100). Intervention groups participated alone (n = 40) or as dyad (n = 40) in a multimodality training curriculum with e-learning, basic, and procedural skills training using box and VR trainers. The control group (n = 20) had no training. Post-performance of a cadaveric porcine laparoscopic cholecystectomy (LC) was measured as the primary outcome by blinded raters using the objective structured assessment of technical skills (OSATS). Global operative assessment of laparoscopic skills (GOALS), time for LC, and VR performances were secondary outcomes. RESULTS: There were no differences between groups for performance scores [OSATS: alone (40.2 ± 9.8) vs. dyad (39.8 ± 8.6), p = 0.995; alone vs. control (37.1 ± 7.4), p = 0.548; or dyad vs. control, p = 0.590; and GOALS score: alone (10.6 ± 3.0) vs. dyad (10.0 ± 2.7), p = 0.599; alone vs. control (10.1 ± 3.0), p = 0.748; or dyad vs. control, p = 0.998]. Dyad finished LC faster than control [median = 62.5 min (CI 58.0-73.0) vs. 76.5 min (CI 72.0-80+); p = 0.042], while there were no inter-group differences between alone vs. control [median = 69.0 min (CI 62.0-76.0) vs. control; p = 0.099] or alone vs. dyad (p = 0.840). Dyad and alone showed superior performance on the VR trainer vs. control for time, number of movements, and path length, but not for complications and application of cautery. CONCLUSIONS: The curriculum provided trainees with the laparoscopic skills needed to perform LC safely, irrespective of the number of trainees per workplace. Dyad training reduced the operation time needed for LC. Therefore, dyad training seems to be a promising alternative, especially if training time is limited and resources must be used as efficiently as possible. Trial registration German Clinical Trials Register: DRKS00004675.

Mobile, real-time, and point-of-care augmented reality is robust, accurate, and feasible: a prospective pilot study.

BACKGROUND: Augmented reality (AR) systems are currently being explored by a broad spectrum of industries, mainly for improving point-of-care access to data and images. Especially in surgery and especially for timely decisions in emergency cases, a fast and comprehensive access to images at the patient bedside is mandatory. Currently, imaging data are accessed at a distance from the patient both in time and space, i.e., at a specific workstation. Mobile technology and 3-dimensional (3D) visualization of radiological imaging data promise to overcome these restrictions by making bedside AR feasible. METHODS: In this project, AR was realized in a surgical setting by fusing a 3D-representation of structures of interest with live camera images on a tablet computer using marker-based registration. The intent of this study was to focus on a thorough evaluation of AR. Feasibility, robustness, and accuracy were thus evaluated consecutively in a phantom model and a porcine model. Additionally feasibility was evaluated in one male volunteer. RESULTS: In the phantom model (n = 10), AR visualization was feasible in 84% of the visualization space with high accuracy (mean reprojection error ± standard deviation (SD): 2.8 ± 2.7 mm; 95th percentile = 6.7 mm). In a porcine model (n = 5), AR visualization was feasible in 79% with high accuracy (mean reprojection error ± SD: 3.5 ± 3.0 mm; 95th percentile = 9.5 mm). Furthermore, AR was successfully used and proved feasible within a male volunteer. CONCLUSIONS: Mobile, real-time, and point-of-care AR for clinical purposes proved feasible, robust, and accurate in the phantom, animal, and single-trial human model shown in this study. Consequently, AR following similar implementation proved robust and accurate enough to be evaluated in clinical trials assessing accuracy, robustness in clinical reality, as well as integration into the clinical workflow. If these further studies prove successful, AR might revolutionize data access at patient bedside.

Computer-assisted 3D bowel length measurement for quantitative laparoscopy.

BACKGROUND: This study aimed at developing and evaluating a tool for computer-assisted 3D bowel length measurement (BMS) to improve objective measurement in minimally invasive surgery. Standardization and quality of surgery as well as its documentation are currently limited by lack of objective intraoperative measurements. To solve this problem, we developed BMS as a clinical application of Quantitative Laparoscopy (QL). METHODS: BMS processes images from a conventional 3D laparoscope. Computer vision algorithms are used to measure the distance between laparoscopic instruments along a 3D reconstruction of the bowel surface. Preclinical evaluation was performed in phantom, ex vivo porcine, and in vivo porcine models. A bowel length of 70 cm was measured with BMS and compared to a manually obtained ground truth. Afterwards 70 cm of bowel (ground truth) was measured and compared to BMS. RESULTS: Ground truth was 66.1 ± 2.7 cm (relative error + 5.8%) in phantom, 65.8 ± 2.5 cm (relative error + 6.4%) in ex vivo, and 67.5 ± 6.6 cm (relative error + 3.7%) in in vivo porcine evaluation when 70 cm was measured with BMS. Using 70 cm of bowel, BMS measured 75.0 ± 2.9 cm (relative error + 7.2%) in phantom and 74.4 ± 2.8 cm (relative error + 6.3%) in ex vivo porcine evaluation. After thorough preclinical evaluation, BMS was successfully used in a patient undergoing laparoscopic Roux-en-Y gastric bypass for morbid obesity. CONCLUSIONS: QL using BMS was shown to be feasible and was successfully translated from studies on phantom, ex vivo, and in vivo porcine bowel to a clinical feasibility study.

Large-scale medical image annotation with crowd-powered algorithms.

Accurate segmentations in medical images are the foundations for various clinical applications. Advances in machine learning-based techniques show great potential for automatic image segmentation, but these techniques usually require a huge amount of accurately annotated reference segmentations for training. The guiding hypothesis of this paper was that crowd-algorithm collaboration could evolve as a key technique in large-scale medical data annotation. As an initial step toward this goal, we evaluated the performance of untrained individuals to detect and correct errors made by three-dimensional (3-D) medical segmentation algorithms. To this end, we developed a multistage segmentation pipeline incorporating a hybrid crowd-algorithm 3-D segmentation algorithm integrated into a medical imaging platform. In a pilot study of liver segmentation using a publicly available dataset of computed tomography scans, we show that the crowd is able to detect and refine inaccurate organ contours with a quality similar to that of experts (engineers with domain knowledge, medical students, and radiologists). Although the crowds need significantly more time for the annotation of a slice, the annotation rate is extremely high. This could render crowdsourcing a key tool for cost-effective large-scale medical image annotation.

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.

Study protocol for a randomized controlled trial on a multimodal training curriculum for laparoscopic cholecystectomy - LapTrain.

BACKGROUND: Although minimally invasive surgery (MIS) has replaced many open procedures in visceral surgery, technical and psychomotor obstacles remain a constant challenge for surgeons and trainees. However, there are various training curricula enabling surgeons to acquire the visuospatial and psychomotor abilities additionally required when performing MIS. Currently accepted training modalities include box-trainers, organ and animal models as well as completely simulated training environments, realized in virtual reality (VR) trainers. All of these methods facilitate an adequate training prior to patient contact, so patient safety can benefit as well. This study aims to evaluate the benefit of a structured multi-modality laparoscopy training curriculum. METHODS: Junior and senior surgical residents are included (n = 60). Groups are stratified with concern to previous experience and training of participants. The training curriculum consists of a standardized sequence of available modalities and exercises on box- and VR-trainers. Specific consideration applies to the training effect during the repeated performance of a laparoscopic cholecystectomy (LC) between intervention (training in between LCs) and control group (no training in between LCs). Analysis of training effects is performed using a cadaveric model for LC and objectified using the validated scoring system Global Operative Assessment of Laparoscopic Skills (GOALS). DISCUSSION: This study assesses the value of a multimodal training platform in medical education and postgraduate training and aims at illustrating possible guidelines when establishing such a curriculum. Possible factors of influence, such as varying backgrounds, learning motivation and -success among participants are explored in the data analysis and add beneficially to further evaluating the efficacy of such training to more heterogeneous participant groups like medical students and other professionals.

Face validity of the pulsatile organ perfusion trainer for laparoscopic cholecystectomy.

BACKGROUND: The pulsatile organ perfusion (POP) trainer provides training of minimally invasive surgery (MIS) with real instruments and cadaveric organs. It provides training of full procedures with simulation of bleeding. Although widely used, the face validity has not yet been evaluated. This study aimed to establish face validity of the POP trainer for laparoscopic cholecystectomy (LC) and its usefulness compared with other training modalities. MATERIALS AND METHODS: During MIS courses, the participants (n = 52) used the POP trainer to perform LC. Face validity was assessed with questionnaires for realism and usefulness on a five-point Likert scale. Participants were divided into two groups: experts (n = 15) who had performed more than 50 laparoscopic procedures and novices (n = 37) with less than 50 procedures. Secondary aims included the ranking of training modalities, as well as exploration of their specific advantages and disadvantages. RESULTS: The POP trainer was found to be realistic (3.8 ± 0.9) and useful (4.6 ± 0.9). Differences between experts and novices were only found for "The training modality resembles reality" (3.1 ± 0.8 vs. 3.8 ± 0.7; p = 0.010), "The operation on the POP trainer is realistic" (3.4 ± 1.1 vs. 4.5 ± 0.8; p = 0.003), and "It would be desirable to have a POP trainer at my own hospital" (4.2 ± 1.1 vs. 4.8 ± 0.8; p = 0.040). In the ranking, the animal training (1.1 ± 0.3) placed first, the POP trainer (2.3 ± 0.9) second, and the VR trainer (2.8 ± 0.9) and box trainer (2.8 ± 1.1) third. The realistic simulation of animal training was named as an advantage most often, while the unrealistic simulation of the VR trainer was the most often named disadvantage. CONCLUSIONS: The POP trainer was rated a highly realistic and useful training modality with face validity for LC. Differences between experts and novices existed concerning realism and desirability. Future studies should evaluate the POP trainer for more advanced surgical procedures. The POP trainer widens the spectrum of modalities for training of MIS in a safe environment outside the operating room.

Crowdtruth validation: a new paradigm for validating algorithms that rely on image correspondences.

PURPOSE: Feature tracking and 3D surface reconstruction are key enabling techniques to computer-assisted minimally invasive surgery. One of the major bottlenecks related to training and validation of new algorithms is the lack of large amounts of annotated images that fully capture the wide range of anatomical/scene variance in clinical practice. To address this issue, we propose a novel approach to obtaining large numbers of high-quality reference image annotations at low cost in an extremely short period of time. METHODS: The concept is based on outsourcing the correspondence search to a crowd of anonymous users from an online community (crowdsourcing) and comprises four stages: (1) feature detection, (2) correspondence search via crowdsourcing, (3) merging multiple annotations per feature by fitting Gaussian finite mixture models, (4) outlier removal using the result of the clustering as input for a second annotation task. RESULTS: On average, 10,000 annotations were obtained within 24 h at a cost of $100. The annotation of the crowd after clustering and before outlier removal was of expert quality with a median distance of about 1 pixel to a publically available reference annotation. The threshold for the outlier removal task directly determines the maximum annotation error, but also the number of points removed. CONCLUSIONS: Our concept is a novel and effective method for fast, low-cost and highly accurate correspondence generation that could be adapted to various other applications related to large-scale data annotation in medical image computing and computer-assisted interventions.

Crowdsourcing for reference correspondence generation in endoscopic images.

Computer-assisted minimally-invasive surgery (MIS) is often based on algorithms that require establishing correspondences between endoscopic images. However, reference annotations frequently required to train or validate a method are extremely difficult to obtain because they are typically made by a medical expert with very limited resources, and publicly available data sets are still far too small to capture the wide range of anatomical/scene variance. Crowdsourcing is a new trend that is based on outsourcing cognitive tasks to many anonymous untrained individuals from an online community. To our knowledge, this paper is the first to investigate the concept of crowdsourcing in the context of endoscopic video image annotation for computer-assisted MIS. According to our study on publicly available in vivo data with manual reference annotations, anonymous non-experts obtain a median annotation error of 2 px (n = 10,000). By applying cluster analysis to multiple annotations per correspondence, this error can be reduced to about 1 px, which is comparable to that obtained by medical experts (n = 500). We conclude that crowdsourcing is a viable method for generating high quality reference correspondences in endoscopic video images.

Can masses of non-experts train highly accurate image classifiers? A crowdsourcing approach to instrument segmentation in laparoscopic images.

Machine learning algorithms are gaining increasing interest in the context of computer-assisted interventions. One of the bottlenecks so far, however, has been the availability of training data, typically generated by medical experts with very limited resources. Crowdsourcing is a new trend that is based on outsourcing cognitive tasks to many anonymous untrained individuals from an online community. In this work, we investigate the potential of crowdsourcing for segmenting medical instruments in endoscopic image data. Our study suggests that (1) segmentations computed from annotations of multiple anonymous non-experts are comparable to those made by medical experts and (2) training data generated by the crowd is of the same quality as that annotated by medical experts. Given the speed of annotation, scalability and low costs, this implies that the scientific community might no longer need to rely on experts to generate reference or training data for certain applications. To trigger further research in endoscopic image processing, the data used in this study will be made publicly available.

Context-aware Augmented Reality in laparoscopic surgery.

Augmented Reality is a promising paradigm for intraoperative assistance. Yet, apart from technical issues, a major obstacle to its clinical application is the man-machine interaction. Visualization of unnecessary, obsolete or redundant information may cause confusion and distraction, reducing usefulness and acceptance of the assistance system. We propose a system capable of automatically filtering available information based on recognized phases in the operating room. Our system offers a specific selection of available visualizations which suit the surgeon's needs best. The system was implemented for use in laparoscopic liver and gallbladder surgery and evaluated in phantom experiments in conjunction with expert interviews.

Classification and detection of errors in minimally invasive surgery.

PURPOSE: To provide a comprehensive review of the classification of surgical errors as well as general measures to detect and prevent their occurrence. MATERIALS AND METHODS: Search in PubMed, Medline, and Cochrane library with combination of the key words: Endoscopy or surgical procedures, minimally invasive, and medical error. Relevant articles were selected by three senior authors involved in minimally invasive surgery (MIS). RESULTS: Error is an unintended healthcare outcome caused by a defect in the delivery of care to a patient. Surgical errors are common and account for half of all hospital adverse events (AEs). Urology is the fifth specialty in decreasing order of AE. Errors may be classified according to the place where they occur (co-face or systemic), to the outcomes (near miss, recovery, and remediation). A specific classification for errors in MIS has also been described (Cushieri), depending on the step of the surgical procedure in which they occur. Each classification serves definite purposes, and no one can be definitive over the others. No classification has been applied so far to urology. Detection through appropriate reporting is the basis for prevention. CONCLUSION: Surgical errors represent a significant proportion of all medical error. Multiple classifications exist, depending on the purposes they are intended to serve. A classification based on the place of occurrence of the errors has been adopted in the medical system; however, when referring to MIS, a finer classification is proposed.