Augmented-reality-based skills training for robot-assisted urethrovesical anastomosis: a multi-institutional randomised controlled trial.

OBJECTIVE: To validate robot-assisted surgery skills acquisition using an augmented reality (AR)-based module for urethrovesical anastomosis (UVA). METHODS: Participants at three institutions were randomised to a Hands-on Surgical Training (HoST) technology group or a control group. The HoST group was given procedure-based training for UVA within the haptic-enabled AR-based HoST environment. The control group did not receive any training. After completing the task, the control group was offered to cross over to the HoST group (cross-over group). A questionnaire administered after HoST determined the feasibility and acceptability of the technology. Performance of UVA using an inanimate model on the daVinci Surgical System (Intuitive Surgical Inc., Sunnyvale, CA, USA) was assessed using a UVA evaluation score and a Global Evaluative Assessment of Robotic Skills (GEARS) score. Participants completed the National Aeronautics and Space Administration Task Load Index (NASA TLX) questionnaire for cognitive assessment, as outcome measures. A Wilcoxon rank-sum test was used to compare outcomes among the groups (HoST group vs control group and control group vs cross-over group). RESULTS: A total of 52 individuals participated in the study. UVA evaluation scores showed significant differences in needle driving (3.0 vs 2.3; P = 0.042), needle positioning (3.0 vs 2.4; P = 0.033) and suture placement (3.4 vs 2.6; P = 0.014) in the HoST vs the control group. The HoST group obtained significantly higher scores (14.4 vs 11.9; P 0.012) on the GEARS. The NASA TLX indicated lower temporal demand and effort in the HoST group (5.9 vs 9.3; P = 0.001 and 5.8 vs 11.9; P = 0.035, respectively). In all, 70% of participants found that HoST was similar to the real surgical procedure, and 75% believed that HoST could improve confidence for carrying out the real intervention. CONCLUSION: Training in UVA in an AR environment improves technical skill acquisition with minimal cognitive demand.

Cognitive skills assessment during robot-assisted surgery: separating the wheat from the chaff.

OBJECTIVE: To investigate the utility of cognitive assessment during robot-assisted surgery (RAS) to define skills in terms of cognitive engagement, mental workload, and mental state; while objectively differentiating between novice and expert surgeons. SUBJECTS AND METHODS: In all, 10 surgeons with varying operative experience were assigned to beginner (BG), combined competent and proficient (CPG), and expert (EG) groups based on the Dreyfus model. The participants performed tasks for basic, intermediate and advanced skills on the da Vinci Surgical System. Participant performance was assessed using both tool-based and cognitive metrics. RESULTS: Tool-based metrics showed significant differences between the BG vs CPG and the BG vs EG, in basic skills. While performing intermediate skills, there were significant differences only on the instrument-to-instrument collisions between the BG vs CPG (2.0 vs 0.2, P = 0.028), and the BG vs EG (2.0 vs 0.1, P = 0.018). There were no significant differences between the CPG and EG for both basic and intermediate skills. However, using cognitive metrics, there were significant differences between all groups for the basic and intermediate skills. In advanced skills, there were no significant differences between the CPG and the EG except time (1116 vs 599.6 s), using tool-based metrics. However, cognitive metrics revealed significant differences between both groups. CONCLUSION: Cognitive assessment of surgeons may aid in defining levels of expertise performing complex surgical tasks once competence is achieved. Cognitive assessment may be used as an adjunct to the traditional methods for skill assessment during RAS.

Construct validation of the key components of Fundamental Skills of Robotic Surgery (FSRS) curriculum--a multi-institution prospective study.

BACKGROUND: Recent incorporation of simulation in surgical training necessitates developing validated platforms for training and assessment. A tool should fulfill the fundamental criteria of validation. OBJECTIVE: To report the ability of a simulation-based robotic training curriculum-Fundamental Skills of Robotic Surgery (FSRS)-to assess and distinguish between different performance levels of operator experience (construct validity). MATERIALS AND METHODS: This is a prospective multicenter observational study. Participants were classified as novice (0 robotic cases performed) and experts (>150 robotic cases performed). All participants were required to complete 4 key tasks in a previously validated FSRS curriculum: ball placement, coordinated tool control, fourth arm control, and needle handling and exchange. Using the metrics available in the simulator software, the performances of each group were compared to evaluate construct validation. RESULTS: A convenience cohort of 61 surgeons participated. Novice group (n = 49) consisted of 41 fellows/residents/medical students and 8 trained open/laparoscopic surgeons, whereas expert group consisted of 12 surgeons. The novice group had no previous robotic console experience, whereas the expert group had >150 prior robotic cases experience. An overall significant difference was observed in favor of the expert group in 4 skill sets (p < 0.05). Time to complete all 4 tasks was significantly shorter in the expert group (p < 0.001). The expert group displayed significantly lesser tool collision (p = 0.002) and reduced tissue damage (p < 0.001). In performing most tasks, the expert group's camera (p < 0.001) and clutch usage (p < 0.001) was significantly greater when compared with the novice group. CONCLUSION: The components of the FSRS curriculum showed construct validity. This validation would help in effectively implementing this curriculum for robot-assisted surgical training.

3-D vascular skeleton extraction and decomposition.

We introduce a novel vascular skeleton extraction and decomposition technique for computer-assisted diagnosis and analysis. We start by addressing the problem of vascular decomposition as a cluster optimization problem and present a methodology for weighted convex approximations. Decomposed vessel structures are then grouped using the vessel skeleton, extracted using a Laplace-based operator. The method is validated using presegmented sections of vasculature archived for 98 aneurysms in 112 patients. We test first for vascular decomposition and next for vessel skeleton extraction. The proposed method produced promising results with an estimated 80.5% of the vessel sections correctly decomposed and 92.9% of the vessel sections having the correct number of skeletal branches, identified by a clinical radiological expert. Next, the method was validated on longitudinal study data from n = 4 subjects, where vascular skeleton extraction and decomposition was performed. Volumetric and surface area comparisons were made between expert segmented sections and the proposed approach on sections containing aneurysms. Results suggest that the method is able to detect changes in aneurysm volumes and surface areas close to that segmented by an expert.

Simulation-based robot-assisted surgical training: a health economic evaluation.

OBJECTIVE: To determine the overall cost effectiveness of surgical skills training on Robotic Surgical Simulator (RoSS). METHODS: This study evaluates the cost analysis of utilizing RoSS for robot-assisted surgical training, at Roswell Park Center for Robotic Surgery. Trainees were queried for time spent on the RoSS console over a period of 1 year, starting from June 2010 to June 2011. Time spent was converted to training time consumed on robotic console, resulting in loss of OR time and revenue. The mechanical durability of the RoSS was also determined. RESULTS: 105 trainees spent 361 h on the RoSS. This duration converted to 73 robot-assisted radical prostatectomy cases, and 72 animal lab sessions. RoSS prevented a potential loss of $600,000, while 72 animal labs would have cost more than $72,000 without including initial robot installation, annual maintenance and personnel expenses. The mechanical durability testing determined breakdown at 180 and 360 h for master control and pinch device, which were repaired under warranty. CONCLUSION: RoSS is a cost effective surgical simulator for implementation of a simulation-based robot-assisted surgical training program.

Development and validation of a composite scoring system for robot-assisted surgical training--the Robotic Skills Assessment Score.

BACKGROUND: A standardized scoring system does not exist in virtual reality-based assessment metrics to describe safe and crucial surgical skills in robot-assisted surgery. This study aims to develop an assessment score along with its construct validation. MATERIALS AND METHODS: All subjects performed key tasks on previously validated Fundamental Skills of Robotic Surgery curriculum, which were recorded, and metrics were stored. After an expert consensus for the purpose of content validation (Delphi), critical safety determining procedural steps were identified from the Fundamental Skills of Robotic Surgery curriculum and a hierarchical task decomposition of multiple parameters using a variety of metrics was used to develop Robotic Skills Assessment Score (RSA-Score). Robotic Skills Assessment mainly focuses on safety in operative field, critical error, economy, bimanual dexterity, and time. Following, the RSA-Score was further evaluated for construct validation and feasibility. Spearman correlation tests performed between tasks using the RSA-Scores indicate no cross correlation. Wilcoxon rank sum tests were performed between the two groups. RESULTS: The proposed RSA-Score was evaluated on non-robotic surgeons (n = 15) and on expert-robotic surgeons (n = 12). The expert group demonstrated significantly better performance on all four tasks in comparison to the novice group. Validation of the RSA-Score in this study was carried out on the Robotic Surgical Simulator. CONCLUSION: The RSA-Score is a valid scoring system that could be incorporated in any virtual reality-based surgical simulator to achieve standardized assessment of fundamental surgical tents during robot-assisted surgery.

3D Vascular Decomposition and Classification for Computer-Aided Detection.

In this study, we propose a weighted approximate convex decomposition (WACD) and classification methodology for computer-aided detection (CADe) and analysis. We start by addressing the problem of vascular decomposition as a cluster optimization problem and introduce a methodology for compact geometric decomposition. The classification of decomposed vessel sections is performed using the most relevant eigenvalues obtained through feature selection. The method was validated using presegmented sections of vasculature archived for 98 aneurysms in 112 patients. We test first for vascular decomposition and next for classification. The proposed method produced promising results with an estimated 81.5% of the vessel sections correctly decomposed. Recursive feature elimination was performed to find the most compact and informative set of features. We showed that the selected subset of eigenvalues produces minimum error and improved classifier precision. The method was also validated on a longitudinal study of four cases having internal cerebral aneurysms. Volumetric and surface area comparisons were made between expert-segmented sections and WACD classified sections containing aneurysms. Results suggest that the approach is able to classify and detect changes in aneurysm volumes and surface areas close to that segmented by an expert.

Fundamental skills of robotic surgery: a multi-institutional randomized controlled trial for validation of a simulation-based curriculum.

OBJECTIVE: To develop and establish effectiveness of simulation-based robotic curriculum--fundamental skills of robotic surgery (FSRS). METHODS: FSRS curriculum was developed and incorporated into a virtual reality simulator, Robotic Surgical Simulator (RoSS). Fifty-three participants were randomized into an experimental group (EG) or control group (CG). The EG was asked to complete the FSRS and 1 final test on the da Vinci Surgical System (dVSS). The dVSS test consisted of 3 tasks: ball placement, suture pass, and fourth arm manipulation. The CG was directly tested on the dVSS then offered the chance to complete the FSRS and re-tested on the dVSS as a crossover (CO) group. RESULTS: Sixty-five percent of participants had never formally trained using laparoscopic surgery. Ball placement: the EG demonstrated shorter time (142 vs 164 seconds, P = .134) and more precise (1.5 vs 2.5 drops, P = .014). The CO took less time (P <.001) with greater precision (P <.001). Instruments were rarely lost from the field. Suture pass: the EG demonstrated better camera utilization (4.3 vs 3.0, P = .078). Less instrument loss occurred (0.5 vs 1.1, P = .026). Proper camera usage significantly improved (P = .009). Fourth arm manipulation: the EG took less time (132 vs 157 seconds, P = .302). Meanwhile, loss of instruments was less frequent (0.2 vs 0.8, P = .076). Precision in the CO improved significantly (P = .042) and camera control and safe instrument manipulation showed improvement (1.5 vs 3.5, 0.2 vs 0.9, respectively). CONCLUSION: FSRS curriculum is a valid, feasible, and structured curriculum that demonstrates its effectiveness by significant improvements in basic robotic surgery skills.

Vascular decomposition using weighted approximate convex decomposition.

OBJECTIVE: Stroke treatment often requires analysis of vascular pathology evaluated using computed tomography (CT) angiography. Due to vascular variability and complexity, finding precise relationships between vessel geometries and arterial pathology is difficult. A new convex shape decomposition strategy was developed to understand complex vascular structures and synthesize a weighted approximate convex decomposition (WACD) method for vascular decomposition in computer-aided diagnosis. MATERIALS AND METHODS: The vascular tree is decomposed into optimal number of components (determined by an expert). The decomposition is based on two primary features of vascular structures: (i) the branching factor that allows structural decomposition and (ii) the concavity over the vessel surface seen primarily at the site of an aneurysm. Such surfaces are decomposed into subcomponents. Vascular sections are reconstructed using CT angiograms. Next the dual graph is constructed, and edge weights for the graph are computed from shape indices. Graph vertices are iteratively clustered by a mesh decimation operator, while minimizing a cost function related to concavity. RESULTS: The method was validated by first comparing results with an approximate convex decomposition (ACD) method and next on vessel sections (n = 177) whose number of clusters (ground truth) was predetermined by an expert. In both cases, WACD produced promising results with 84.7 % of the vessel sections correctly clustered and when compared with ACD produced a more effective decomposition. Next, the algorithm was validated in a longitudinal study data of 4 subjects where volumetric and surface area comparisons were made between expert segmented sections and WACD decomposed sections that contained aneurysms. The results showed a mean error rate of 7.8 % for volumetric comparisons and 10.4 % for surface area comparisons. CONCLUSION: Decomposition of the cerebral vasculature from CT angiograms into a geometrically optimal set of convex regions may be useful for computer-assisted diagnosis. A new WACD method capable of decomposing complex vessel structures, including bifurcations and aneurysms, was developed and tested with promising results.

Validation of Robotic Surgery Simulator (RoSS).

Recent growth of daVinci Robotic Surgical System as a minimally invasive surgery tool has led to a call for better training of future surgeons. In this paper, a new virtual reality simulator, called RoSS is presented. Initial results from two studies - face and content validity, are very encouraging. 90% of the cohort of expert robotic surgeons felt that the simulator was excellent or somewhat close to the touch and feel of the daVinci console. Content validity of the simulator received 90% approval in some cases. These studies demonstrate that RoSS has the potential of becoming an important training tool for the daVinci surgical robot.