Evolving robotic surgery training and improving patient safety, with the integration of novel technologies.

Funder: University College London (UCL)INTRODUCTION: Robot-assisted surgery is becoming increasingly adopted by multiple surgical specialties. There is evidence of inherent risks of utilising new technologies that are unfamiliar early in the learning curve. The development of standardised and validated training programmes is crucial to deliver safe introduction. In this review, we aim to evaluate the current evidence and opportunities to integrate novel technologies into modern digitalised robotic training curricula. METHODS: A systematic literature review of the current evidence for novel technologies in surgical training was conducted online and relevant publications and information were identified. Evaluation was made on how these technologies could further enable digitalisation of training. RESULTS: Overall, the quality of available studies was found to be low with current available evidence consisting largely of expert opinion, consensus statements and small qualitative studies. The review identified that there are several novel technologies already being utilised in robotic surgery training. There is also a trend towards standardised validated robotic training curricula. Currently, the majority of the validated curricula do not incorporate novel technologies and training is delivered with more traditional methods that includes centralisation of training services with wet laboratories that have access to cadavers and dedicated training robots. CONCLUSIONS: Improvements to training standards and understanding performance data have good potential to significantly lower complications in patients. Digitalisation automates data collection and brings data together for analysis. Machine learning has potential to develop automated performance feedback for trainees. Digitalised training aims to build on the current gold standards and to further improve the 'continuum of training' by integrating PBP training, 3D-printed models, telementoring, telemetry and machine learning

Catheter manipulation analysis for objective performance and technical skills assessment in transcatheter aortic valve implantation

Purpose Transcatheter aortic valve implantation (TAVI) demands precise and efficient handling of surgical instruments within the confines of the aortic anatomy. Operational performance and dexterous skills are critical for patient safety, and objective methods are assessed with a number of manipulation features, derived from the kinematic analysis of the catheter/guidewire in fluoroscopy video sequences. Methods A silicon phantom model of a type I aortic arch was used for this study. Twelve endovascular surgeons, divided into two experience groups, experts (n=6) and novices (n=6), performed cannulation of the aorta, representative of valve placement in TAVI. Each participant completed two TAVI experiments, one with conventional catheters and one with the Magellan robotic platform. Video sequences of the fluoroscopic monitor were recorded for procedural processing. A semi-automated tracking software provided the 2D coordinates of the catheter/guidewire tip. In addition, the aorta phantom was segmented in the videos and the shape of the entire catheter was manually annotated in a subset of the available video frames using crowdsourcing. The TAVI procedure was divided into two stages, and various metrics, representative of the catheter’s overall navigation as well as its relative movement to the vessel wall, were developed. Results Experts consistently exhibited lower values of procedure time and dimensionless jerk, and higher average speed and acceleration than novices. Robotic navigation resulted in increased average distance to the vessel wall in both groups, a surrogate measure of safety and reduced risk of embolisation. Discrimination of experience level and types of equipment was achieved with the generated motion features and established clustering algorithms. Conclusions Evaluation of surgical skills is possible through the analysis of the catheter/guidewire motion pattern. The use of robotic endovascular platforms seems to enable more precise and controlled catheter navigation

Why rankings of biomedical image analysis competitions should be interpreted with care

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future

Combined 2D and 3D tracking of surgical instruments for minimally invasive and robotic-assisted surgery

PURPOSE: Computer-assisted interventions for enhanced minimally invasive surgery (MIS) require tracking of the surgical instruments. Instrument tracking is a challenging problem in both conventional and robotic-assisted MIS, but vision-based approaches are a promising solution with minimal hardware integration requirements. However, vision-based methods suffer from drift, and in the case of occlusions, shadows and fast motion, they can be subject to complete tracking failure. METHODS: In this paper, we develop a 2D tracker based on a Generalized Hough Transform using SIFT features which can both handle complex environmental changes and recover from tracking failure. We use this to initialize a 3D tracker at each frame which enables us to recover 3D instrument pose over long sequences and even during occlusions. RESULTS: We quantitatively validate our method in 2D and 3D with ex vivo data collected from a DVRK controller as well as providing qualitative validation on robotic-assisted in vivo data. CONCLUSIONS: We demonstrate from our extended sequences that our method provides drift-free robust and accurate tracking. Our occlusion-based sequences additionally demonstrate that our method can recover from occlusion-based failure. In both cases, we show an improvement over using 3D tracking alone suggesting that combining 2D and 3D tracking is a promising solution to challenges in surgical instrument tracking. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11548-016-1393-4) contains supplementary material, which is available to authorized users

Robust Catheter and Guidewire Tracking Using B-spline Tube Model and Pixel-Wise Posteriors

In endovascular surgery and cardiology, robotic catheters are emerging as a promising technology for enhanced catheter manipulation and navigation while reducing radiation exposure. For robotic catheter systems especially with tendon actuation, a key challenge is the localisation of the catheter shape and position within the anatomy. An effective approach is through image-based catheter/guidewire detection and tracking. However, these are difficult problems due to the thin appearance of the instruments in the image and the low signal-to-noise ratio of fluoroscopy. In this paper, we propose a deformable B-spline tube model which can effectively represent the shape of a catheter and guidewire. The model allows fitting using a region-based probabilistic algorithm which does not rely on intensity gradients but exploits a signed distance function and the non-parametric distributions of measurements. Unlike previous B-spline fitting approaches which optimise the spline with respect to control points, we propose a knot-driven scheme with an equidistance prior in order to better fit complex curves. Our probabilistic framework shows promising results for catheter and guidewire tracking in different procedures even with handling overlapping instrument segments. We present empirical studies using phantom model data and in vivo fluoroscopic sequences with annotated ground truth. Our results indicate that the proposed approach can precisely model the catheter and guidewire contours in near real time, and this information can be embedded in a robotic catheter control loop or utilised for image-guidance.status: publishe