Improving Surgical Training Phantoms by Hyperrealism: Deep Unpaired Image-to-Image Translation from Real Surgeries

Current `dry lab' surgical phantom simulators are a valuable tool for surgeons which allows them to improve their dexterity and skill with surgical instruments. These phantoms mimic the haptic and shape of organs of interest, but lack a realistic visual appearance. In this work, we present an innovative application in which representations learned from real intraoperative endoscopic sequences are transferred to a surgical phantom scenario. The term hyperrealism is introduced in this field, which we regard as a novel subform of surgical augmented reality for approaches that involve real-time object transfigurations. For related tasks in the computer vision community, unpaired cycle-consistent Generative Adversarial Networks (GANs) have shown excellent results on still RGB images. Though, application of this approach to continuous video frames can result in flickering, which turned out to be especially prominent for this application. Therefore, we propose an extension of cycle-consistent GANs, named tempCycleGAN, to improve temporal consistency.The novel method is evaluated on captures of a silicone phantom for training endoscopic reconstructive mitral valve procedures. Synthesized videos show highly realistic results with regard to 1) replacement of the silicone appearance of the phantom valve by intraoperative tissue texture, while 2) explicitly keeping crucial features in the scene, such as instruments, sutures and prostheses. Compared to the original CycleGAN approach, tempCycleGAN efficiently removes flickering between frames. The overall approach is expected to change the future design of surgical training simulators since the generated sequences clearly demonstrate the feasibility to enable a considerably more realistic training experience for minimally-invasive procedures.Comment: 8 pages, accepted at MICCAI 2018, supplemental material at https://youtu.be/qugAYpK-Z4

Evaluation of a new virtual-reality training simulator for hysteroscopy

Background: To determine realism and training capacity of HystSim, a new virtual-reality simulator for the training of hysteroscopic interventions. Methods: Sixty-two gynaecological surgeons with various levels of expertise were interviewed at the 13th Practical Course in Gynaecologic Endoscopy in Davos, Switzerland. All participants received a 20-min hands-on training on the simulator and filled out a four-page questionnaire. Twenty-three questions with respect to the realism of the simulation and the training capacity were answered on a seven-point Likert scale along with 11 agree-disagree statements concerning the HystSim training in general. Results: Twenty-six participants had performed more than 50 hysteroscopies ("experts”) and 36 equal to or fewer than 50 ("novices”). Four of 60 (6.6%) responding participants judged the overall impression as "7 - absolutely realistic”, 40 (66.6%) as "6 - realistic”, and 16 (26.6%) as "5 - somewhat realistic”. Novices (6.48; 95% confidence interval [CI] 6.28-6.7) rated the overall training capacity significantly higher than experts (6.08; 95% CI 5.85-6.3), however, high-grade acceptance was found in both groups. In response to the statements, 95.2% believe that HystSim allows procedural training of diagnostic and therapeutic hysteroscopy, and 85.5% suggest that HystSim training should be offered to all novices before performing surgery on real patients. Conclusion: Face validity has been established for a new hysteroscopic surgery simulator. Potential trainees and trainers assess it to be a realistic and useful tool for the training of hysteroscopy. Further systematic validation studies are needed to clarify how this system can be optimally integrated into the gynaecological curriculu

Algoritmos generales para simuladores de cirugía laparoscópica

Recent advances in fields such as modeling of deformable objects, haptic technologies, immersive technologies, computation capacity and virtual environments have created the conditions to offer novel and suitable training tools and learning methods in the medical area. One of these training tools is the virtual surgical simulator, which has no limitations of time or risk, unlike conventional methods of training. Moreover, these simulators allow for the quantitative evaluation of the surgeon performance, giving the possibility to create performance standards in order to define if the surgeon is well prepared to execute a determined surgical procedure on a real patient. This paper describes the development of a virtual simulator for laparoscopic surgery. The simulator allows the multimodal interaction between the surgeon and the surgical virtual environment using visual and haptic feedback devices. To make the experience of the surgeon closer to the real surgical environment a specific user interface was developed. Additionally in this paper we describe some implementations carried out to face typical challenges presented in surgical simulators related to the tradeoff between real-time performance and high realism; for instance, the deformation of soft tissues are simulated using a GPU (Graphics Processor Unit) -based implementation of the mass-spring model. In this case, we explain the algorithms developed taking into account the particular case of a cholecystectomy procedure in laparoscopic surgery.Recientes avances en áreas tales como modelación computacional de objetos deformables, tecnologías hápticas, tecnologías inmersivas, capacidad de procesamiento y ambiente virtuales han proporcionado las bases para el desarrollo de herramientas y métodos de aprendizaje confiables en el entrenamiento médico. Una de estas herramientas de entrenamiento son los simuladores quirúrgicos virtuales, los cuales no tienen limitaciones de tiempo o riesgos a diferencia de los métodos convencionales de entrenamiento. Además, dichos simuladores permiten una evaluación cuantitativa del desempeño del cirujano, dando la posibilidad de crear estándares de desempeño con el fin de definir en qué momento un cirujano está preparado para realizar un determinado procedimiento quirúrgico sobre un paciente. Este artículo describe el desarrollo de un simulador virtual para cirugía laparoscópica. Este simulador permite la interacción multimodal entre el cirujano y el ambiente virtual quirúrgico usando dispositivos de retroalimentación visual y háptica. Para hacer la experiencia del cirujano más cercana a la de una ambiente quirúrgico real se desarrolló una interfaz cirujano-simulador especial. Adicionalmente en este artículo se describen algunas implementaciones que solucionan los problemas típicos cuando se desarrolla un simulador quirúrgico, principalmente relacionados con lograr un desempeño en tiempo real mientras se sacrifica el nivel de realismo de la simulación: por ejemplo, la deformación de los tejidos blandos simulados usando una implementación del modelo masa-resorte en la unidad de procesamiento gráfico. En este caso se describen los algoritmos desarrollados tomando en cuenta la simulación de un procedimiento laparoscópico llamado colecistectomía

Educação e Treino em Cirurgia - o Desafio da Qualidade e o Evitar do Erro - o Papel dos Simuladores

info:eu-repo/semantics/publishedVersio

Healthcare Robotics

Robots have the potential to be a game changer in healthcare: improving health and well-being, filling care gaps, supporting care givers, and aiding health care workers. However, before robots are able to be widely deployed, it is crucial that both the research and industrial communities work together to establish a strong evidence-base for healthcare robotics, and surmount likely adoption barriers. This article presents a broad contextualization of robots in healthcare by identifying key stakeholders, care settings, and tasks; reviewing recent advances in healthcare robotics; and outlining major challenges and opportunities to their adoption.Comment: 8 pages, Communications of the ACM, 201

A. Training Simulators for Gastrointestinal Endoscopy: Current and Future Perspectives

Over the last decades, visual endoscopy has become a gold standard for the detection and treatment of gastrointestinal cancers. However, mastering endoscopic procedures is complex and requires long hours of practice. In this context, simulation-based training represents a valuable opportunity for acquiring technical and cognitive skills, suiting the different trainees’ learning pace and limiting the risks for the patients. In this regard, the present contribution aims to present a critical and comprehensive review of the current technology for gastrointestinal (GI) endoscopy training, including both commercial products and platforms at a research stage. Not limited to it, the recent revolution played by the technological advancements in the fields of robotics, artificial intelligence, virtual/augmented reality, and computational tools on simulation-based learning is documented and discussed. Finally, considerations on the future trend of this application field are drawn, highlighting the impact of the most recent pandemic and the current demographic trends