Understanding expertise in surgical gesture by means of Hidden Markov Models

Minimally invasive surgery (MIS) has became very widespread in the last ten years. Due to the difficulties encountered by the surgeons to learn and manage this technique, a huge importance has the improvement of training procedures, the improvement of surgical instrumentation and the robotic automation of surgical gesture. All these purposes require the analysis of surgical performance with the aim to understand it and to define what is expertise in surgical gesture. In this paper for the first time the Hidden Markov Models (HMMs) are used as a tool for the understanding of surgical performance and of the human factors that characterize it. In our experiments we used position data concerning the tools movements during exercises performed on a surgical simulator. Using Hidden Markov theory, we create a model of the expert surgeon performance able to evaluate surgical capability and to distinguish between expert and non-expert surgeons. By analyzing the trained model of the expert surgeon performance we show that it is possible to deduce information about features characterizing the surgical expertise

Comparison of Control Modes of a Hand-Held Robot for Laparoscopic Surgery

Teleoperated robots for minimally invasive surgery make surgeons loose direct contact with the patient. We are developing a handheld, dexterous surgical robot that can be controlled with one hand only, while standing at the operating table. The instrument is composed of a master part (the handle) and a slave part (the tip). This work compares the performance of different control modes, i.e. different ways to map the degrees of freedom of the handle to those of the tip. We ask users to drive the tip along complex trajectories in a virtual environment, using the real master to drive a simulated slave, and assess their performance. Results show that, concerning time, users with no training in laparoscopy prefer a direct mapping of position and orientation, like in free hand motion. However, users trained in laparoscopy perform equally fast with our hand-held robot and, concerning precision, make a smaller number of errors

Manejo integrado de pragas de grãos armazenados.

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Laparoscopic Video Analysis for Training and Image Guided Surgery

Automatic analysis of Minimally Invasive Surgical video has the potential to drive new solutions for alleviating needs of safe and reproducible training programs, objective and transparent evaluation systems and navigation tools to assist surgeons and improve patient safety. Surgical video is an always available source of information, which can be used without any additional intrusive hardware in the operating room. This paper is focused on surgical video analysis methods and techniques. It describes authors' contributions in two key aspects, the 3D reconstruction of the surgical field and the segmentation and tracking of tools and organs based on laparoscopic video images. Results are given to illustrate the potential of this field of research, like the calculi of the 3D position and orientation of a tool from its 2D image, or the translation of a preoperative resection plan into a hepatectomy surgical procedure using the shading information of the image. Research efforts are required to further develop these technologies in order to harness all the valuable information available in any video-based surgery

Prospects on Brain-Machine Interfaces for Space System Control

The dream of controlling and guiding computer-based systems using human brain signals has slowly but steadily become a reality. The available technology allows real-time implementation of systems that measure neuronal activity, convert their signals, and translate their output for the purpose of controlling mechanical systems. This paper describes the state of the art of non-invasive BMIs and critically investigates both the current technological limits and the future potential that BMIs have for space applications. We present an assessment of the advantages that BMIs can provide and justify the preferred candidate concepts for space applications together with a vision of future directions for their implementation

Systems and technologies for objective evaluation of technical skills in laparoscopic surgery

Minimally invasive surgery is a highly demanding surgical approach regarding technical requirements for the surgeon, who must be trained in order to perform a safe surgical intervention. Traditional surgical education in minimally invasive surgery is commonly based on subjective criteria to quantify and evaluate surgical abilities, which could be potentially unsafe for the patient. Authors, surgeons and associations are increasingly demanding the development of more objective assessment tools that can accredit surgeons as technically competent. This paper describes the state of the art in objective assessment methods of surgical skills. It gives an overview on assessment systems based on structured checklists and rating scales, surgical simulators, and instrument motion analysis. As a future work, an objective and automatic assessment method of surgical skills should be standardized as a means towards proficiency-based curricula for training in laparoscopic surgery and its certification

EVA: Laparoscopic instrument tracking based on endoscopic video analysis for psychomotor skills assessment

INTRODUCTION: The EVA (Endoscopic Video Analysis) tracking system a new tracking system for extracting motions of laparoscopic instruments based on non-obtrusive video tracking was developed. The feasibility of using EVA in laparoscopic settings has been tested in a box trainer setup. METHODS: EVA makes use of an algorithm that employs information of the laparoscopic instrument's shaft edges in the image, the instrument's insertion point, and the camera's optical centre to track the 3D position of the instrument tip. A validation study of EVA comprised a comparison of the measurements achieved with EVA and the TrEndo tracking system. To this end, 42 participants (16 novices, 22 residents, and 4 experts) were asked to perform a peg transfer task in a box trainer. Ten motion-based metrics were used to assess their performance. RESULTS: Construct validation of the EVA has been obtained for seven motion-based metrics. Concurrent validation revealed that there is a strong correlation between the results obtained by EVA and the TrEndo for metrics such as path length (p=0,97), average speed (p=0,94) or economy of volume (p=0,85), proving the viability of EVA. CONCLUSIONS: EVA has been successfully used in the training setup showing potential of endoscopic video analysis to assess laparoscopic psychomotor skills. The results encourage further implementation of video tracking in training setups and in image guided surgery