125 research outputs found

    Validation of a Sensorized Instrument-Based Training System for Minimally Invasive Surgery

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    Minimally invasive surgery training is complicated by the restraints imposed by the surgical environment. A sensorized laparoscopic instrument capable of sensing force in 5 degrees of freedom and position in 6 degrees of freedom was evaluated. Novice and Expert laparoscopists performed a complex minimally invasive surgical task - suturing - using the novel instruments. Their force and position profiles were compared. The novel minimally invasive surgical instrument is construct-valid and capable of detecting differences between novices and experts in a laparoscopic suturing task with respect to force and position. It is also concurrently valid with an existing standard: the Fundamentals of Laparoscopic Skills. Further evaluation is mandated to better understand the ability to predict performance based on force and position as well as the potential for new metrics in minimally invasive surgical education

    Robot-Assisted Minimally Invasive Surgery-Surgical Robotics in the Data Age

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    Telesurgical robotics, as a technical solution for robot-assisted minimally invasive surgery (RAMIS), has become the first domain within medicosurgical robotics that achieved a true global clinical adoption. Its relative success (still at a low single-digit percentile total market penetration) roots in the particular human-in-the-loop control, in which the trained surgeon is always kept responsible for the clinical outcome achieved by the robot-actuated invasive tools. Nowadays, this paradigm is challenged by the need for improved surgical performance, traceability, and safety reaching beyond the human capabilities. Partially due to the technical complexity and the financial burden, the adoption of telesurgical robotics has not reached its full potential, by far. Apart from the absolutely market-dominating da Vinci surgical system, there are already 60+ emerging RAMIS robot types, out of which 15 have already achieved some form of regulatory clearance. This article aims to connect the technological advancement with the principles of commercialization, particularly looking at engineering components that are under development and have the potential to bring significant advantages to the clinical practice. Current RAMIS robots often do not exceed the functionalities deriving from their mechatronics, due to the lack of data-driven assistance and smart human–machine collaboration. Computer assistance is gradually gaining more significance within emerging RAMIS systems. Enhanced manipulation capabilities, refined sensors, advanced vision, task-level automation, smart safety features, and data integration mark together the inception of a new era in telesurgical robotics, infiltrated by machine learning (ML) and artificial intelligence (AI) solutions. Observing other domains, it is definite that a key requirement of a robust AI is the good quality data, derived from proper data acquisition and sharing to allow building solutions in real time based on ML. Emerging RAMIS technologies are reviewed both in a historical and a future perspective

    Task Analysis, Modeling, And Automatic Identification Of Elemental Tasks In Robot-Assisted Laparoscopic Surgery

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    Robotic microsurgery provides many advantages for surgical operations, including tremor filtration, an increase in dexterity, and smaller incisions. There is a growing need for a task analyses on robotic laparoscopic operations to understand better the tasks involved in robotic microsurgery cases. A few research groups have conducted task observations to help systems automatically identify surgeon skill based on task execution. Their gesture analyses, however, lacked depth and their class libraries were composed of ambiguous groupings of gestures that did not share contextual similarities. A Hierarchical Task Analysis was performed on a four-throw suturing task using a robotic microsurgical platform. Three skill levels were studied: attending surgeons, residents, and naïve participants. From this task analysis, a subtask library was created. The Hierarchical Task Analysis subtask library, a computer system was created that accurately identified surgeon subtasks based on surgeon hand gestures. An automatic classifier was trained on the subtasks identified during the Hierarchical Task Analysis of a four-throw suturing task and the motion signature recorded during task performance. Using principal component analysis and a J48 decision tree classifier, an average individual classification accuracy of 94.56% was achieved. This research lays the foundation for accurate and meaningful autonomous computer assistance in a surgical arena by creating a gesture library from a detailed Hierarchical Task Analysis. The results of this research will improve the surgeon-robot interface and enhance surgery performance. The classes used will eliminate human machine miscommunication by using an understandable and structured class library based on a Hierarchical Task Analysis. By enabling a robot to understand surgeon actions, intelligent contextual-based assistance could be provide to the surgeon by the robot. Limitations of this research included the small participant sample size used for this research which resulted in high subtask execution variability. Future work will include a larger participant population to address this limitation. Additionally, a Hidden Markov Model will be incorporated into the classification process to help increase the classification accuracy. Finally, a closer investigation of vestigial techniques will be conducted to study the effect of past learned laparoscopic techniques, which are no longer necessary in the robotic-assisted laparoscopic surgery arena

    Methods and Tools for Objective Assessment of Psychomotor Skills in Laparoscopic Surgery

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    Training and assessment paradigms for laparoscopic surgical skills are evolving from traditional mentor–trainee tutorship towards structured, more objective and safer programs. Accreditation of surgeons requires reaching a consensus on metrics and tasks used to assess surgeons’ psychomotor skills. Ongoing development of tracking systems and software solutions has allowed for the expansion of novel training and assessment means in laparoscopy. The current challenge is to adapt and include these systems within training programs, and to exploit their possibilities for evaluation purposes. This paper describes the state of the art in research on measuring and assessing psychomotor laparoscopic skills. It gives an overview on tracking systems as well as on metrics and advanced statistical and machine learning techniques employed for evaluation purposes. The later ones have a potential to be used as an aid in deciding on the surgical competence level, which is an important aspect when accreditation of the surgeons in particular, and patient safety in general, are considered. The prospective of these methods and tools make them complementary means for surgical assessment of motor skills, especially in the early stages of training. Successful examples such as the Fundamentals of Laparoscopic Surgery should help drive a paradigm change to structured curricula based on objective parameters. These may improve the accreditation of new surgeons, as well as optimize their already overloaded training schedules

    Robot-Assisted Minimally Invasive Surgical Skill Assessment—Manual and Automated Platforms

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    The practice of Robot-Assisted Minimally Invasive Surgery (RAMIS) requires extensive skills from the human surgeons due to the special input device control, such as moving the surgical instruments, use of buttons, knobs, foot pedals and so. The global popularity of RAMIS created the need to objectively assess surgical skills, not just for quality assurance reasons, but for training feedback as well. Nowadays, there is still no routine surgical skill assessment happening during RAMIS training and education in the clinical practice. In this paper, a review of the manual and automated RAMIS skill assessment techniques is provided, focusing on their general applicability, robustness and clinical relevance

    HUMAN-ROBOT COLLABORATION IN ROBOTIC-ASSISTED SURGICAL TRAINING

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    Ph.DDOCTOR OF PHILOSOPH
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