558 research outputs found
Deep Reinforcement Learning in Surgical Robotics: Enhancing the Automation Level
Surgical robotics is a rapidly evolving field that is transforming the
landscape of surgeries. Surgical robots have been shown to enhance precision,
minimize invasiveness, and alleviate surgeon fatigue. One promising area of
research in surgical robotics is the use of reinforcement learning to enhance
the automation level. Reinforcement learning is a type of machine learning that
involves training an agent to make decisions based on rewards and punishments.
This literature review aims to comprehensively analyze existing research on
reinforcement learning in surgical robotics. The review identified various
applications of reinforcement learning in surgical robotics, including
pre-operative, intra-body, and percutaneous procedures, listed the typical
studies, and compared their methodologies and results. The findings show that
reinforcement learning has great potential to improve the autonomy of surgical
robots. Reinforcement learning can teach robots to perform complex surgical
tasks, such as suturing and tissue manipulation. It can also improve the
accuracy and precision of surgical robots, making them more effective at
performing surgeries
Augmented reality (AR) for surgical robotic and autonomous systems: State of the art, challenges, and solutions
Despite the substantial progress achieved in the development and integration of augmented reality (AR) in surgical robotic and autonomous systems (RAS), the center of focus in most devices remains on improving end-effector dexterity and precision, as well as improved access to minimally invasive surgeries. This paper aims to provide a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human-robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. A number of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems (such as deep learning algorithms), and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. The accuracy of results in robot end-effector collisions and reduced occlusion remain promising within the scope of our research, validating the propositions made for the surgical clearance of ever-expanding AR technology in the future
Patient-Specific 3D Printed Models for Education, Research and Surgical Simulation
3D printing techniques are increasingly used in engineering science, allowing the use of computer aided design (CAD) to rapidly and inexpensively create prototypes and components. There is also growing interest in the application of these techniques in a clinical context for the creation of anatomically accurate 3D printed models from medical images for therapy planning, research, training and teaching applications. However, the techniques and tools available to create 3D models of anatomical structures typically require specialist knowledge in image processing and mesh manipulation to achieve. In this book chapter we describe the advantages of 3D printing for patient education, healthcare professional education, interventional planning and implant development. We also describe how to use medical image data to segment volumes of interest, refine and prepare for 3D printing. We will use a lung as an example. The information in this section will allow anyone to create own 3D printed models from medical image data. This knowledge will be of use to anyone with little or no previous experience in medical image processing who have identified a potential application for 3D printing in a medical context, or those with a more general interest in the techniques
Radiological Society of North America (RSNA) 3D printing Special Interest Group (SIG): Guidelines for medical 3D printing and appropriateness for clinical scenarios
Este número da revista Cadernos de Estudos Sociais estava em organização quando fomos colhidos pela morte do sociólogo Ernesto Laclau. Seu falecimento em 13 de abril de 2014 surpreendeu a todos, e particularmente ao editor Joanildo Burity, que foi seu orientando de doutorado na University of Essex, Inglaterra, e que recentemente o trouxe à Fundação Joaquim Nabuco para uma palestra, permitindo que muitos pudessem dialogar com um dos grandes intelectuais latinoamericanos contemporâneos. Assim, buscamos fazer uma homenagem ao sociólogo argentino publicando uma entrevista inédita concedida durante a sua passagem pelo Recife, em 2013, encerrando essa revista com uma sessão especial sobre a sua trajetória
Augmented Reality in Ventriculostomy
Freehand ventriculostomy is one of the most common neurological procedures
performed when the cerebrospinal
uid increases in the ventricular system. This
procedure is most often performed in the emergency room or intensive care unit and
thus without a navigation system to help surgeons locate the ventricles. Surgeons
instead use anatomical landmarks on the face and skull to determine the best location
of the burr hole and trajectory for moving catheter through the brain to the ventricles
to drain excess cerebrospinal
uid (CSF) and decrease intracranial pressure (ICP).
Freehand ventriculostomy has an associated catheter misplacement rate of over 30%
which can lead to a number of complications including mortality and morbidity.
In this dissertation, we propose an augmented-reality pipeline for ventriculostomy
using an optical-see-through head-mounted device, the Microsoft HoloLens. Our system,
projects a 3D constructed model of the patient's skull and ventricles directly onto
the patient's head to guide the surgeon to locate a target on the ventricle. As part
of this pipeline, we implemented an API to send real-time tracking information from
the optical tracker to the the HoloLens, provided a manual gesture-based registration
method, as well as a colored-based depth visualization to help users understand the
spatial relationship between the patient's ventricular anatomy and surgical tool.
In a study with 15 subjects, we found that the proposed gesture-based registration
has an accuracy of 10:75 millimeters and target hitting accuracy of 12:28
millimeters. In terms of usability, our developed system received a score of 74.5
on the System usability scale (SUS), indicating that the system is easily usable.
Our preliminary results suggest that augmented-reality systems can be helpful for
neuronavigation procedures that require target localization
Brain and Human Body Modelling 2021
This open access book describes modern applications of computational human modelling to advance neurology, cancer treatment, and radio-frequency studies including regulatory, safety, and wireless communication fields. Readers working on any application that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest models and techniques available to assess a given technology’s safety and efficacy in a timely and efficient manner. This is an Open Access book
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