3,649 research outputs found
Robot Autonomy for Surgery
Autonomous surgery involves having surgical tasks performed by a robot
operating under its own will, with partial or no human involvement. There are
several important advantages of automation in surgery, which include increasing
precision of care due to sub-millimeter robot control, real-time utilization of
biosignals for interventional care, improvements to surgical efficiency and
execution, and computer-aided guidance under various medical imaging and
sensing modalities. While these methods may displace some tasks of surgical
teams and individual surgeons, they also present new capabilities in
interventions that are too difficult or go beyond the skills of a human. In
this chapter, we provide an overview of robot autonomy in commercial use and in
research, and present some of the challenges faced in developing autonomous
surgical robots
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
Computer- and robot-assisted Medical Intervention
Medical robotics includes assistive devices used by the physician in order to
make his/her diagnostic or therapeutic practices easier and more efficient.
This chapter focuses on such systems. It introduces the general field of
Computer-Assisted Medical Interventions, its aims, its different components and
describes the place of robots in that context. The evolutions in terms of
general design and control paradigms in the development of medical robots are
presented and issues specific to that application domain are discussed. A view
of existing systems, on-going developments and future trends is given. A
case-study is detailed. Other types of robotic help in the medical environment
(such as for assisting a handicapped person, for rehabilitation of a patient or
for replacement of some damaged/suppressed limbs or organs) are out of the
scope of this chapter.Comment: Handbook of Automation, Shimon Nof (Ed.) (2009) 000-00
Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning
Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence
imaging technology that has the potential to increase intraoperative precision,
extend resection, and tailor surgery for malignant invasive brain tumors
because of its subcellular dimension resolution. Despite its promising
diagnostic potential, interpreting the gray tone fluorescence images can be
difficult for untrained users. In this review, we provide a detailed
description of bioinformatical analysis methodology of CLE images that begins
to assist the neurosurgeon and pathologist to rapidly connect on-the-fly
intraoperative imaging, pathology, and surgical observation into a
conclusionary system within the concept of theranostics. We present an overview
and discuss deep learning models for automatic detection of the diagnostic CLE
images and discuss various training regimes and ensemble modeling effect on the
power of deep learning predictive models. Two major approaches reviewed in this
paper include the models that can automatically classify CLE images into
diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and
models that can localize histological features on the CLE images using weakly
supervised methods. We also briefly review advances in the deep learning
approaches used for CLE image analysis in other organs. Significant advances in
speed and precision of automated diagnostic frame selection would augment the
diagnostic potential of CLE, improve operative workflow and integration into
brain tumor surgery. Such technology and bioinformatics analytics lend
themselves to improved precision, personalization, and theranostics in brain
tumor treatment.Comment: See the final version published in Frontiers in Oncology here:
https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful
From Concept to Market: Surgical Robot Development
Surgical robotics and supporting technologies have really become a prime example of modern applied
information technology infiltrating our everyday lives. The development of these systems spans across
four decades, and only the last few years brought the market value and saw the rising customer base
imagined already by the early developers. This chapter guides through the historical development of the
most important systems, and provide references and lessons learnt for current engineers facing similar
challenges. A special emphasis is put on system validation, assessment and clearance, as the most
commonly cited barrier hindering the wider deployment of a system
Advancing Intra-operative Precision: Dynamic Data-Driven Non-Rigid Registration for Enhanced Brain Tumor Resection in Image-Guided Neurosurgery
During neurosurgery, medical images of the brain are used to locate tumors
and critical structures, but brain tissue shifts make pre-operative images
unreliable for accurate removal of tumors. Intra-operative imaging can track
these deformations but is not a substitute for pre-operative data. To address
this, we use Dynamic Data-Driven Non-Rigid Registration (NRR), a complex and
time-consuming image processing operation that adjusts the pre-operative image
data to account for intra-operative brain shift. Our review explores a specific
NRR method for registering brain MRI during image-guided neurosurgery and
examines various strategies for improving the accuracy and speed of the NRR
method. We demonstrate that our implementation enables NRR results to be
delivered within clinical time constraints while leveraging Distributed
Computing and Machine Learning to enhance registration accuracy by identifying
optimal parameters for the NRR method. Additionally, we highlight challenges
associated with its use in the operating room
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
Robot ontologies for sensor- and Image-guided surgery
Robots and robotics are becoming more com-
plex and flexible, due to technological advancement, improved
sensing capabilities and machine intelligence. Service robots
target a wide range of applications, relying on advanced
Human–Robot Interaction. Medical robotics is becoming a
leading application area within, and the number of surgical,
rehabilitation and hospital assistance robots is rising rapidly.
However, the complexity of the medical environment has been
a major barrier, preventing a wider use of robotic technology,
thus mostly teleoperated, human-in-the-loop control solutions
emerged so far. Providing smarter and better medical robots
requires a systematic approach in describing and translating
human processes for the robots. It is believed that ontologies can
bridge human cognitive understanding and robotic reasoning
(machine intelligence). Besides, ontologies serve as a tool and
method to assess the added value robotic technology brings
into the medical environment. The purpose of this paper is to
identify relevant ontology research in medical robotics, and to
review the state-of-the art. It focuses on the surgical domain,
fundamental terminology and interactions are described for two
example applications in neurosurgery and orthopaedics
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