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

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

Full Issue: Volume 13, Issue 1 - Winter 2018

Full Issue: Volume 13, Issue 1 - Winter 201

Getting the best outcomes from epilepsy surgery.

Neurosurgery is an underutilized treatment that can potentially cure drug-refractory epilepsy. Careful, multidisciplinary presurgical evaluation is vital for selecting patients and to ensure optimal outcomes. Advances in neuroimaging have improved diagnosis and guided surgical intervention. Invasive electroencephalography allows the evaluation of complex patients who would otherwise not be candidates for neurosurgery. We review the current state of the assessment and selection of patients and consider established and novel surgical procedures and associated outcome data. We aim to dispel myths that may inhibit physicians from referring and patients from considering neurosurgical intervention for drug-refractory focal epilepsies. Ann Neurol 2018;83:676-690

Intensity-modulated stereotactic radiosurgery for arteriovenous malformations: guidance for treatment planning.

BackgroundStereotactic Radiosurgery (SRS) is a common tool used to treat Arteriovenous Malformations (AVMs) in anatomical locations associated with a risk of surgical complications. Despite high rates of clinical effectiveness, SRS carries a risk of toxicity as a result of radiation injury to brain tissue. The use of intensity-modulated radiotherapy (IMRT) has increased because it may lead to improved PTV conformity and better Normal Tissue (NT) sparing compared to 3D Conformal Radiotherapy (3DCRT). The aim of this study was twofold: 1) to develop simple patient stratification rules for the recommendation of IMRT planning strategies over 3DCRT in the treatment of AVMs with SRS; and 2) to estimate the impact of IMRT in terms of toxicity reduction using retrospectively reported data for symptomatic radiation injury following SRS.MethodsThirty-one AVM patients previously treated with 3DCRT were replanned in a commercial treatment planning system using 3DCRT and static gantry IMRT with identical beam arrangements. The radiotherapy planning metrics analyzed included AVM volume, diameter, and volume to surface area ratio. The dosimetric endpoints analyzed included conformity index improvements and NT sparing measured by the maximum NT dose, and the volume of surrounding tissue that received 7Gy and 12Gy.ResultsOur analysis revealed stratified subsets of patients for IMRT that were associated with improved conformity, and those that were associated with decreased doses to normal tissue. The stratified patients experienced an improvement in conformity index by -6-68%, a reduction in the maximum NT dose by -0.5-12.3%, a reduction in the volume of NT receiving 7Gy by 1-8 cc, and a reduction in the volume of NT receiving 12Gy by 0-3.7 cc. The reduction in NT receiving 12Gy translated to a theoretical decrease in the probability of symptomatic injury by 0-9.3%.ConclusionsThis work indicates the potential for significant patient improvements when treating AVMs and provides rules to predict which patients are likely to benefit from IMRT

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

Evaluation of Thiel cadaveric model for MRI-guided stereotactic procedures in neurosurgery

BACKGROUND: Magnetic resonance imaging (MRI)-guided deep brain stimulation (DBS) and high frequency focused ultrasound (FUS) is an emerging modality to treat several neurological disorders of the brain. Developing reliable models to train and assess future neurosurgeons is paramount to ensure safety and adequate training of neurosurgeons of the future. METHODS: We evaluated the use of Thiel cadaveric model to practice MRI-guided DBS implantation and high frequency MRI-guided FUS in the human brain. We performed three training sessions for DBS and five sonications using high frequency MRI-guided FUS in five consecutive cadavers to assess the suitability of this model to use in training for stereotactic functional procedures. RESULTS: We found the brains of these cadavers preserved in an excellent anatomical condition up to 15 months after embalmment and they were excellent model to use, MRI-guided DBS implantation and FUS produced the desired lesions accurately and precisely in these cadaveric brains. CONCLUSION: Thiel cadavers provided a very good model to perform these procedures and a potential model to train and assess neurosurgeons of the future

Prevalence of haptic feedback in robot-mediated surgery : a systematic review of literature

© 2017 Springer-Verlag. This is a post-peer-review, pre-copyedit version of an article published in Journal of Robotic Surgery. The final authenticated version is available online at: https://doi.org/10.1007/s11701-017-0763-4With the successful uptake and inclusion of robotic systems in minimally invasive surgery and with the increasing application of robotic surgery (RS) in numerous surgical specialities worldwide, there is now a need to develop and enhance the technology further. One such improvement is the implementation and amalgamation of haptic feedback technology into RS which will permit the operating surgeon on the console to receive haptic information on the type of tissue being operated on. The main advantage of using this is to allow the operating surgeon to feel and control the amount of force applied to different tissues during surgery thus minimising the risk of tissue damage due to both the direct and indirect effects of excessive tissue force or tension being applied during RS. We performed a two-rater systematic review to identify the latest developments and potential avenues of improving technology in the application and implementation of haptic feedback technology to the operating surgeon on the console during RS. This review provides a summary of technological enhancements in RS, considering different stages of work, from proof of concept to cadaver tissue testing, surgery in animals, and finally real implementation in surgical practice. We identify that at the time of this review, while there is a unanimous agreement regarding need for haptic and tactile feedback, there are no solutions or products available that address this need. There is a scope and need for new developments in haptic augmentation for robot-mediated surgery with the aim of improving patient care and robotic surgical technology further.Peer reviewe

Advanced Magnetic Resonance Imaging in Glioblastoma: A Review

INTRODUCTION In 2017, it is estimated that 26,070 patients will be diagnosed with a malignant primary brain tumor in the United States, with more than half having the diagnosis of glioblas- toma (GBM).1 Magnetic resonance imaging (MRI) is a widely utilized examination in the diagnosis and post-treatment management of patients with glioblastoma; standard modalities available from any clinical MRI scanner, including T1, T2, T2-FLAIR, and T1-contrast-enhanced (T1CE) sequences, provide critical clinical information. In the last decade, advanced imaging modalities are increasingly utilized to further charac- terize glioblastomas. These include multi-parametric MRI sequences, such as dynamic contrast enhancement (DCE), dynamic susceptibility contrast (DSC), diffusion tensor imaging (DTI), functional imaging, and spectroscopy (MRS), to further characterize glioblastomas, and significant efforts are ongoing to implement these advanced imaging modalities into improved clinical workflows and personalized therapy approaches. A contemporary review of standard and advanced MR imaging in clinical neuro-oncologic practice is presented