Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519

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

Grid simulation services for the medical community

The first part of this paper presents a selection of medical simulation applications, including image reconstruction, near real-time registration for neuro-surgery, enhanced dose distribution calculation for radio-therapy, inhaled drug delivery prediction, plastic surgery planning and cardio-vascular system simulation. The latter two topics are discussed in some detail. In the second part, we show how such services can be made available to the clinical practitioner using Grid technology. We discuss the developments and experience made during the EU project GEMSS, which provides reliable, efficient, secure and lawful medical Grid services

Personalized medicine in surgical treatment combining tracking systems, augmented reality and 3D printing

Mención Internacional en el título de doctorIn the last twenty years, a new way of practicing medicine has been focusing on the problems and needs of each patient as an individual thanks to the significant advances in healthcare technology, the so-called personalized medicine. In surgical treatments, personalization has been possible thanks to key technologies adapted to the specific anatomy of each patient and the needs of the physicians. Tracking systems, augmented reality (AR), three-dimensional (3D) printing and artificial intelligence (AI) have previously supported this individualized medicine in many ways. However, their independent contributions show several limitations in terms of patient-to-image registration, lack of flexibility to adapt to the requirements of each case, large preoperative planning times, and navigation complexity. The main objective of this thesis is to increase patient personalization in surgical treatments by combining these technologies to bring surgical navigation to new complex cases by developing new patient registration methods, designing patient-specific tools, facilitating access to augmented reality by the medical community, and automating surgical workflows. In the first part of this dissertation, we present a novel framework for acral tumor resection combining intraoperative open-source navigation software, based on an optical tracking system, and desktop 3D printing. We used additive manufacturing to create a patient-specific mold that maintained the same position of the distal extremity during image-guided surgery as in the preoperative images. The feasibility of the proposed workflow was evaluated in two clinical cases (soft-tissue sarcomas in hand and foot). We achieved an overall accuracy of the system of 1.88 mm evaluated on the patient-specific 3D printed phantoms. Surgical navigation was feasible during both surgeries, allowing surgeons to verify the tumor resection margin. Then, we propose and augmented reality navigation system that uses 3D printed surgical guides with a tracking pattern enabling automatic patient-to-image registration in orthopedic oncology. This specific tool fits on the patient only in a pre-designed location, in this case bone tissue. This solution has been developed as a software application running on Microsoft HoloLens. The workflow was validated on a 3D printed phantom replicating the anatomy of a patient presenting an extraosseous Ewing’s sarcoma, and then tested during the actual surgical intervention. The results showed that the surgical guide with the reference marker can be placed precisely with an accuracy of 2 mm and a visualization error lower than 3 mm. The application allowed physicians to visualize the skin, bone, tumor and medical images overlaid on the phantom and patient. To enable the use of AR and 3D printing by inexperienced users without broad technical knowledge, we designed a step-by-step methodology. The proposed protocol describes how to develop an AR smartphone application that allows superimposing any patient-based 3D model onto a real-world environment using a 3D printed marker tracked by the smartphone camera. Our solution brings AR solutions closer to the final clinical user, combining free and open-source software with an open-access protocol. The proposed guide is already helping to accelerate the adoption of these technologies by medical professionals and researchers. In the next section of the thesis, we wanted to show the benefits of combining these technologies during different stages of the surgical workflow in orthopedic oncology. We designed a novel AR-based smartphone application that can display the patient’s anatomy and the tumor’s location. A 3D printed reference marker, designed to fit in a unique position of the affected bone tissue, enables automatic registration. The system has been evaluated in terms of visualization accuracy and usability during the whole surgical workflow on six realistic phantoms achieving a visualization error below 3 mm. The AR system was tested in two clinical cases during surgical planning, patient communication, and surgical intervention. These results and the positive feedback obtained from surgeons and patients suggest that the combination of AR and 3D printing can improve efficacy, accuracy, and patients’ experience In the final section, two surgical navigation systems have been developed and evaluated to guide electrode placement in sacral neurostimulation procedures based on optical tracking and augmented reality. Our results show that both systems could minimize patient discomfort and improve surgical outcomes by reducing needle insertion time and number of punctures. Additionally, we proposed a feasible clinical workflow for guiding SNS interventions with both navigation methodologies, including automatically creating sacral virtual 3D models for trajectory definition using artificial intelligence and intraoperative patient-to-image registration. To conclude, in this thesis we have demonstrated that the combination of technologies such as tracking systems, augmented reality, 3D printing, and artificial intelligence overcomes many current limitations in surgical treatments. Our results encourage the medical community to combine these technologies to improve surgical workflows and outcomes in more clinical scenarios.Programa de Doctorado en Ciencia y Tecnología Biomédica por la Universidad Carlos III de MadridPresidenta: María Jesús Ledesma Carbayo.- Secretaria: María Arrate Muñoz Barrutia.- Vocal: Csaba Pinte

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

X23D-Intraoperative 3D Lumbar Spine Shape Reconstruction Based on Sparse Multi-View X-ray Data

Visual assessment based on intraoperative 2D X-rays remains the predominant aid for intraoperative decision-making, surgical guidance, and error prevention. However, correctly assessing the 3D shape of complex anatomies, such as the spine, based on planar fluoroscopic images remains a challenge even for experienced surgeons. This work proposes a novel deep learning-based method to intraoperatively estimate the 3D shape of patients' lumbar vertebrae directly from sparse, multi-view X-ray data. High-quality and accurate 3D reconstructions were achieved with a learned multi-view stereo machine approach capable of incorporating the X-ray calibration parameters in the neural network. This strategy allowed a priori knowledge of the spinal shape to be acquired while preserving patient specificity and achieving a higher accuracy compared to the state of the art. Our method was trained and evaluated on 17,420 fluoroscopy images that were digitally reconstructed from the public CTSpine1K dataset. As evaluated by unseen data, we achieved an 88% average F1 score and a 71% surface score. Furthermore, by utilizing the calibration parameters of the input X-rays, our method outperformed a counterpart method in the state of the art by 22% in terms of surface score. This increase in accuracy opens new possibilities for surgical navigation and intraoperative decision-making solely based on intraoperative data, especially in surgical applications where the acquisition of 3D image data is not part of the standard clinical workflow