This work presents a novel miniature contact-aided compliant joint mechanism that can be integrated into millimeter-sized manual or robotic surgical instruments. The design aims to address the trade-off between notched-tube compliant joints' range of motion and stiffness, while also ensuring a compact form factor. The mechanism is constructed from a nitinol tube with asymmetric cutouts and is actuated in bending by a cable. The innovative feature of this design is the incorporation of a contact aid into the notched-tube topology, which acts to both increase the stiffness of the joint and change the shape that it undertakes during bending. Using finite element modeling (FEM) techniques, we present a sensitivity analysis investigating how the performance of this contact-aided compliant mechanism (CCM) is affected by its geometry, and derive a kinematics and statics model for the joint. The FEM simulations and the kinematic and static models are compared to experimental results. The design and modeling presented in this study can be used to develop new miniature dexterous instruments, with a particular emphasis on applications in minimally invasive neurosurgery.

Background: Artificial Intelligence (AI) is recognized by emergency physicians (EPs) as an important technology that will affect clinical practice. Several AI-tools have already been developed to aid care delivery in emergency medicine (EM). However, many EM tools appear to have been developed without a cross-disciplinary needs assessment, making it difficult to understand their broader importance to general-practice. Clinician surveys about AI tools have been conducted within other medical specialties to help guide future design. This study aims to understand the needs of Canadian EPs for the apt use of AI-based tools.Methods: A national cross-sectional, two-stage, mixed-method electronic survey of Canadian EPs was conducted from January-May 2022. The survey includes demographic and physician practice-pattern data, clinicians’ current use and perceptions of AI, and individual rankings of which EM work-activities most benefit from AI.Results: The primary outcome is a ranked list of high-priority AI-tools for EM that physicians want translated into general use within the next 10 years. When ranking specific AI examples, ‘automated charting/report generation’, ‘clinical prediction rules’ and ‘monitoring vitals with early-warning detection’ were the top items. When ranking by physician work-activities, ‘AI-tools for documentation’, ‘AI-tools for computer use’ and ‘AI-tools for triaging patients’ were the top items. For secondary outcomes, EPs indicated AI was ‘likely’ (43.1%) or ‘extremely likely’ (43.7%) to be able to complete the task of ‘documentation’ and indicated either ‘a-great-deal’ (32.8%) or ‘quite-a-bit’ (39.7%) of potential for AI in EM. Further, EPs were either ‘strongly’ (48.5%) or ‘somewhat’ (39.8%) interested in AI for EM.Conclusions: Physician input on the design of AI is essential to ensure the uptake of this technology. Translation of AI-tools to facilitate documentation is considered a high-priority, and respondents had high confidence that AI could facilitate this task. This study will guide future directions regarding the use of AI for EM and help direct efforts to address prevailing technology-translation barriers such as access to high-quality application-specific data and developing reporting guidelines for specific AI-applications. With a prioritized list of high-need AI applications, decision-makers can develop focused strategies to address these larger obstacles.

The da Vinci Surgical System is a commercially available surgical robotics platform used to perform more than 450,000 surgeries annually, primarily for gynecological and urological procedures [1]. This technology enhances the surgeon's dexterity through the use of a multiarm robot with wristed instruments that enter a patient's body through small incisions. However, the size of existing instruments (5-8 mm in shaft diameter) has prevented the adoption of the da Vinci system in areas such as neurosurgery, head and neck surgery, and pediatrics [2], [3]. These applications require high dexterity in small, confined workspaces, and, therefore, new miniaturized tools are needed [4].

We present a kinetostatic model for a needle-sized notched tube continuum joint design, and examine the impact that the design parameters have on both stiffness and joint kinematics. The joint is fabricated by removing a series of asymmetric notches from a metal tube. By fixing a cable to the distal end of the tube, and routing this tendon through the tube's lumen, the joint can be actuated in bending. This simple cutting pattern allows for impressive performance compared to other mechanisms of similar size. However, selecting the cutting geometry using kinematics alone results in many redundant solutions. Since the notches significantly affect the overall structure's stiffness, and limit the maximum forces that it can transmit, a stiffness model can be used to constrain the design-space. Further, because the notch geometry represents a non-prismatic beam configuration, modelling the force-deflection behavior is nontrivial. We have approached this problem using a variation of Castigliano's 2^nd Theorem, experimentally validated its performance with several test specimens, and analyzed the behavior of the model over a design space specific to neurosurgical applications. The outcomes of this study aim to aid in the design of joints given task-specific constraints, particularly within the field of surgical robotics.

OBJECTIVE: Endoscope-assisted repair of craniosynostosis is a safe and efficacious alternative to open techniques. However, this procedure is challenging to learn, and there is significant variation in both its execution and outcomes. Surgical simulators may allow trainees to learn and practice this procedure prior to operating on an actual patient. The purpose of this study was to develop a realistic, relatively inexpensive simulator for endoscope-assisted repair of metopic and sagittal craniosynostosis and to evaluate the models’ fidelity and teaching content.METHODS: Two separate, 3D-printed, plastic powder–based replica skulls exhibiting metopic (age 1 month) and sagittal (age 2 months) craniosynostosis were developed. These models were made into consumable skull “cartridges” that insert into a reusable base resembling an infant’s head. Each cartridge consists of a multilayer scalp (skin, subcutaneous fat, galea, and periosteum); cranial bones with accurate landmarks; and the dura mater. Data related to model construction, use, and cost were collected. Eleven novice surgeons (residents), 9 experienced surgeons (fellows), and 5 expert surgeons (attendings) performed a simulated metopic and sagittal craniosynostosis repair using a neuroendoscope, high-speed drill, rongeurs, lighted retractors, and suction/irrigation. All participants completed a 13-item questionnaire (using 5-point Likert scales) to rate the realism and utility of the models for teaching endoscope-assisted strip suturectomy.RESULTS: The simulators are compact, robust, and relatively inexpensive. They can be rapidly reset for repeated use and contain a minimal amount of consumable material while providing a realistic simulation experience. More than 80% of participants agreed or strongly agreed that the models’ anatomical features, including surface anatomy, subgaleal and subperiosteal tissue planes, anterior fontanelle, and epidural spaces, were realistic and contained appropriate detail. More than 90% of participants indicated that handling the endoscope and the instruments was realistic, and also that the steps required to perform the procedure were representative of the steps required in real life.CONCLUSIONS: Both the metopic and sagittal craniosynostosis simulators were developed using low-cost methods and were successfully designed to be reusable. The simulators were found to realistically represent the surgical procedure and can be used to develop the technical skills required for performing an endoscope-assisted craniosynostosis repair.