Background Surgeons are pursuing accurate head and neck reconstruction to enhance aesthetic and functional outcomes after oncologic resection. This study aimed to investigate whether accuracy of head and neck reconstruction is improved with the use of three-dimensionally (3D)-printed patient-specific surgical plates compared with conventional plates. Methods In this comparative study, patients were prospectively recruited into the study group (3DJP16) with 3D-printed patient-specific surgical plates. The patients in control group with conventional surgical plates were from a historic cohort in the same unit. The primary end point of the study was the accuracy of head and neck reconstruction. The secondary end points were accuracy of osteotomy, intraoperative blood loss, total operative time, and length of hospital stay. Results The study recruited of 33 patients, including 17 in the study group and 16 in the control group. The patients’ baseline characteristics were similar between the two groups. The absolute distance deviation of the maxilla or mandible was 1.5 ± 0.5 mm in the study group and 2.1 ± 0.7 mm in the control group [mean difference, − 0.7 mm; 95% confidence interval (CI) − 1.1 to − 0.3; p = 0.003], showing superior accuracy of reconstruction for the patients with 3D-printed patient-specific surgical plates. Improved accuracy of reconstruction also was detected in terms of bilateral mandibular angles and bone grafts. Concerning the secondary end points, the accuracy of the osteotomy was similar in the two groups. No difference was found regarding intraoperative blood loss, total operative time, or length of hospital stay. Conclusions This is the first study to prove that compared with conventional plates, 3D-printed patient-specific surgical plates improve the accuracy of oncologic head and neck reconstruction.

Background: Left atrial appendage (LAA) occlusion is an alternative to anticoagulation for stroke prevention in patients with atrial fibrillation. Accurate device sizing is crucial for optimal outcome. Patient-specific LAA models can be created using three-dimensional (3D) printing from 3D transesophageal echocardiographic (TEE) images, allowing in vitro model testing for device selection. The aims of this study were to assess the association of model-based device selection with procedural safety and efficacy and to determine if preprocedural model testing leads to superior outcomes. Methods: In 72 patients who underwent imaging-guided LAA occlusion, 3D models of the LAA were created from 3D TEE data sets retrospectively (retrospective cohort). The optimal device determined by in vitro model testing was compared with the actual device used. Associations of model-match and model-mismatch device sizing with outcomes were analyzed. In another 32 patients, device selection was prospectively guided by 3D models in adjunct to imaging (prospective cohort). The impact of model-based sizing on outcomes was assessed by comparing the two cohorts. Results: Patients in the retrospective cohort with model-mismatch sizing had longer procedure times, more implantation failures, more devices used per procedure, more procedural complications, more peridevice leak, more device thrombus, and higher cumulative incidence rates of ischemic stroke and cardiovascular or unexplained death (P < .05 for all) over 3.0 6 2.3 years after LAA occlusion. Compared with the retrospective imaging-guided cohort, the prospective model-guided patients achieved higher implantation success and shorter procedural times (P < .05) without complications. Clinical device compression (r = 0.92) and protrusion (r = 0.95) agreed highly with model testing (P < .0001). Predictors for sizing mismatch were nonwindsock morphology (odds ratio, 4.7) and prominent LAA trabeculations (odds ratio, 7.1). Conclusions: In patients undergoing LAA occlusion, device size selection in agreement with 3D-printed modelbased sizing is associated with improved safety and efficacy. Preprocedural device sizing with 3D models in adjunct to imaging guidance may lead to superior outcomes.

Bioinspired robotic structures comprising soft actuation units have attracted increasing research interest. Taking advantage of its inherent compliance, soft robots can assure safe interaction with external environments, provided that precise and effective manipulation could be achieved. Endoscopy is a typical application. However, previous model-based control approaches often require simplified geometric assumptions on the soft manipulator, but which could be very inaccurate in the presence of unmodeled external interaction forces. In this study, we propose a generic control framework based on nonparametric and online, as well as local, training to learn the inverse model directly, without prior knowledge of the robot's structural parameters. Detailed experimental evaluation was conducted on a soft robot prototype with control redundancy, performing trajectory tracking in dynamically constrained environments. Advanced element formulation of finite element analysis is employed to initialize the control policy, hence eliminating the need for random exploration in the robot's workspace. The proposed control framework enabled a soft fluid-driven continuum robot to follow a 3D trajectory precisely, even under dynamic external disturbance. Such enhanced control accuracy and adaptability would facilitate effective endoscopic navigation in complex and changing environments.

Magnetic resonance (MR) imaging (MRI) provides compelling features for the guidance of interventional procedures, including high-contrast soft tissue imaging, detailed visualization of physiological changes, and thermometry. Laser-based tumor ablation stands to benefit greatly from MRI guidance because 3D resection margins alongside thermal distributions can be evaluated in real time to protect critical structures while ensuring adequate resection margins. However, few studies have investigated the use of projection-based lasers like those for transoral laser microsurgery, potentially because dexterous laser steering is required at the ablation site, raising substantial challenges in the confined MRI bore and its strong magnetic field. Here, we propose an MR-safe soft robotic system for MRI-guided transoral laser microsurgery. Owing to its miniature size (Ø12 × 100 mm), inherent compliance, and five degrees of freedom, the soft robot ensures zero electromagnetic interference with MRI and enables safe and dexterous operation within the confined oral and pharyngeal cavities. The laser manipulator is rapidly fabricated with hybrid soft and hard structures and is powered by microvolume (<0.004 milliter) fluid flow to enable laser steering with enhanced stiffness and lowered hysteresis. A learning-based controller accommodates the inherent nonlinear robot actuation, which was validated with laser path–following tests. Submillimeter laser steering accuracy was demonstrated with a mean error < 0.20 mm. MRI compatibility testing demonstrated zero observable image artifacts during robot operation. Ex vivo tissue ablation and a cadaveric head-and-neck trial were carried out under MRI, where we employed MR thermometry to monitor the tissue ablation margin and thermal diffusion intraoperatively.

Bio-inspired robotic structure composed of soft actuation units has attracted increasing research interests in its potential and capacity of complying with unstructured and dynamic environment, as well as providing safe interaction with human; however, this inevitably poses technical challenging to achieve steady, reliable control due to the remarkable non-linearity of its kinematics and dynamics. To resolve this challenge, we propose a novel control framework that can characterize the kinematics of a soft continuum robot through the hyper-elastic Finiteelement modeling (FEM). This enables frequent updates of the Jacobian mapping from the user motion input to the endeffector's point of view. Experimental validation has been conducted to show the feasibility of controlling the soft robot for intracavitary path following. This could be the first success to demonstrate the perspectives of achieving stable, accurate and effective manipulation under large change of robot morphology without having to deduce its analytical model. It is anticipated to draw further extensive attention on resolving the bottleneck against the application of FEM, namely its intensive computation. Index Terms-Finite-element modeling (FEM), Soft Robot Motion Control I. INTRODUCTION Recent advances in soft robotics have attracted much research attention. Inspired by nature [1-3], the robot made of soft materials facilitates many applications demanding for safe manipulation interacting with human, as well as excellent compliance with confined, unstructured and dynamic environment. Minimally invasive surgery could be a typical example [4, 5]. The inherent flexibility theoretically poses infinite degrees of freedom (DoFs). Unlike the conventional robots comprising of rigid linkages between joints, the kinematics and dynamics of soft robot cannot be explicit and well-formulated, but also susceptible to structural error due to its hyper non-linearity. Many research attempts [6-9] were to deduce the analytical model which simulates the robot kinematics behavior. Analytical modeling is a common approach to formulate the kinematics of small and simple structured robots [10]. However, in particular for high DoFs or hyperelastic robot structure, the complexity of the model will increase tremendously with the actuation dimensionality, and it will be very challenging to formulate a precise 1

No abstract