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Compact, low‐inertia, and soft compliant robotic joint mechanisms are in great demand for ensuring safe interactions in human–robot collaborative tasks. Tensegrity, of which the structural integrity is constrained by tension, does not involve static/sliding friction among the rigid components. However, this mechanical stability is very susceptible to actuation errors. It requires complex kinematics modeling and sophisticated control model with sensing feedback. Herein, a low‐inertia tensegrity joint that is covered/protected by a fiber Bragg grating (FBG)‐embedded silicone sheath is proposed, with the aim to reinforce the joint motion stability and enable self‐contained sensing feedback. A learning‐based closed‐loop controller is also designed and trained with the proper joint configurations selected by a two‐step sampling method. Both the kinematics and static equilibriums of such configurations can be well satisfied. The experiments demonstrate that the joint can follow paths accurately in 2D by compensating manipulation error shortly under the closed‐loop control. The joint stiffness can also be varied against the external/impulsive disturbances. It can be foreseen that this primitive robot joint component with 2 degrees of freedom (DoFs) can provide safe, compliant interaction with human, for which a simple test of maneuvering a portable ultrasound probe (≈210 g) for abdominal imaging is demonstrated.

Optical waveguides create interesting opportunities in the area of soft sensing and electronic skins due to their potential for high flexibility, quick response time, and compactness. The loss or change of light intensities inside a waveguide can be measured and converted into useful sensing feedback such as strain or shape sensing. Compared to other approaches such as those based on microelectromechanical system modules or flexible conductors, the entire sensor state can be characterized by fewer sensing nodes and less encumbering wiring, allowing greater scalability. Herein, simple light‐emitting diodes (LEDs) and photodetectors (PDs) combined with an intelligent shape decoding framework are utilized to enable 3D shape sensing of a self‐contained flexible substrate. Multiphysics finite element analysis is leveraged to optimize the PDs/LEDs layout and enrich ground‐truth data from sparse to dense points for model training. The mapping from light intensities to overall sensor shape is achieved with an autoregression‐based model that considers temporal continuity and spatial locality. The sensing framework is evaluated on an A5‐sized flexible sensor prototype and a fish‐shaped prototype, where sensing accuracy (RMSE = 0.27 mm) and repeatability (Δ light intensity

Abstract Magnetic resonance imaging (MRI) demonstrates clear advantages over other imaging modalities in neurosurgery with its ability to delineate critical neurovascular structures and cancerous tissue in high‐resolution 3D anatomical roadmaps. However, its application has been limited to interventions performed based on static pre/post‐operative imaging, where errors accrue from stereotactic frame setup, image registration, and brain shift. To leverage the powerful intra‐operative functions of MRI, e.g., instrument tracking, monitoring of physiological changes and tissue temperature in MRI‐guided bilateral stereotactic neurosurgery, a multi‐stage robotic positioner is proposed. The system positions cannula/needle instruments using a lightweight (203 g) and compact (Ø97 × 81 mm) skull‐mounted structure that fits within most standard imaging head coils. With optimized design in soft robotics, the system operates in two stages: i) manual coarse adjustment performed interactively by the surgeon (workspace of ±30°), ii) automatic fine adjustment with precise (

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a valuable diagnostic tool in prostate lesion assessment. However, training convolutional neural networks (CNNs) inevitably involves magnetic resonance (MR) images from multiple cohorts. There always exists variation in scanning protocol among cohorts, inducing significant changes in data distribution between source and target domains. This challenge has greatly limited clinical adoption on a large scale. Herein, a coarse mask‐guided deep domain adaptation network (CMD2A‐Net) is proposed to develop a fully automated framework for prostate lesion detection and classification (PLDC). No category or mask label is required from the target domain. A coarse segmentation module is trained to cover the possible lesion‐related regions, so that attention maps can be generated to dedicate the local feature extraction of lesions within those regions. Experiments are performed on 512 mpMRI sets from datasets of PROSTATEx (330 sets) and two cohorts, A (74 sets) and B (108 sets). Using ensemble learning, CMD2A‐Net accomplishes an AUC of 0.921 in cohort A and 0.913 in cohort B, demonstrating its transferability from a large‐scale public dataset PROSTATEx to small‐scale target domains. Results from an ablation study also support its effectiveness in classification between benign and malignant lesions, compared to the state‐of‐the‐art models. An interactive preprint version of the article can be found here: https://doi.org/10.22541/au.166081031.11420810/v1.

Soft manipulators integrated with optical laser fibers offer new opportunities for endoscopic noncontact laser surgery. To achieve precise laser projection in a confined workspace and avoid damage, a controller with high accuracy and stability is required. An effective way is to close the control loop. Therefore, a visual servo controller that allows automatic laser spot steering using soft manipulators is proposed. An epipolar geometry model is established to acquire the inverse transition mapping from image to actuation. With this mapping, a feedback controller is derived without prior information of tissue surface geometry. Experimental validation demonstrates accurate path following using a magnetic resonance‐safe manipulator, with root‐mean‐square (RMS) tracking errors

BackgroundTo better understand the different clinical phenotypes across the disease spectrum in patients with COVID-19 using an unsupervised machine learning clustering approach.Materials and MethodsA population-based retrospective study was conducted utilizing demographics, clinical characteristics, comorbidities, and clinical outcomes of 7,606 COVID-19–positive patients on admission to public hospitals in Hong Kong in the year 2020. An unsupervised machine learning clustering was used to explore this large cohort.ResultsFour clusters of differing clinical phenotypes based on data at initial admission was derived in which 86.6% of the deceased cases were aggregated in one of the clusters without prior knowledge of their clinical outcomes. Other distinctive clinical characteristics of this cluster were old age and high concurrent comorbidities as well as laboratory characteristics of lower hemoglobin/hematocrit levels, higher neutrophil, C-reactive protein, lactate dehydrogenase, and creatinine levels. The clinical patterns captured by the cluster analysis was validated on other temporally distinct cohorts in 2021. The phenotypes aligned with existing literature.ConclusionThe study demonstrated the usefulness of unsupervised machine learning techniques with the potential to uncover latent clinical phenotypes. It could serve as a more robust classification for patient triaging and patient-tailored treatment strategies.

Proprioception, the ability to perceive one's own configuration and movement in space, enables organisms to safely and accurately interact with their environment and each other. The underlying sensory nerves that make this possible are highly dense and use sophisticated communication pathways to propagate signals from nerves in muscle, skin, and joints to the central nervous system wherein the organism can process and react to stimuli. In a step forward to realize robots with such perceptive capability, a flexible sensor framework that incorporates a novel modeling strategy, taking advantage of computational mechanics and machine learning, is proposed. The sensor framework on a large flexible sensor that transforms sparsely distributed strains into continuous surface is implemented. Finite element (FE) analysis is utilized to determine design parameters, while an FE model is built to enrich the morphological data used in the supervised training to achieve continuous surface reconstruction. A mapping between the local strain data and the enriched surface data is subsequently trained using ensemble learning. This hybrid approach enables real time, robust, and high‐order surface reconstruction. The sensing performance is evaluated in terms of accuracy, repeatability, and feasibility with numerous scenarios, which has not been demonstrated on such a large‐scale sensor before.

Soft continuum robots have been accepted as a promising category of biomedical robots, accredited to the robots’ inherent compliance that makes them safely interact with their surroundings. In its application of minimally invasive surgery, such a continuum concept shares the same view of robotization for conventional endoscopy/laparoscopy. Different from rigid-link robots with accurate analytical kinematics/dynamics, soft robots encounter modeling uncertainties due to intrinsic and extrinsic factors, which would deteriorate the model-based control performances. However, the trade-off between flexibility and controllability of soft manipulators may not be readily optimized but would be demanded for specific kinds of modeling approaches. To this end, data-driven modeling strategies making use of machine learning algorithms would be an encouraging way out for the control of soft continuum robots. In this article, we attempt to overview the current state of kinematic/dynamic model-free control schemes for continuum manipulators, particularly by learning-based means, and discuss their similarities and differences. Perspectives and trends in the development of new control methods are also investigated through the review of existing limitations and challenges.

Identifying Clostridioides difficile infection (CDI) patients at risk of mortality or recurrence facilitates prevention, timely treatment, and improves clinical outcomes. The aim herein is to establish an open‐access web‐based prediction system, which estimates CDI patients’ mortality and recurrence outcomes and explains machine learning prediction with patients’ characteristics. Prognostic models are developed using four various types of machine learning algorithms and the statistical logistics regression model utilizing over 15 000 CDI patients from 41 hospitals in Hong Kong. The boosting‐based machine learning algorithm gradient boosting machine (GBM) (Mortality AUC: 0.7878; Recurrence AUC: 0.7076) outperforms statistical models (Mortality AUC: 0.7573; Recurrence AUC: 0.6927) and other machine learning algorithms. As the difficulty to interpret complex machine learning results limits their use in the medical area, Shapley additive explanations (SHAP) are adapted to identify which features are crucial to the machine learning models and associate them with clinical findings. SHAP analysis shows that older age, reduced albumin levels, higher creatinine levels, and higher white blood cell count are the most highly associated mortality features, which is consistent with existing clinical findings. The open‐access prediction system for clinicians to assess and interpret the risk factors of CDI patients is now available at https://www.cdiml.care/.

Collision detection, which refers to the computational problem of finding the relative placement or con-figuration of two or more objects, is an essential component of many applications in computer graphics and robotics. In image-guided robotic surgery, real-time collision detection is critical for preserving healthy anatomical structures during the surgical procedure. However, the computational complexity of the problem usually results in algorithms that operate at low speed. In this paper, we present a fast and accurate algorithm for collision detection between Oriented-Bounding-Boxes (OBBs) that is suitable for real-time implementation. Our proposed Sweep and Prune algorithm can perform a preliminary filtering to reduce the number of objects that need to be tested by the classical Separating Axis Test algorithm, while the OBB pairs of interest are preserved. These OBB pairs are re-checked by the Separating Axis Test algorithm to obtain accurate overlapping status between them. To accelerate the execution, our Sweep and Prune algorithm is tailor-made for the proposed method. Meanwhile, a high performance scalable hardware architecture is proposed by analyzing the intrinsic parallelism of our algorithm, and is implemented on FPGA platform. Results show that our hardware design on the FPGA platform can achieve around 8X higher running speed than the software design on a CPU platform. As a result, the proposed algorithm can achieve a collision frame rate of 1 KHz, and fulfill the requirement for the medical surgery scenario of Robot Assisted Laparoscopy.