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Intraluminal epithelial abnormalities, potential precursors to significant conditions like cancer, necessitate early detection for improved prognosis. We present a motor-free telerobotic optical coherence tomography (OCT) endoscope that offers high-resolution intraluminal imaging and overcomes the limitations of traditional systems in navigating curved lumens. This system incorporates a compact magnetic rotor with a rotatable diametrically magnetized cylinder permanent magnet (RDPM) and a reflector, effectively mitigating thermal and electrical risks by utilizing an external magnetic field to maintain temperature increases below 0.5 °C and generated voltage under 0.02 mV. Additionally, a learning-based method corrects imaging distortions resulting from nonuniform rotational speeds. Demonstrating superior maneuverability, the device achieves steerable angles up to 110° and operates effectively in vivo, providing distortion-free 3D programmable imaging in mouse colons. This advancement represents a significant step towards guidewire-independent endomicroscopy, enhancing both safety and potential patient outcomes. Abnormalities in intraluminal epithelium can point to serious diseases. Here, the authors present a mono-magnet actuated OCT endoscope that enables high-resolution, programmable imaging in complex lumens without local heat effects, with safety and quality in thermoelectrically sensitive scenarios. [ABSTRACT FROM AUTHOR]

Purpose: Depth estimation in robotic surgery is vital in 3D reconstruction, surgical navigation and augmented reality visualization. Although the foundation model exhibits outstanding performance in many vision tasks, including depth estimation (e.g., DINOv2), recent works observed its limitations in medical and surgical domain-specific applications. This work presents a low-ranked adaptation (LoRA) of the foundation model for surgical depth estimation. Methods: We design a foundation model-based depth estimation method, referred to as Surgical-DINO, a low-rank adaptation of the DINOv2 for depth estimation in endoscopic surgery. We build LoRA layers and integrate them into DINO to adapt with surgery-specific domain knowledge instead of conventional fine-tuning. During training, we freeze the DINO image encoder, which shows excellent visual representation capacity, and only optimize the LoRA layers and depth decoder to integrate features from the surgical scene. Results: Our model is extensively validated on a MICCAI challenge dataset of SCARED, which is collected from da Vinci Xi endoscope surgery. We empirically show that Surgical-DINO significantly outperforms all the state-of-the-art models in endoscopic depth estimation tasks. The analysis with ablation studies has shown evidence of the remarkable effect of our LoRA layers and adaptation. Conclusion: Surgical-DINO shed some light on the successful adaptation of the foundation models into the surgical domain for depth estimation. There is clear evidence in the results that zero-shot prediction on pre-trained weights in computer vision datasets or naive fine-tuning is not sufficient to use the foundation model in the surgical domain directly. [ABSTRACT FROM AUTHOR]

Purpose: In curriculum learning, the idea is to train on easier samples first and gradually increase the difficulty, while in self-paced learning, a pacing function defines the speed to adapt the training progress. While both methods heavily rely on the ability to score the difficulty of data samples, an optimal scoring function is still under exploration. Methodology: Distillation is a knowledge transfer approach where a teacher network guides a student network by feeding a sequence of random samples. We argue that guiding student networks with an efficient curriculum strategy can improve model generalization and robustness. For this purpose, we design an uncertainty-based paced curriculum learning in self-distillation for medical image segmentation. We fuse the prediction uncertainty and annotation boundary uncertainty to develop a novel paced-curriculum distillation (P-CD). We utilize the teacher model to obtain prediction uncertainty and spatially varying label smoothing with Gaussian kernel to generate segmentation boundary uncertainty from the annotation. We also investigate the robustness of our method by applying various types and severity of image perturbation and corruption. Results: The proposed technique is validated on two medical datasets of breast ultrasound image segmentation and robot-assisted surgical scene segmentation and achieved significantly better performance in terms of segmentation and robustness. Conclusion: P-CD improves the performance and obtains better generalization and robustness over the dataset shift. While curriculum learning requires extensive tuning of hyper-parameters for pacing function, the level of performance improvement suppresses this limitation. [ABSTRACT FROM AUTHOR]

Video-assisted transoral tracheal intubation (TI) necessitates using an endoscope that helps the physician insert a tracheal tube into the glottis instead of the esophagus. The growing trend of robotic-assisted TI would require a medical robot to distinguish anatomical features like an experienced physician which can be imitated by utilizing supervised deep-learning techniques. However, the real datasets of oropharyngeal organs are often inaccessible due to limited open-source data and patient privacy. In this work, we propose a domain adaptive Sim-to-Real framework called IoU-Ranking Blend-ArtFlow (IRB-AF) for image segmentation of oropharyngeal organs. The framework includes an image blending strategy called IoU-Ranking Blend (IRB) and style-transfer method ArtFlow. Here, IRB alleviates the problem of poor segmentation performance caused by significant datasets domain differences, while ArtFlow is introduced to reduce the discrepancies between datasets further. A virtual oropharynx image dataset generated by the SOFA framework is used as the learning subject for semantic segmentation to deal with the limited availability of actual endoscopic images. We adapted IRB-AF with the state-of-the-art domain adaptive segmentation models. The results demonstrate the superior performance of our approach in further improving the segmentation accuracy and training stability. [ABSTRACT FROM AUTHOR]

Transformer-based methods have led to the revolutionizing of multiple computer vision tasks. Inspired by this, we propose a transformer-based network with a channel-enhanced attention module to explore contextual and spatial information in non-contrast (NC) and contrast-enhanced (CE) computed tomography (CT) images for pulmonary vessel segmentation and artery-vein separation. Our proposed network employs a 3D contextual transformer module in the encoder and decoder part and a double attention module in skip connection to effectively finish high-quality vessel and artery-vein segmentation. Extensive experiments are conducted on the in-house dataset and the ISICDM2021 challenge dataset. The in-house dataset includes 56 NC CT scans with vessel annotations and the challenge dataset consists of 14 NC and 14 CE CT scans with vessel and artery-vein annotations. For vessel segmentation, Dice is 0.840 for CE CT and 0.867 for NC CT. For artery-vein separation, the proposed method achieves a Dice of 0.758 of CE images and 0.602 of NC images. Quantitative and qualitative results demonstrated that the proposed method achieved high accuracy for pulmonary vessel segmentation and artery-vein separation. It provides useful support for further research associated with the vascular system in CT images. The code is available at https://github.com/wuyanan513/Pulmonary-Vessel-Segmentation-and-Artery-vein-Separation. [ABSTRACT FROM AUTHOR]

Biopsy is a method commonly used for early cancer diagnosis. However, bleeding complications of widely available biopsy are risky for patients. Safer biopsy will result in a more accurate cancer diagnosis and a decrease in the risk of complications. In this article, we propose a novel biopsy needle that can reduce bleeding during biopsy procedures and achieve stable hemostasis. The proposed biopsy needle features a compact structure and can be operated easily by left and right hands. A predictive model for puncture force and tip deflection based on coupled Eulerian–Lagrangian (CEL) method is developed. Experimental results show that the biopsy needle can smoothly deliver the gelatin sponge hemostatic plug into the tissue. Although the hemostatic plug bends, the overall delivery process is stable, and the hemostatic plug retains in the tissue without being affected by the withdrawal of the needle. Further experiments indicate that the specimens are well obtained and evenly distributed in the groove of the outer needle without scattering. Our proposed design of biopsy needle possesses strong ability of hemostasis, tissue cutting, and tissue retention. The CEL model accurately predicts the peak of puncture force and produces close estimation of the insertion force at the postpuncture stage and tip position. [ABSTRACT FROM AUTHOR]

Purpose: Surgery scene understanding with tool-tissue interaction recognition and automatic report generation can play an important role in intra-operative guidance, decision-making and postoperative analysis in robotic surgery. However, domain shifts between different surgeries with inter and intra-patient variation and novel instruments' appearance degrade the performance of model prediction. Moreover, it requires output from multiple models, which can be computationally expensive and affect real-time performance. Methodology: A multi-task learning (MTL) model is proposed for surgical report generation and tool-tissue interaction prediction that deals with domain shift problems. The model forms of shared feature extractor, mesh-transformer branch for captioning and graph attention branch for tool-tissue interaction prediction. The shared feature extractor employs class incremental contrastive learning to tackle intensity shift and novel class appearance in the target domain. We design Laplacian of Gaussian-based curriculum learning into both shared and task-specific branches to enhance model learning. We incorporate a task-aware asynchronous MTL optimization technique to fine-tune the shared weights and converge both tasks optimally. Results: The proposed MTL model trained using task-aware optimization and fine-tuning techniques reported a balanced performance (BLEU score of 0.4049 for scene captioning and accuracy of 0.3508 for interaction detection) for both tasks on the target domain and performed on-par with single-task models in domain adaptation. Conclusion: The proposed multi-task model was able to adapt to domain shifts, incorporate novel instruments in the target domain, and perform tool-tissue interaction detection and report generation on par with single-task models. [ABSTRACT FROM AUTHOR]

Accurate needle targeting is critical for many clinical procedures, such as transcutaneous biopsy or radiofrequency ablation of tumors. However, targeting errors may arise, limiting the widespread adoption of these procedures. Advances in flexible needle (FN) steering are emerging to mitigate these errors. This review summarizes the state-of-the-art developments of FNs and addresses possible targeting errors that can be overcome with steering actuation techniques. FN steering techniques can be classified as passive and active. Passive steering directly results from the needle-tissue interaction forces, whereas active steering requires additional forces to be applied at the needle tip, which enhances needle steerability. Therefore, the corresponding targeting errors of most passive FNs and active FNs are between 1 and 2 mm, and less than 1 mm, respectively. However, the diameters of active FNs range from 1.42 to 12 mm, which is larger than the passive steering needle varying from 0.5 to 1.4 mm. Therefore, the development of active FNs is an area of active research. These active FNs can be steered using tethered internal direct actuation or untethered external actuation. Examples of tethered internal direct actuation include tendon-driven, longitudinal segment transmission and concentric tube transmission. Tendon-driven FNs have various structures, and longitudinal segment transmission needles could be adapted to reduce tissue damage. Additionally, concentric tube needles have immediate advantages and clinical applications in natural orifice surgery. Magnetic actuation enables active FN steering with untethered external actuation and facilitates miniaturization. The challenges faced in the fabrication, sensing, and actuation methods of FN are analyzed. Finally, bio-inspired FNs may offer solutions to address the challenges faced in FN active steering mechanisms. [ABSTRACT FROM AUTHOR]

All-solid-state Na battery is one of the most promising batteries because it can solve safety and cost issues of current Li battery simultaneously. In the all-solid-state battery, the interface quality between electrodes and solid-state electrolytes is still one of the critical issues. In this work, surface texturing of Na3Zr2Si2PO12 (NZSP) solid electrolyte is studied. Surface texturing is prepared by the femtosecond laser etching. By the laser etching, periodic surface ditches with 15 μm of width is successfully prepared. Impurity formation is not confirmed after the etching, implying that the laser etching would not affect crystal structure of NZSP. All-solid-state Na battery is constructed by using etched NZSP, NaMnO2 cathode, and Na metal anode. The all-solid-state Na battery works 50 cycles stably. The improvement of performance could be achieved by optimization of the surface texture. [ABSTRACT FROM AUTHOR]

Segmentation of intracerebral hemorrhage (ICH) helps improve the quality of diagnosis, draft the desired treatment methods, and clinically observe the variations with healthy patients. The clinical utilization of various ICH progression scoring systems has limitations due to the systems' modest predictive value. This paper proposes a single pipeline of a multi-task model for end-to-end hemorrhage segmentation and risk estimation. We introduce a 3D spatial attention unit and integrate it into the state-of-the-art segmentation architecture, UNet, to enhance the accuracy by bootstrapping the global spatial representation. We further extract the geometric features from the segmented hemorrhage volume and fuse them with clinical features such as CT angiography (CTA) spot, Glasgow Coma Scale (GCS), and age to predict the ICH stability. Several state-of-the-art machine learning techniques such as multilayer perceptron (MLP), support vector machine (SVM), gradient boosting, and random forests are applied to train stability estimation and to compare the performances. To align clinical intuition with model learning, we determine the shapely values (SHAP) and explain the most significant features for the ICH risk scoring system. A total of 79 patients are included, of which 20 are found in critical condition. Our proposed single pipeline model achieves a segmentation accuracy of 86.3%, stability prediction accuracy of 78.3%, and precision of 82.9%; the mean square error of exact expansion rate regression is observed to be 0.46. The SHAP analysis reveals that CTA spot sign, age, solidity, location, and length of the first axis of the ICH volume are the most critical characteristics that help define the stability of the stroke lesion. We also show that integrating significant geometric features with clinical features can improve the ICH progression scoring by predicting long-term outcomes. [ABSTRACT FROM AUTHOR]

A transurethral prostate biopsy device is proposed in this paper, which can shoot a biopsy needle at different angles to take samples from multiple locations within the prostate. Firstly, the traditional prostate biopsy methods, including transrectal prostate biopsy and transperineal prostate biopsy, are introduced and compared. Then, the working principles of the new prostate biopsy procedure are illustrated. The designs of the needle bending system and the flexible needle are presented, and a proofs-of-concept study of the robotic biopsy device is demonstrated. Design parameters, material selection, and control unit are introduced. Experiments are carried out to test and demonstrate the functions of the prototype. Theoretical and measured bending angles are compared and analyzed. The bending system can effectively bend the biopsy needle to any angle between 15 and 45°. The penetration force of the biopsy needle decreases with the increase of the bending angle. The range of rotation of the bending system on one hemisphere is ±25°. Together with the translational motion, the biopsy needle can reach any point within the workspace. Finally, a phantom test and a cadaver experiment were carried out to simulate biopsy. [ABSTRACT FROM AUTHOR]

In this paper, a novel electromagnetic micropositioner is designed from an orthogonal 3-P(4S) parallel mechanism through the substitution method and modular design techniques. Preliminary prototype experiments show that the micropositioner possesses an excellent decoupling performance. Thus an independent control strategy is carried out for the motion control of the micropositioner. An RBF neural networks based adaptive backstepping terminal sliding mode controller is designed according to the nonlinearity characteristics of the actuator. Parameters of the system are identified with a genetic algorithm. Finally, the performances of the micropositioner and the developed control strategy are verified. Experimental results demonstrate that satisfactory performances can be achieved. [ABSTRACT FROM AUTHOR]

This paper presents the concept, design, and experimental characterization of a compliant finger exoskeleton with telescoping super-elastic transmissions. Nickel-Titanium (Ni-Ti) rods with the super-elastic feature are adopted as transmission components for the concentric telescoping mechanism to be flexible and safe. The mechanism of this finger exoskeleton is characterized in detail, including the finger connector, the elastic-rod transmission system (ERTS), and the actuator. The performance of compliance is demonstrated by finite element analysis. Then, the finger motion mapping relationship is captured by the experiments for a two-finger prototype. Experiment results show that certain compliance (e.g., 16∘/2N for the flexion/extension motion of the index finger) can be achieved by the Ni-Ti rods, and reflects in the assisted motion. [ABSTRACT FROM AUTHOR]

Purpose: The combined small-cell lung cancer (c-SCLC) is rare and has unique clinicopathological futures. The aim of this study is to investigate 18F-FDG PET/CT parameters and clinicopathological factors that influence the prognosis of c-SCLC. Methods: Between November 2005 and October 2014, surgical-resected tumor samples from c-SCLC patients who received preoperative 18F-FDG PET/CT examination were retrospectively reviewed. The maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were used to evaluate metabolic parameters in primary tumors. The survivals were evaluated with the Kaplan–Meier method. Univariate and multivariate analyses were used to evaluate potential prognostic factors. Results: Thirty-one patients were enrolled, with a median age of 62 (range: 35 − 79) years. The most common mixed component was squamous cell carcinoma (SCC, n = 12), followed by large-cell carcinoma (LCC, n = 7), adenocarcinoma (AC, n = 6), spindle cell carcinoma (n = 4), adenosquamous carcinoma (n = 1) and atypical carcinoid (n = 1). The median follow-up period was 53.0 (11.0–142.0) months; the 5-year overall survival (OS) and progression-free survival(PFS) rate were 48.4% and 35.5%, respectively. Univariate survival analysis showed that gender, smoking history, tumor location were associated with PFS (P = 0.036, P = 0.043, P = 0.048), SUVmax and TNM stage were closely related to PFS in both Mixed SCC and non-SCC component groups (P = 0.007, P = 0.048). SUVmax, smoking history, tumor size and mixed SCC component were influencing factors of OS in patients (P = 0.040, P = 0.041, P = 0.046, P = 0.029). Multivariate survival analysis confirmed that TNM stage (HR = 2.885, 95%CI: 1.323–6.289, P = 0.008) was the most significantly influential factor for PFS. High SUVmax value (HR = 9.338, 95%CI: 2.426–35.938, P = 0.001) and mixed SCC component (HR = 0.155, 95%CI: 0.045–0.530, P = 0.003) were poor predictors for OS. Conclusion: Surgical-resected c-SCLCs have a relatively good prognosis. TNM stage is the most significant factor influencing disease progression in surgical-resected c-SCLCs. SUVmax and mixed NSCLC components within c-SCLCs had a considerable influence on the survival. Both high SUVmax and mixed SCC component are poor predictors for patients with c-SCLCs. [ABSTRACT FROM AUTHOR]

Robot-assisted technologies are overcoming the limitations of the current approaches for transoral surgeries, which are suffering from limited vision and workspace. As a result, we develop a novel teleoperated parallel continuum robot with variable stiffness for collision avoidance. This paper focuses on the feasibility study on a cadaveric model for the robotic system as a first trial. We introduce the configuration of the robotic system, the description of the processes of the trial, including the setting of the robotic system, the test of stiffness, and the action of the manipulation. The contact force between the manipulators with different stiffness and the surrounding tissues and a series of surgical operations of the manipulator, including grasping, cutting, pushing, and pulling tissues under the master-slave control mode, were recorded and analyzed. Experimental results suggest that the typical surgical procedure on a cadaveric model was successfully performed. Moreover, the efficacy and feasibility of the developed robotic system are verified to satisfy the requirements of transoral robotic surgery (TORS). [ABSTRACT FROM AUTHOR]

Glioblastoma multiforme (GBM) is a very aggressive and infiltrative brain tumor with a high mortality rate. There are radiomic models with handcrafted features to estimate glioblastoma prognosis. In this work, we evaluate to what extent of combining genomic with radiomic features makes an impact on the prognosis of overall survival (OS) in patients with GBM. We apply a hypercolumn-based convolutional network to segment tumor regions from magnetic resonance images (MRI), extract radiomic features (geometric, shape, histogram), and fuse with gene expression profiling data to predict survival rate for each patient. Several state-of-the-art regression models such as linear regression, support vector machine, and neural network are exploited to conduct prognosis analysis. The Cancer Genome Atlas (TCGA) dataset of MRI and gene expression profiling is used in the study to observe the model performance in radiomic, genomic, and radiogenomic features. The results demonstrate that genomic data are correlated with the GBM OS prediction, and the radiogenomic model outperforms both radiomic and genomic models. We further illustrate the most significant genes, such as IL1B, KLHL4, ATP1A2, IQGAP2, and TMSL8, which contribute highly to prognosis analysis. [ABSTRACT FROM AUTHOR]

Decomposition strategy which employs predefined subproblem framework and reference vectors has significant contribution in multi-objective optimization, and it can enhance local convergence as well as global diversity. However, the fixed exploring directions sacrifice flexibility and adaptability; therefore, extra reference adaptations should be considered under different shapes of the Pareto front. In this paper, a population-based heuristic orientation generating approach is presented to build a dynamic decomposition. The novel approach replaces the exhaustive reference distribution with reduced and partial orientations clustered within potential areas and provides flexible and scalable instructions for better exploration. Numerical experiment results demonstrate that the proposed method is compatible with both regular Pareto fronts and irregular cases and maintains outperformance or competitive performance compared to some state-of-the-art multi-objective approaches and adaptive-based algorithms. Moreover, the novel strategy presents more independence on subproblem aggregations and provides an autonomous evolving branch in decomposition-based researches. [ABSTRACT FROM AUTHOR]

Minimally invasive surgical procedures often require needle insertion. For these procedures, efficacy greatly depends on precise needle placement. Many methods, such as optical tracking and electromagnetic tracking, have been applied to assist needle placement by tracking the real-time position information of the needle. Compared with the optical tracking method, electromagnetic tracking is more suitable for minimally invasive surgery since it has no requirement of line-of-sight. However, the devices needed for electromagnetic tracking are usually expensive, which will increase the cost of surgery. In this study, we presented a low-cost smartphone-based permanent magnet tracking method compatible with CT imaging and designed a 3D printed operation platform to assist with needle placement prior to needle insertion during minimally invasive surgery. The needle positioning accuracy of this method was tested in an open air test and a prostate phantom test in a CT environment. For these two tests, the average radial errors were 0.47 and 2.25 mm, respectively, and the standard deviations were 0.29 and 1.63, respectively. The materials and fabrication required for the presented method are inexpensive. Thus, many image-guided therapies may benefit from the presented method as a low-cost option for needle positioning prior to needle insertion. [ABSTRACT FROM AUTHOR]

Purpose: Ultrasound (US)-guided percutaneous kidney biopsy is a challenge for interventionists as US artefacts prevent accurate viewing of the biopsy needle tip. Automatic needle tracking and trajectory prediction can increase operator confidence in performing biopsies, reduce procedure time, minimize the risk of inadvertent biopsy bleedings, and enable future image-guided robotic procedures. Methods: In this paper, we propose a tracking-by-segmentation model with spatial and channel "Squeeze and Excitation" (scSE) for US needle detection and trajectory prediction. We adopt a light deep learning architecture (e.g. LinkNet) as our segmentation baseline network and integrate the scSE module to learn spatial information for better prediction. The proposed model is trained with the US images of anonymized kidney biopsy clips from 8 patients. The contour is obtained using the border-following algorithm and area calculated using Green formula. Trajectory prediction is made by extrapolating from the smallest bounding box that can capture the contour. Results: We train and test our model on a total of 996 images extracted from 102 short videos at a rate of 3 frames per second from each video. A set of 794 images is used for training and 202 images for testing. Our model has achieved IOU of 41.01%, dice accuracy of 56.65%, F1-score of 36.61%, and root-mean-square angle error of 13.3 ∘ . We are thus able to predict and extrapolate the trajectory of the biopsy needle with decent accuracy for interventionists to better perform biopsies. Conclusion: Our novel model combining LinkNet and scSE shows a promising result for kidney biopsy application, which implies potential to other similar ultrasound-guided biopsies that require needle tracking and trajectory prediction. [ABSTRACT FROM AUTHOR]

Sudden hearing loss can be treated noninvasively by administering drugs to the middle ear (≈1 ml) via the eustachian tube. The nasopharyngeal cavity requires high dexterity manipulation as it is restricted by the nasal vestibule, and precise drug delivery through the small cavity can allow previously unreachable areas to be reconsidered for localized delivery. Nitinol has shape memory capabilities and can be used for distal actuation accessed from small lumen and a tortuous path. The drug delivery device (DDD) is a soft and needle-sized (2 mm) and comprises of Nitinol, ribbon spring, and a drug delivery tube. By controlling the input voltage to the Nitinol, bending of the device at different angles could be achieved, and the ribbon spring works antagonistically to the Nitinol to revert to the initial position once deactivated. The actuation of the device and its corresponding bending are calculated in vitro and found to have a bending angle ranging between 36.2 and 66.8° for applied voltages of 1.2-2.0 V, with surface temperature of 45.6-154 °C. The DDD is able to actuate 200 cycles with ≈91-76% retention of bending performance, with a temperature increase of ≈8.5-9% when 1.2-2.0 V is applied. Addition of soft insulating material shows ≈34-62% reduction in the surface temperature in the first cycle and ≈37-59% over 200 cycles when actuated at 1.2-2.0 V. The active steering and navigation capabilities of the DDD are demonstrated in simulated environments (based on the eustachian tube dimensions of adult and infant). Preclinical testing in human cadavers is demonstrated and suggests the developed DDD controlled by varying the input voltages for bending, and mechanically varied drug delivery may be a feasible option for localized drug delivery in eustachian tube. Graphical abstract. [ABSTRACT FROM AUTHOR]

A bionic artificial device commonly integrates various distributed functional units to mimic the functions of biological sensory neural system, bringing intricate interconnections, complicated structure, and interference in signal transmission. Here we show an all-in-one bionic artificial nerve based on a separate electrical double-layers structure that integrates the functions of perception, recognition, and transmission. The bionic artificial nerve features flexibility, rapid response (<21 ms), high robustness, excellent durability (>10,000 tests), personalized cutability, and no energy consumption when no mechanical stimulation is being applied. The response signals are highly regionally differentiated for the mechanical stimulations, which enables the bionic artificial nerve to mimic the spatiotemporally dynamic logic of a biological neural network. Multifunctional touch interactions demonstrate the enormous potential of the bionic artificial nerve for human-machine hybrid perceptual enhancement. By incorporating the spatiotemporal resolution function and algorithmic analysis, we hope that bionic artificial nerves will promote further development of sophisticated neuroprosthetics and intelligent robotics. Designing artificial sensory neural system for neuroprosthetics and smart robotics applications remains a challenge. Here, the authors demonstrate an all-in-one bionic sensory transmission nerve capable integrating the functions of perception, recognition and transmission of mechano-sensitive signal. [ABSTRACT FROM AUTHOR]

Robots in orthopedic surgery have been developed rapidly for decades and bring significant benefits to the patients and healthcare providers. However, robotics in fracture reduction remains at the infant stage. As essential components of the current robotic system, external fixators were used in fracture reduction, including the unilateral and Ilizarov-like ring fixators. With emerging of the industrial robots and mechanical arms, their sterilized variants were developed as the serial robots, including the traction device and robotic arm, for fracture reduction. Besides, parallel robots (e.g., Gough–Stewart platform) were devised for lower extremity traction and fracture reduction. After combining the advantages of the serial and parallel mechanisms, hybrid robots can fulfill specific clinical requirements (e.g., the joint fracture, including multiple major fragments). Furthermore, with the aid of intra-operative navigation systems, fracture reduction can be performed under real-time guidance. The paper presents a comprehensive overview of the advancement of the robots in fracture reduction and evaluates research challenges and future perspectives, including ergonomic and economic issues, operation time, artificial realities and intelligence, and telesurgery. [ABSTRACT FROM AUTHOR]

Conventional needle-based approaches in intravitreal drug delivery carry needle-stick-injury risk and could scare patients (belonephobia). Alternatively, our group has explored the application of an electromagnetic needleless injector in this paper. This work aims to improve intravitreal drug delivery, which in the future could assist physicians with automation and benefit patients by providing a needleless approach. Electromagnetic needleless intravitreal injections lack quantification studies. We investigate the delivery properties of the needleless injector where the characterization can be used to refine the design parameters of the prototype in subsequent iterations. Experiments were performed to characterize the injectant delivered from the electromagnetic needleless injector. Penetration tests were conducted to observe the influences of various injection barriers and tissues. Ultrasonic imaging modality was explored for future applications of the prototype. The dispersion of the injectant was controllable where injection depth and distribution is dependent on the input voltage. The synthetic barriers highlighted significant energy losses for penetration (maximum velocity falls from 4.46 to 1.57 mm/s with a 0.1-mm barrier). The biological barriers were difficult to penetrate with the current prototype. Our results indicate that the current electromagnetic injector offers controllable dispersion (depth and distribution) correlated with input voltages, which should have increased injection power for use with biological tissue. Ultrasonic imaging modality produced velocity profiles comparable to the optical approach which is promising for future in vivo studies. The influences of injection barriers should be further investigated in in vivo experiments with ultrasonic imaging modalities. Graphical abstract . [ABSTRACT FROM AUTHOR]

Glioblastoma multiforme (GBM) are malignant brain tumors, associated with poor overall survival (OS). This study aims to predict OS of GBM patients (in days) using a regression framework and assess the impact of tumor shape features on OS prediction. Multi-channel MR image derived texture features, tumor shape, and volumetric features, and patient age were obtained for 163 GBM patients. In order to assess the impact of tumor shape features on OS prediction, two feature sets, with and without tumor shape features, were created. For the feature set with tumor shape features, the mean prediction error (MPE) was 14.6 days and its 95% confidence interval (CI) was 195.8 days. For the feature set excluding shape features, the MPE was 17.1 days and its 95% CI was observed to be 212.7 days. The coefficient of determination (R2) value obtained for the feature set with shape features was 0.92, while it was 0.90 for the feature set excluding shape features. Although marginal, inclusion of shape features improves OS prediction in GBM patients. The proposed OS prediction method using regression provides good accuracy and overcomes the limitations of GBM OS classification, like choosing data-derived or pre-decided thresholds to define the OS groups. Graphical abstract Two feature sets: with and without tumor shape features were extracted from T1-weighted contrast-enhanced, T2-weighted and FLAIR MRI. These feature sets were analyzed using the Mean Prediction Error (MPE) and its 95% Confidence Interval (CI) obtained from the Bland-Altman plot, along with the coefficient of determination (R2) value to assess the impact of tumor shape features on overall survival prediction of glioblastoma multiforme patients. [ABSTRACT FROM AUTHOR]

Transoral robotic surgery (TORS) allows for access to oropharyngeal regions in an effective and minimally invasive manner. However, safe TORS access to deep pharyngeal (such as hypopharynx) sites remains a great challenge for current surgical robotic systems. In this work, we introduce a novel continuum robot with an optimized flexible parallel mechanism, to meet stringent requirements imposed by TORS on size, workspace, flexibility, and compliance. The system is comprised of two parts, a guidance part and an execution part, and achieves 11 controllable degrees of freedom. The execution part of the robot, based on the optimized flexible parallel mechanism, is able to reach deep sites in the oropharynx and larynx with the assistance of the continuum guidance part. In addition to the mechanical design, extensive analysis and experiments were carried out. Kinematic models were derived and the reachable workspace of the robot was verified to cover the entire target surgical area. Experimental results indicate that the robot achieves significantly enhanced compliance. Additionally, the designed robot can withstand a load of 1.5 N within the allowable range of the deflection. The positioning errors caused by the interference between different mechanisms can be effectively eliminated using the proposed compensation method. The maximum displacement error of this system under various conditions is less than 2 mm and the maximum bending error is less than 7.5°, which are satisfied for TORS. Cadaver trials were conducted to further demonstrate the feasibility. The reduced setup time and the reduced time to access the target site indicate that the developed surgical robotic system can achieve better operative efficiency in TORS when compared with current systems. [ABSTRACT FROM AUTHOR]

In this paper, the high precision control problem is investigated for the nonlinear discrete system with modeling uncertainties. A new method based on nonlinear U model is proposed for sliding mode control, then based on which a new sliding mode observer is proposed firstly and a new adaptive reaching law is designed. The convergence of the designed sliding mode observer and the reach ability of the designed adaptive reaching law is proved respectively, then based on which and the Lyapunov's stability theory the stability of the control system is proved. The observation error problem is also discussed, it is shown that the proposed sliding mode observer has a smaller observation error than the traditional method. Finally, two numerical example is given to illustrate the feasibility and superiority of the proposed design scheme. [ABSTRACT FROM AUTHOR]

Low-intensity therapeutic ultrasound has demonstrated an impetus in bone signaling and tissue healing for decades now. Though this technology is clinically well proven, still there are breaches in studies to understand the fundamental principle of how osteoblast tissue regenerates physiologically at the cellular level with ultrasound interaction as a form of acoustic wave stimuli. Through this article, we illustrate an analysis for cytomechanical changes of cell membrane periphery as a basic first physical principle for facilitating late downstream biochemical pathways. With the help of in situ single-cell direct analysis in a microfluidic confinement, we demonstrate that alteration of low-intensity pulse ultrasound (LIPUS) frequency would physically perturb cell membrane and establish inherent cell oscillation. We experimentally demonstrate here that, at LIPUS resonance near 1.7 MHz (during 1-3 MHz alteration), cell membrane area would expand to 6.85 ± 0.7% during ultrasound exposure while it contracts 44.68 ± 0.8% in post actuation. Conversely, cell cross-sectional area change (%) from its previous morphology during and after switching off LIPUS was reversibly different before and after resonance. For instance, at 1.5 MHz, LIPUS exposure produced 1.44 ± 0.5% expansion while in contrast 2 MHz instigates 1.6 ± 0.3% contraction. We conclude that alteration of LIPUS frequency from 1-3 MHz keeping other ultrasound parameters like exposure time, pulse repetition frequency (PRF), etc., constant, if applied to a microconfined biological single living cell, would perturb physical structure reversibly based on the system resonance during and post exposure ultrasound pulsing. We envision, in the near future, our results would constitute the foundation of mechanistic effects of low-intensity therapeutic ultrasound and its allied potential in medical applications. Graphical Abstract Frequency Dependent Characterization of Area Strain in Cell Membrane by Microfluidic Based Single Cell Analysis. [ABSTRACT FROM AUTHOR]

There is an increasing demand for reliable motor imagery (MI) classification algorithms for applications in consumer level brain-computer interfacing (BCI). For the practical use, such algorithms must be robust to both device limitations and subject variability, which make MI classification a challenging task. This study proposes methods to study the effect of limitations including a limited number of electrodes, limited spatial distribution of electrodes, lower signal quality, subject variabilities and BCI literacy, on the performance of MI classification. To mitigate these limitations, we propose a machine learning approach, WaveCSP that uses 24 features extracted from EEG signals using wavelet transform and common spatial pattern (CSP) filtering techniques. The algorithm shows better performance in terms of subject variability compared to existing work. The application of WaveCSP to Physionet MI database shows more than 50% of the 109 subjects achieving accuracy higher than 64%. The data obtained from a commercial EEG headset using the same experimental protocol result in up to four out of five subjects who had prior BCI experience (out of a total of 25 subjects) performing with accuracy higher than 64%. [ABSTRACT FROM AUTHOR]

Magnetic resonance within the field of wireless power transfer has seen an increase in popularity over the past decades. This rise can be attributed to the technological advances of electronics and the increased efficiency of popular battery technologies. The same principles of electromagnetic theory can be applied to the medical field. Several medical devices intended for use inside the body use batteries and electrical circuits that could be powered wirelessly. Other medical devices limit the mobility or make patients uncomfortable while in use. The fundamental theory of electromagnetics can improve the field by solving some of these problems. This survey paper summarizes the recent uses and discoveries of wireless power in the medical field. A comprehensive search for papers was conducted using engineering search engines and included papers from related conferences. During the initial search, 247 papers were found then non-relevant papers were eliminated to leave only suitable material. Seventeen relevant journal papers and/or conference papers were found, then separated into defined categories: Implants, Pumps, Ultrasound Imaging, and Gastrointestinal (GI) Endoscopy. The approach and methods for each paper were analyzed and compared yielding a comprehensive review of these state of the art technologies. [ABSTRACT FROM AUTHOR]

Robotic systems play an increasingly important role in improving feasibility and effectiveness of minimally invasive neurosurgery (MIN). However, large footprint, bulky size, and complex mechanisms limit the clinical application of existing robotic neurosurgery solutions. This paper proposes a novel skull-mounted robot with a compact and lightweight parallel mechanism for positioning of surgical tools in MIN. The system serves as a mechanical guide for automatic positioning of needles, catheters, probes, or electrodes. A parallel mechanism with 4 degrees of freedom (DOFs) is adopted, with the aim of providing sufficient accuracy and load capacity. The volume of the robot is only 50 mm × 50 mm × 40 mm and the weight is 73 g. The miniature design allows the robot to be mounted on the skull easily without consuming space in the operating room while avoiding the patient’s immobilization, simplifying the registration operation, and increasing patient comfort and tolerability. The mechanical design, kinematics and workspace are analyzed and described in detail. Three experiments on the prototype are conducted to test the stiffness, accuracy and performance. Results show that the deflection is less than 0.1 mm for holding common surgical tools and the tracking errors are less than 1.2 mm and 1.9° which is acceptable for MIN. The robot can be easily and firmly mounted on the skull model and cadaver head, and flexibly manipulated on the skull model. [ABSTRACT FROM AUTHOR]

We investigate a new framework of surgical instrument detection based on power-varying ultrasound images with simple and efficient pixel-wise intensity processing. Without using complicated feature extraction methods, we identified the instrument with an estimated optimal power level and by comparing pixel values of varying transducer power level images. The proposed framework exploits the physics of ultrasound imaging system by varying the transducer power level to effectively distinguish metallic surgical instruments from tissue. This power-varying image-guidance is motivated from our observations that ultrasound imaging at different power levels exhibit different contrast enhancement capabilities between tissue and instruments in ultrasound-guided robotic beating-heart surgery. Using lower transducer power levels (ranging from 40 to 75% of the rated lowest ultrasound power levels of the two tested ultrasound scanners) can effectively suppress the strong imaging artifacts from metallic instruments and thus, can be utilized together with the images from normal transducer power levels to enhance the separability between instrument and tissue, improving intraoperative instrument tracking accuracy from the acquired noisy ultrasound volumetric images. We performed experiments in phantoms and ex vivo hearts in water tank environments. The proposed multi-level power-varying ultrasound imaging approach can identify robotic instruments of high acoustic impedance from low-signal-to-noise-ratio ultrasound images by power adjustments. [ABSTRACT FROM AUTHOR]

Purpose: Origami-based biomedical device design is an emerging technology due to its ability to be deployed from a minimal foldable pattern to a larger volume. This paper aims to review state-of-the-art origami structures applied in the medical device field. Methods: Publications and reports of origami structure related to medical device design from the past 10 years are reviewed and categorized according to engineering specifications, including the application field, fabrication material, size/volume, deployment method, manufacturability, and advantages. Results: This paper presents an overview of the biomedical applications of devices based on origami structures, including disposable sterilization covers, cardiac catheterization, stent grafts, encapsulation and microsurgery, gastrointestinal microsurgery, laparoscopic surgical grippers, microgrippers, microfluidic devices, and drug delivery. Challenges in terms of materials and fabrication, assembly, modeling and computation design, and clinical adoptability are discussed at the end of this paper to provide guidance for future origami-based design in the medical device field. Conclusion: Concepts from origami can be used to design and develop novel medical devices. Origami-based medical device design is currently progressing, with researchers improving design methods, materials, fabrication techniques, and folding efficiency. [ABSTRACT FROM AUTHOR]

Traditional posterior nasopharyngeal biopsy using a flexible nasal endoscope has the risks of abrasion and injury to the nasal mucosa and thus causing trauma to the patient. Recently, a new class of robots known as continuum tubular robots (CTRs) provide a novel solution to the challenge with miniaturized size, curvilinear maneuverability, and capability of avoiding collision within the nasal environment. This paper presents a compact CTR which is 35 cm in total length, 10 cm in diameter, 2.15 kg in weight, and easy to be integrated with a robotic arm to perform more complicated operations. Structural design, end-effector design, and workspace analysis are described in detail. In addition, teleoperation of the CTR using a haptic input device is developed for position control in 3D space. Moreover, by integrating the robot with three electromagnetic tracking sensors, a navigation system together with a shape reconstruction algorithm is developed. Comprehensive experiments are conducted to test the functionality of the proposed prototype; experiment results show that under teleoperation, the system has an accuracy of 2.20 mm in following a linear path, an accuracy of 2.01 mm in following a circular path, and a latency time of 0.1 s. It is also found that the proposed shape reconstruction algorithm has a mean error of around 1 mm along the length of the tubes. Besides, the feasibility and effectiveness of the proposed robotic system being applied to posterior nasopharyngeal biopsy are demonstrated by a cadaver experiment. The proposed robotic system holds promise to enhance clinical operation in transnasal procedures. [ABSTRACT FROM AUTHOR]

Cognitive robotic systems nowadays are intensively involving learning algorithms to achieve highly adaptive and intelligent behaviors, including actuation, sensing, perception and adaptive control. Deep learning has emerged as an effective approach in image-based robotic perception and actions. Towards cognitive robotic perception based on deep learning, this paper focuses the Constrained Restricted Boltzmann Machine (RBM) on visual images for sparse feature representation. Inspired by sparse coding, the sparse constraints are performed on the hidden layer of RBM to obtain sparse and effective feature representation from perceived visual images. The RBM with Sparse Constraint (RBMSC) is proposed with a generalized optimization problem, where the constraints are applied on the probability density of hidden units directly to obtain more sparse representation. This paper presents three novel RBM variants, namely L-RBM, L-RBM, and L-RBM constrained by L-norm, L-norm, and L-norm on RBM, respectively. A Deep Belief Network with two hidden layers is built for comparison between each RBM variants. The experiments on MNIST database (Mixed National Institute of Standards and Technology database) show that the L-RBM can obtain more sparse representation than RBM, L-RBM, L-RBM, and Sparse-RBM (SRBM) in terms of sparseness metric. For further verification, the proposed methods are still tested on MNIST Variations dataset. The recognition results from perceived images in MNIST and MNIST Variations demonstrate that our proposed constrained RBM variants are feasible for object cognitive and perception, and the proposed L-RBM and L-RBM outperforms RBM and SRBM in terms of object recognition. [ABSTRACT FROM AUTHOR]

As an essential factor of evolutionary algorithms (EAs), mutation operator plays an important role in exploring the search space, maintaining the diversity of individuals and breaking away local optimums. In most standard evolutionary algorithms, the mutation operator is independent from the recombination operator. Nevertheless, in biogeography-based optimization (BBO), the mutation operator is affected not only by predefined constants but also by recombination models, namely the migration operator. However to date, the relationship between the mutation and migration has never been investigated. To reveal the relationship and evaluate the mutation models, we utilize drift analysis to investigate the expected first hitting time of BBO with different migration models. The analysis compares three different kinds of mutation models in a mathematical way and the conclusion is helpful for designing migration models of BBO. The simulation results are also in agreement with our analysis. [ABSTRACT FROM AUTHOR]

Coordinate Alignment (CA) is an important problem in hybrid tracking systems involving two or more tracking devices. CA typically associates the measurements from two or more tracking systems with respect to distinct base frames and makes them comparable in the same aligned coordinate system. In this article, we discuss a sub-problem, Paired-Orientation Alignment (POA), in the category of CA. This sub-problem occurs during the development of an integrated electromagnetic and inertial attitude (orientation) tracking system, where only the orientation information is acquired from the two tracking devices. The problem is modeled as a matrix equation YC = D with constraints, which can be solved as a least-squares problem using quaternions. A closed-form analytical solution is given by the pseudo-inverse matrix. This method is specifically for registering the paired-orientation measurements between two coordinate systems, without using position information. The algorithm is illustrated by simulations and proof-of-concept tracking experiments. [ABSTRACT FROM AUTHOR]

In the paper, a novel power-splitting scheme based on two dimensional photonic crystal (2D PhC) is proposed. The structure can be divided into three sections, including the input waveguide, coupling region, and output region, and the latter two sections consist of two parallel waveguides placed in proximity. The operation principle of the splitter is that only one of the super-modes splitting from the directional coupler can propagate through coupling region in the frequency range of interest. The radius of air holes next to the guiding region in coupling region is increased to avoid the acute back reflection at the entrance to the input waveguide induced by the modes mismatch between the input waveguide and coupling region. While in output region, the radius of these corresponding air holes is also increased so that the two splitting super-modes have same propagation constants to avoid the coupling between the two output waveguides. Moreover, as the length of coupling region is varied, its influence on the splitting performance is discussed, and it is verified that the relationship between the splitter length and bandwidth has a trade-off. [ABSTRACT FROM AUTHOR]

Wearable dry electrodes are needed for long-term biopotential recordings but are limited by their imperfect compliance with the skin, especially during body movements and sweat secretions, resulting in high interfacial impedance and motion artifacts. Herein, we report an intrinsically conductive polymer dry electrode with excellent self-adhesiveness, stretchability, and conductivity. It shows much lower skin-contact impedance and noise in static and dynamic measurement than the current dry electrodes and standard gel electrodes, enabling to acquire high-quality electrocardiogram (ECG), electromyogram (EMG) and electroencephalogram (EEG) signals in various conditions such as dry and wet skin and during body movement. Hence, this dry electrode can be used for long-term healthcare monitoring in complex daily conditions. We further investigated the capabilities of this electrode in a clinical setting and realized its ability to detect the arrhythmia features of atrial fibrillation accurately, and quantify muscle activity during deep tendon reflex testing and contraction against resistance. Reported wearable dry electrodes have limited long-term use due to their imperfect skin compliance and high motion artifacts. Here, the authors report an intrinsically conductive, stretchable polymer dry electrode with excellent self-adhesiveness for long-term high-quality biopotential detection. [ABSTRACT FROM AUTHOR]