Design, Optimization, and Modeling of a Hydraulic Soft Robot for Chronic Total Occlusions.

Chronic total occlusion (CTO) is one of the most severe and sophisticated vascular stenosis because of complete blockage, greater operation difficulty, and lower procedural success rate. This study proposes a hydraulic-driven soft robot imitating the earthworm's locomotion to assist doctors or operators in actively opening thrombi in coronary or peripheral artery vessels. Firstly, a three-actuator bionic soft robot is developed based on earthworms' physiological structure. The soft robot's locomotion gait inspired by the earthworm's mechanism is designed. Secondly, the influence of structure parameters on actuator deformation, stress, and strain is explored, which can help us determine the soft actuators' optimal structure parameters. Thirdly, the relationship between hydraulic pressure and actuator deformation is investigated by performing finite element analysis using the bidirectional fluid-structure interaction (FSI) method. The kinematic models of the soft actuators are established to provide a valuable reference for the soft actuators' motion control.

High-resolution feature based central venous catheter tip detection network in X-ray images.

Hospital patients can have catheters and lines inserted during the course of their admission to give medicines for the treatment of medical issues, especially the central venous catheter (CVC). However, malposition of CVC will lead to many complications, even death. Clinicians always detect the malposition based on position detection of CVC tip via X-ray images. To reduce the workload of the clinicians and the percentage of malposition occurrence, we propose an automatic catheter tip detection framework based on a convolutional neural network (CNN). The proposed framework contains three essential components which are modified HRNet, segmentation supervision module, and deconvolution module. The modified HRNet can retain high-resolution features from start to end, ensuring the maintenance of precise information from the X-ray images. The segmentation supervision module can alleviate the presence of other line-like structures such as the skeleton as well as other tubes and catheters used for treatment. In addition, the deconvolution module can further increase the feature resolution on the top of the highest-resolution feature maps in the modified HRNet to get a higher-resolution heatmap of the catheter tip. A public CVC Dataset is utilized to evaluate the performance of the proposed framework. The results show that the proposed algorithm offering a mean Pixel Error of 4.11 outperforms three comparative methods (Ma's method, SRPE method, and LCM method). It is demonstrated to be a promising solution to precisely detect the tip position of the catheter in X-ray images.

A Novel Spatial Position Prediction Navigation System Makes Surgery More Accurate.

During intravascular interventional surgery, the 3D surgical navigation system can provide doctors with 3D spatial information of the vascular lumen, reducing the impact of missing dimension caused by digital subtraction angiography (DSA) guidance and further improving the success rate of surgeries. Nevertheless, this task often comes with the challenge of complex registration problems due to vessel deformation caused by respiratory motion and high requirements for the surgical environment because of the dependence on external electromagnetic sensors. This article proposes a novel 3D spatial predictive positioning navigation (SPPN) technique to predict the real-time tip position of surgical instruments. In the first stage, we propose a trajectory prediction algorithm integrated with instrumental morphological constraints to generate the initial trajectory. Then, a novel hybrid physical model is designed to estimate the trajectory's energy and mechanics. In the second stage, a point cloud clustering algorithm applies multi-information fusion to generate the maximum probability endpoint cloud. Then, an energy-weighted probability density function is introduced using statistical analysis to achieve the prediction of the 3D spatial location of instrument endpoints. Extensive experiments are conducted on 3D-printed human artery and vein models based on a high-precision electromagnetic tracking system. Experimental results demonstrate the outstanding performance of our method, reaching 98.2% of the achievement ratio and less than 3 mm of the average positioning accuracy. This work is the first 3D surgical navigation algorithm that entirely relies on vascular interventional robot sensors, effectively improving the accuracy of interventional surgery and making it more accessible for primary surgeons.

Learning Skill Characteristics From Manipulations.

Percutaneous coronary intervention (PCI) has increasingly become the main treatment for coronary artery disease. The procedure requires high experienced skills and dexterous manipulations. However, there are few techniques to model PCI skill so far. In this study, a learning framework with local and ensemble learning is proposed to learn skill characteristics of different skill-level subjects from their PCI manipulations. Ten interventional cardiologists (four experts and six novices) were recruited to deliver a medical guidewire to two target arteries on a porcine model for in vivo studies. Simultaneously, translation and twist manipulations of thumb, forefinger, and wrist are acquired with electromagnetic (EM) and fiber-optic bend (FOB) sensors, respectively. These behavior data are then processed with wavelet packet decomposition (WPD) under 1-10 levels for feature extraction. The feature vectors are further fed into three candidate individual classifiers in the local learning layer. Furthermore, the local learning results from different manipulation behaviors are fused in the ensemble learning layer with three rule-based ensemble learning algorithms. In subject-dependent skill characteristics learning, the ensemble learning can achieve 100% accuracy, significantly outperforming the best local result (90%). Furthermore, ensemble learning can also maintain 73% accuracy in subject-independent schemes. These promising results demonstrate the great potential of the proposed method to facilitate skill learning in surgical robotics and skill assessment in clinical practice.

To Explore the Mechanism of "Fuzi-Guizhi" for the Treatment of Osteoarthritis on the Basis of Network Pharmacology and Molecular Docking.

OBJECTIVE: The objective of this study is to analyze and verify the main drug components and targets of "Fuzi-Guizhi" in the treatment of osteoarthritis by using the network pharmacology platform. METHODS: The integrated pharmacology of "Fuzi-Guizhi" was analyzed by using the platform of integrated pharmacology of traditional Chinese medicine to explore its mechanism in the treatment of osteoarthritis. By establishing an arthritis model in vitro, the pharmacological effect of "aconitecassia twigs" on articular cartilage was evaluated and conducted for molecular docking. RESULTS: 28 candidate active components, 37 compound targets, and 583 osteoarthritis-related potential targets were screened, and 10 key target processes were screened in the protein interaction network model. Enrichment analysis showed that the 10 core targets involved 958 GO biologic function items and 76 KEGG signal pathways, which were mainly related to apoptosis and mitochondrial functional metabolism and "Fuzi-Guizhi" drug-containing serum inhibited the expression of Caspase-3 mRNA and protein in chondrocytes and promoted the synthesis of ATP. CONCLUSION: Our research is preliminary that the mechanism of action of "Fuzi-Guizhi" may inhibit chondrocyte degeneration by resisting mitochondrial apoptosis, and further experimental research is required to determine.

A Multilayer and Multimodal-Fusion Architecture for Simultaneous Recognition of Endovascular Manipulations and Assessment of Technical Skills.

The clinical success of the percutaneous coronary intervention (PCI) is highly dependent on endovascular manipulation skills and dexterous manipulation strategies of interventionalists. However, the analysis of endovascular manipulations and related discussion for technical skill assessment are limited. In this study, a multilayer and multimodal-fusion architecture is proposed to recognize six typical endovascular manipulations. The synchronously acquired multimodal motion signals from ten subjects are used as the inputs of the architecture independently. Six classification-based and two rule-based fusion algorithms are evaluated for performance comparisons. The recognition metrics under the determined architecture are further used to assess technical skills. The experimental results indicate that the proposed architecture can achieve the overall accuracy of 96.41%, much higher than that of a single-layer recognition architecture (92.85%). In addition, the multimodal fusion brings significant performance improvement in comparison with single-modal schemes. Furthermore, the K -means-based skill assessment can obtain an accuracy of 95% to cluster the attempts made by different skill-level groups. These hopeful results indicate the great possibility of the architecture to facilitate clinical skill assessment and skill learning.

A Unified Framework for Multi-Guidewire Endpoint Localization in Fluoroscopy Images.

OBJECTIVE: In this paper, Keypoint Localization Region-based CNN (KL R-CNN) is proposed, which can simultaneously accomplish the guidewire detection and endpoint localization in a unified model. METHODS: KL R-CNN modifies Mask R-CNN by replacing the mask branch with a novel keypoint localization branch. Besides, some settings of Mask R-CNN are also modified to generate the keypoint localization results at a higher detail level. At the same time, based on the existing metrics of Average Precision (AP) and Percentage of Correct Keypoints (PCK), a new metric named APPCK is proposed to evaluate the overall performance on the multi-guidewire endpoint localization task. Compared with existing metrics, APPCK is easy to use and its results are more intuitive. RESULTS: Compared with existing methods, KL R-CNN has better performance when the threshold is loose, reaching a mean APPCK of 90.65% when the threshold is 9 pixels. CONCLUSION: KL R-CNN achieves the state-of-the-art performance on the multi-guidewire endpoint localization task and has application potentials. SIGNIFICANCE: KL R-CNN can achieve the localization of guidewire endpoints in fluoroscopy images, which is a prerequisite for computer-assisted percutaneous coronary intervention. KL R-CNN can also be extended to other multi-instrument localization tasks.

Reliability and validity of the Pittsburgh Sleep Quality Index among frontline COVID-19 health care workers using classical test theory and item response theory.

STUDY OBJECTIVES: The applicability of sleep-related scales to frontline medical staff for the COVID-19 pandemic has not been fully proved, so sleep survey results lack credibility and accuracy, creating difficulties for the guidance and treatment of frontline medical staff with sleep disorders, which is not conducive to the prevention and control of COVID-19. This study sought to analyze the reliability and validity of the Pittsburgh Sleep Quality Index (PSQI) among frontline medical staff fighting the COVID-19 pandemic. METHODS: A network questionnaire survey was used to investigate the PSQI among frontline medical staff who fought COVID-19 in Wuhan, China from March 19 to April 15, 2020. Combined with classical test theory and item response theory, the content validity, internal consistency, construct validity, and other aspects of the PSQI were evaluated. RESULTS: According to classical test theory, content validity, criterion validity, and construct validity of the PSQI were good. But the internal consistency was better after the deletion of the "daytime dysfunction" subscale. With regard to item response theory, difficulty, the differential item function, and the Wright map performed well. CONCLUSIONS: The original PSQI showed acceptable applicability in frontline COVID-19 medical staff, and its characteristics moderately improved after the "daytime dysfunction" subscale was removed. CITATION: Wang L, Wu Y-X, Lin Y-Q, et al. Reliability and validity of the Pittsburgh Sleep Quality Index among frontline COVID-19 health care workers using classical test theory and item response theory. J Clin Sleep Med. 2022;18(2):541-551.

Machine Learning for Structure Determination in Single-Particle Cryo-Electron Microscopy: A Systematic Review.

Recently, single-particle cryo-electron microscopy (cryo-EM) has become an indispensable method for determining macromolecular structures at high resolution to deeply explore the relevant molecular mechanism. Its recent breakthrough is mainly because of the rapid advances in hardware and image processing algorithms, especially machine learning. As an essential support of single-particle cryo-EM, machine learning has powered many aspects of structure determination and greatly promoted its development. In this article, we provide a systematic review of the applications of machine learning in this field. Our review begins with a brief introduction of single-particle cryo-EM, followed by the specific tasks and challenges of its image processing. Then, focusing on the workflow of structure determination, we describe relevant machine learning algorithms and applications at different steps, including particle picking, 2-D clustering, 3-D reconstruction, and other steps. As different tasks exhibit distinct characteristics, we introduce the evaluation metrics for each task and summarize their dynamics of technology development. Finally, we discuss the open issues and potential trends in this promising field.

An Improved Networked Predictive Controller for Vascular Robot Using 5G Networks.

Percutaneous coronary intervention (PCI) has gradually become the most common treatment of coronary artery disease (CAD) in clinical practice due to its advantages of small trauma and quick recovery. However, the availability of hospitals with cardiac catheterization facilities and trained interventionalists is extremely limited in remote and underdeveloped areas. Remote vascular robotic system can assist interventionalists to complete operations precisely, and reduce occupational health hazards occurrence. In this paper, a bionic remote vascular robot is introduced in detail from three parts: mechanism, communication architecture, and controller model. Firstly, human finger-like mechanisms in vascular robot enable the interventionalists to advance, retract and rotate the guidewires or balloons. Secondly, a 5G-based communication system is built to satisfy the end-to-end requirements of strong data transmission and packet priority setting in remote robot control. Thirdly, a generalized predictive controller (GPC) is developed to suppress the effect of time-varying network delay and parameter identification error, while adding a designed polynomial compensation module to reduce tracking error and improve system responsiveness. Then, the simulation experiment verifies the system performance in comparison with different algorithms, network delay, and packet loss rate. Finally, the improved control system conducted PCI on an experimental pig, which reduced the delivery integral absolute error (IAE) by at least 20% compared with traditional methods.

Design and Performance Evaluation of a Novel Vascular Robotic System for Complex Percutaneous Coronary Interventions.

The robotic-assisted percutaneous coronary intervention is an emerging technology with great potential to solve the shortcomings of existing treatments. However, the current robotic systems can not manipulate two guidewires or ballons/stents simultaneously for coronary bifurcation lesions. This paper presents VasCure, a novel bio-inspired vascular robotic system, to deliver two guidewires and stents into the main branch and side branch of bifurcation lesions in sequence. The system is designed in master-slave architecture to reduce occupational hazards of radiation exposure and orthopedic injury to interventional surgeons. The slave delivery device has one active roller and two passive rollers to manipulate two interventional devices. The performance of the VasCure was verified by in vitro and in vivo animal experiments. In vitro results showed the robotic system has good accuracy to deliver guidewires and the maximum error is 0.38mm. In an animal experiment, the interventional surgeon delivered two guidewires and balloons to the left circumflex branch and the left anterior descending branch of the pig, which confirmed the feasibility of the vascular robotic system.

Real-Time Multi-Guidewire Endpoint Localization in Fluoroscopy Images.

The real-time localization of the guidewire endpoints is a stepping stone to computer-assisted percutaneous coronary intervention (PCI). However, methods for multi-guidewire endpoint localization in fluoroscopy images are still scarce. In this paper, we introduce a framework for real-time multi-guidewire endpoint localization in fluoroscopy images. The framework consists of two stages, first detecting all guidewire instances in the fluoroscopy image, and then locating the endpoints of each single guidewire instance. In the first stage, a YOLOv3 detector is used for guidewire detection, and a post-processing algorithm is proposed to refine the guidewire detection results. In the second stage, a Segmentation Attention-hourglass (SA-hourglass) network is proposed to predict the endpoint locations of each single guidewire instance. The SA-hourglass network can be generalized to the keypoint localization of other surgical instruments. In our experiments, the SA-hourglass network is applied not only on a guidewire dataset but also on a retinal microsurgery dataset, reaching the mean pixel error (MPE) of 2.20 pixels on the guidewire dataset and the MPE of 5.30 pixels on the retinal microsurgery dataset, both achieving the state-of-the-art localization results. Besides, the inference rate of our framework is at least 20FPS, which meets the real-time requirement of fluoroscopy images (6-12FPS).

Pyramid attention recurrent networks for real-time guidewire segmentation and tracking in intraoperative X-ray fluoroscopy.

In endovascular and cardiovascular surgery, real-time and accurate segmentation and tracking of interventional instruments can aid in reducing radiation exposure, contrast agent and processing time. Nevertheless, this task often comes with the challenges of the elongated deformable structures with low contrast in noisy X-ray fluoroscopy. To address these issues, a novel efficient network architecture, termed pyramid attention recurrent networks (PAR-Net), is proposed for real-time guidewire segmentation and tracking. The proposed PAR-Net contains three major modules, namely pyramid attention module, recurrent residual module and pre-trained MobileNetV2 encoder. Specifically, a hybrid loss function of both reinforced focal loss and dice loss is proposed to better address the issues of class imbalance and misclassified examples. Quantitative and qualitative evaluations on clinical intraoperative images demonstrate that the proposed approach significantly outperforms simpler baselines as well as the best previously published result for this task, achieving the state-of-the-art performance.

Differential expression of AURKA/PLK4 in quiescence and senescence of osteosarcoma U2OS cells.

This study aimed to identify co-expressed differentially expressed genes (DEGs) in quiescence and senescence of osteosarcoma (OS) U2OS cells and investigate their biological functions. GSE94805 from Gene Expression Omnibus database was extracted, involving 12 samples of OS U2OS cells (4 quiescence, 4 senescence, and 4 control samples). After analysis of DEGs by limma package, VENN analysis was performed to identify co-expressed DEGs in quiescence and senescent. The Cytoscape software was used to construct an interactive network of co-expressed DEGs. Finally, box-plot was drawn for the co-expressed DEGs in sub-network. Besides, the relation literatures were selected in GenCLiP database for the co-expressed DEGs. Seven hundred and forty-three DEGs (255 up-regulated genes, 488 down-regulated genes) were obtained in quiescence and 2135 DEGs (1189 up-regulated genes, 946 down-regulated genes) in senescence. Through VENN analysis, 448 DEGs (131 up-regulated genes, 317 down-regulated genes) were co-expressed in quiescent and senescence. In the co-expressed DEGs network, 896 nodes (448 nodes in quiescent, 448 nodes in senescent) were obtained. Finally, 16 co-expressed DEGs were obtained in the sub-network analysis, in which Aurora kinase A (AURKA) and polo-like kinase (PLK4) had been reported in OS. AURKA and PLK4 might be the key genes in quiescence and senescence of OS U2OS cells.

Reduced Sleep Duration and Sleep Efficiency Were Independently Associated With Frequent Nightmares in Chinese Frontline Medical Workers During the Coronavirus Disease 2019 Outbreak.

OBJECTIVES: Nightmares were related to emotion and behavioral problems and also emerged as one of the core features of post-traumatic stress disorder (PTSD). Our study aimed to investigate the associations of frequent nightmares with sleep duration and sleep efficiency among frontline medical workers in Wuhan during the coronavirus disease 2019 (COVID-19) outbreak. METHODS: A total of 528 health-care workers from the province of Fujian providing medical aid in Wuhan completed the online questionnaires. There were 114 doctors and 414 nurses. The age, sex, marital status, and work situation were recorded. A battery of scales including the Pittsburgh Sleep Quality Index (PSQI) and the 12-item General Health Questionnaire (GHQ-12) were used to evaluate subjects' sleep and general mental health. Frequent nightmares were defined as the response of at least once a week in the item of "nightmare" of PSQI. RESULTS: Frequent nightmares were found in 27.3% of subjects. The frequent nightmare group had a higher score of PSQI-sleep duration and PSQI-habitual sleep efficiency (frequent nightmares vs. non-frequent nightmares: PSQI-sleep duration, 1.08 ± 0.97 vs. 0.74 ± 0.85, P < 0.001; PSQI-habitual sleep efficiency, 1.08 ± 1.10 vs. 0.62 ± 0.88, P < 0.001). Reduced sleep duration and reduced sleep efficiency were independently associated with frequent nightmares after adjustment for age, sex, poor mental health, and regular sleeping medication use (reduced sleep duration: OR = 1.96, 95% CI = 1.07-3.58, P = 0.029; reduced sleep efficiency: OR = 2.17, 95% CI = 1.09-4.32, P = 0.027). Subjects with both reduced sleep duration and sleep efficiency were also associated with frequent nightmares (OR = 2.70, 95% CI = 1.57-4.65, P < 0.001). CONCLUSION: The present study found that sleep duration and sleep efficiency were both independently associated with frequent nightmares among frontline medical workers in Wuhan during the COVID-19 pandemic. We should pay attention to nightmares and even the ensuing PTSD symptoms among subjects with reduced sleep duration or sleep efficiency facing potential traumatic exposure.

Qualitative and Quantitative Assessment of Technical Skills in Percutaneous Coronary Intervention: In Vivo Porcine Studies.

OBJECTIVE: Technical skill assessment plays an important role in the professional development of an interventionalist in percutaneous coronary intervention (PCI). However, most of the traditional assessment methods are time consuming and subjective. This paper aims to develop objective assessment techniques. METHODS: In this study, a natural-behavior-based assessment framework is proposed to qualitatively and quantitatively assess technical skills in PCI. In vivo porcine studies were conducted to deliver a medical guidewire to two target coronaries of left circumflex arteries by six novice and four expert interventionalists. Simultaneously, four types of natural behaviors (i.e., hand motion, proximal force, muscle activity, and finger motion) were acquired from the subjects' dominant hand and arm. The features extracted from the behaviors of different skill-level groups were compared using the Mann-Whitney U-test for effective behavior selection. The effective ones were further applied in the Gaussian-mixture-model-based qualitative assessment and Mahalanobis-distance-based quantitative assessment. RESULTS: The qualitative assessment achieves an accuracy of 92% to distinguish the novice and expert attempts, which is significantly higher than that of using single guidewire motions. Furthermore, the quantitative assessment can assign objective and effective scores for all attempts, indicating high correlation ( R = 0.9225) to those obtained by traditional methods. CONCLUSION: The objective, effective, and comprehensive assessment of technical skills can be provided by qualitatively and quantitatively analyzing interventionalists' natural behaviors in PCI. SIGNIFICANCE: This paper suggests a novel approach for the technical skill assessment and the promising results demonstrate the great importance and effectiveness of the proposed method for promoting the development of objective assessment techniques.

Analysis of Interventionalists' Natural Behaviors for Recognizing Motion Patterns of Endovascular Tools During Percutaneous Coronary Interventions.

Many robotic platforms can indeed reduce radiation exposure to clinicians during percutaneous coronary intervention (PCI), however, interventionalists' natural manipulations are rarely involved in robot-assisted PCI. This requires more attention to analyze interventionalists' natural behaviors during conventional PCI. In this study, four types of natural behavior (i.e., muscle activity, hand motion, proximal force, and finger motion) were synchronously acquired from ten subjects while performing six typical types of guidewire manipulation. These behaviors are evaluated by a hidden Markov model (HMM) based analysis framework for relevant behavior selection. Relevant behaviors are further used as the input of two HMM-based classification frameworks to recognize guidewire motion patterns. Experimental results show that under the basic classification framework (BCF), 91.01% and 93.32% recognition accuracies can be achieved by using all behaviors and relevant behaviors, respectively. Furthermore, the hierarchical classification framework can significantly enhance the recognition ability of relevant behaviors with an accuracy of 96.39%. These promising results demonstrate great potential of proposed methods for promoting the future design of human-robot interfaces in robot-assisted PCI.

RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation Network.

Segmentation for tracking surgical instruments plays an important role in robot-assisted surgery. Segmentation of surgical instruments contributes to capturing accurate spatial information for tracking. In this paper, a novel network, Refined Attention Segmentation Network, is proposed to simultaneously segment surgical instruments and identify their categories. The U-shape network which is popular in segmentation is used. Different from previous work, an attention module is adopted to help the network focus on key regions, which can improve the segmentation accuracy. To solve the class imbalance problem, the weighted sum of the cross entropy loss and the logarithm of the Jaccard index is used as loss function. Furthermore, transfer learning is adopted in our network. The encoder is pre-trained on ImageNet. The dataset from the MICCAI EndoVis Challenge 2017 is used to evaluate our network. Based on this dataset, our network achieves state-of-the-art performance 94.65% mean Dice and 90.33% mean IOU.

Recognition of Endovascular Manipulations using Recurrent Neural Networks.

The ability to accurately recognize elementary surgical gestures is a stepping stone to automated surgical assessment and surgical training. In this paper, a long short-term memory (LSTM) recurrent neural network is applied to the task of recognizing six typical manipulations in percutaneous coronary intervention (PCI). The manipulation mentioned above is referring to the atomic surgical operation, also called surgeme in many research. Instead of using the video data or kinematic data of surgical instruments, we propose to use the kinematic data of the operator's hand acquired by our wearable data glove to recognize the manipulations. To establish a baseline for comparison, a method based on Hidden Markov Model (HMM) is applied because HMM is frequently used in the tasks of surgical sequence learning. Two cross-validation schemes are used in our experiments, they both illustrate that our LSTM-based method far outperforms the HMM-based method. To our knowledge, this is the first paper to apply the LSTM recurrent neural network in the field of PCI.

Preliminary study for motion scaling based control in minimally invasive vascular interventional robot.

Robot-assisted vascular interventions present promising trend for reducing the X-ray radiation to the surgeon during the operation. However, the control methods in the current vascular interventional robots only repeat the manipulation of the surgeon. While under certain circumstances, it is necessary to scale the manipulation of the surgeon to obtain a higher precision or a shorter manipulation time. A novel control method based on motion scaling for vascular interventional robot is proposed in this paper. The main idea of the method is to change the motion speed ratios between the master and the slave side. The motion scaling based control method is implemented in the vascular interventional robot we've developed before, so the operator can deliver the interventional devices under different motion scaling factors. Experiment studies verify the effectiveness of the motion scaling based control.