Table of Content

    24 January 2024, Volume 29 Issue 1 Previous Issue   

    Medicine-Engineering Interdisciplinary Research
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    Medicine-Engineering Interdisciplinary Research
    Review of Power-Assisted Lower Limb Exoskeleton Robot
    HE Guisong (贺贵松), HUANG Xuegong (黄学功), LI Feng (李峰), WANG Huixing (汪辉兴)
    2024, 29 (1):  1-15.  doi: 10.1007/s12204-022-2489-3
    Abstract ( 351 )   PDF (1195KB) ( 120 )  
    Power-assisted lower limb exoskeleton robot is a wearable intelligent robot system involving mechanics,materials, electronics, control, robotics, and many other fields. The system can use external energy to provide additional power to humans, enhance the function of the human body, and help the wearer to bear weight that is previously unbearable. At the same time, employing reasonable structure design and passive energy storage can also assist in specific actions. First, this paper introduces the research status of power-assisted lower limb exoskeleton robots at home and abroad, and analyzes several typical prototypes in detail. Then, the key technologies such as structure design, driving mode, sensing technology, control method, energy management, and human-machine coupling are summarized, and some common design methods of the exoskeleton robot are summarized and compared. Finally, the existing problems and possible solutions in the research of power-assisted lower limb exoskeleton robots are summarized, and the prospect of future development trend has been analyzed.
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    Review of Key Technologies for Developing Personalized Lower Limb Rehabilitative Exoskeleton Robots
    TAO Jing, (陶璟), ZHOU Zhenhuan (周振欢)
    2024, 29 (1):  16-28.  doi: 10.1007/s12204-022-2452-3
    Abstract ( 167 )   PDF (1179KB) ( 110 )  
    Rehabilitative training and assistance to daily living activities play critical roles in improving the life quality of lower limb dyskinesia patients and older people with motor function degeneration. Lower limb rehabilitative exoskeleton has a promising application prospect in support of the above population. In this paper, critical technologies for developing lower limb rehabilitative exoskeleton for individualized user needs are identi- fied and reviewed, including exoskeleton hardware modularization, bionic compliant driving, individualized gait planning and individual-oriented motion intention recognition. Inspired by the idea of servitization, potentials in exoskeleton product-service system design and its enabling technologies are then discussed. It is suggested that future research will focus on exoskeleton technology and exoskeleton-based service development oriented to an individual’s physical features and personalized requirements to realize better human-exoskeleton coordination in terms of technology, as well as accessible and high-quality rehabilitation and living assistance in terms of utility.
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    Time-Resolved Imaging in Short-Wave Infrared Region
    XU Yang (徐杨), LI Wanwan∗ (李万万)
    2024, 29 (1):  29-36.  doi: 10.1007/s12204-022-2547-x
    Abstract ( 79 )   PDF (810KB) ( 39 )  
    Compared with the conventional first near-infrared (NIR-I, 700—900 nm) window, the short-wave infrared region (SWIR, 900—1 700 nm) possesses the merits of the increasing tissue penetration depths and the suppression of scattering background, leading to great potential for in vivo imaging. Based on the limitations of the common spectral domain, and the superiority of the time-dimension, time-resolved imaging eliminates the auto-fluorescence in the biological tissue, thus supporting higher signal-to-noise ratio and sensitivities. The imaging technique is not affected by the difference in tissue composition or thickness and has the practical value of quantitative in vivo detection. Almost all the relevant time-resolved imaging was carried out around lanthanide-doped upconversion nanomaterials, owing to the advantages of ultralong luminescence lifetime, excellent photostability, controllable morphology, easy surface modification and various strategies of regulating lifetime. Therefore, this review presents the research progress of SWIR time-resolved imaging technology based on nanomaterials doped with lanthanide ions as luminescence centers in recent years.
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    Transfer Learning in Motor Imagery Brain Computer Interface: A Review
    LI Mingai1,2,3∗ (李明爱), XU Dongqin1 (许东芹)
    2024, 29 (1):  37-59.  doi: 10.1007/s12204-022-2488-4
    Abstract ( 100 )   PDF (1733KB) ( 32 )  
    Transfer learning, as a new machine learning methodology, may solve problems in related but different domains by using existing knowledge, and it is often applied to transfer training data from another domain for model training in the case of insufficient training data. In recent years, an increasing number of researchers who engage in brain-computer interface (BCI), have focused on using transfer learning to make most of the available electroencephalogram data from different subjects, effectively reducing the cost of expensive data acquisition and labeling as well as greatly improving the learning performance of the model. This paper surveys the development of transfer learning and reviews the transfer learning approaches in BCI. In addition, according to the “what to transfer” question in transfer learning, this review is organized into three contexts: instance-based transfer learning, parameter-based transfer learning, and feature-based transfer learning. Furthermore, the current transfer learning applications in BCI research are summarized in terms of the transfer learning methods, datasets, evaluation performance, etc. At the end of the paper, the questions to be solved in future research are put forward, laying the foundation for the popularization and in-depth research of transfer learning in BCI.
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    A Novel Cable-Driven Soft Robot for Surgery
    LI Ru1 (李茹), CHEN Fang2 (陈方), YU Wenwei3 (俞文伟), IGARASH Tatsuo3,4, SHU Xiongpeng1 (舒雄鹏), XIE Le1,5,6∗ (谢叻)
    2024, 29 (1):  60-72.  doi: 10.1007/s12204-022-2497-3
    Abstract ( 69 )   PDF (2938KB) ( 32 )  
    Robot-assisted laparoscopic radical prostatectomy (RARP) is widely used to treat prostate cancer. The rigid instruments primarily used in RARP cannot overcome the problem of blind areas in surgery and lead to more trauma such as more incision for the passage of the instrument and additional tissue damage caused by rigid instruments. Soft robots are relatively flexible and theoretically have infinite degrees of freedom which can overcome the problem of the rigid instrument. A soft robot system for single-port transvesical robot-assisted radical prostatectomy (STvRARP) is developed in this study. The soft manipulator with 10 mm in diameter and a maximum bending angle of 270? has good flexibility and dexterity. The design and mechanical structure of the soft robot are described. The kinematics of the soft manipulator is established and the inverse kinematics is compensated based on the characteristics of the designed soft manipulator. The master-slave control system of soft robot for surgery is built and the feasibility of the designed soft robot is verified.
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    Retinal Vessel Segmentation via Adversarial Learning and Iterative Refinement
    GU Wen (顾闻), XU Yi∗ (徐奕)
    2024, 29 (1):  73-80.  doi: 10.1007/s12204-022-2479-5
    Abstract ( 56 )   PDF (913KB) ( 30 )  
    Retinal vessel segmentation is a challenging medical task owing to small size of dataset, micro blood vessels and low image contrast. To address these issues, we introduce a novel convolutional neural network in this paper, which takes the advantage of both adversarial learning and recurrent neural network. An iterative design of network with recurrent unit is performed to refine the segmentation results from input retinal image gradually. Recurrent unit preserves high-level semantic information for feature reuse, so as to output a sufficiently refined segmentation map instead of a coarse mask. Moreover, an adversarial loss is imposing the integrity and connectivity constraints on the segmented vessel regions, thus greatly reducing topology errors of segmentation. The experimental results on the DRIVE dataset show that our method achieves area under curve and sensitivity of 98.17% and 80.64%, respectively. Our method achieves superior performance in retinal vessel segmentation compared with other existing state-of-the-art methods.
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    Unsupervised Oral Endoscope Image Stitching Algorithm
    HUANG Rong (黄荣), CHANG Qing (常青), ZHANG Yang (张扬)
    2024, 29 (1):  81-90.  doi: 10.1007/s12204-022-2513-7
    Abstract ( 43 )   PDF (5773KB) ( 25 )  
    Oral endoscope image stitching algorithm is studied to obtain wide-field oral images through registration and stitching, which is of great significance for auxiliary diagnosis. Compared with natural images, oral images have lower textures and fewer features. However, traditional feature-based image stitching methods rely heavily on feature extraction quality, often showing an unsatisfactory performance when stitching images with few features. Moreover, due to the hand-held shooting, there are large depth and perspective disparities between the captured images, which also pose a challenge to image stitching. To overcome the above problems, we propose an unsupervised oral endoscope image stitching algorithm based on the extraction of overlapping regions and the loss of deep features. In the registration stage, we extract the overlapping region of the input images by sketching polygon intersection for feature points screening and estimate homography from coarse to fine on a three-layer feature pyramid structure. Moreover, we calculate loss using deep features instead of pixel values to emphasize the importance of depth disparities in homography estimation. Finally, we reconstruct the stitched images from feature to pixel, which can eliminate artifacts caused by large parallax. Our method is compared with both feature-based and previous deep-based methods on the UDIS-D dataset and our oral endoscopy image dataset. The experimental results show that our algorithm can achieve higher homography estimation accuracy, and better visual quality, and can be effectively applied to oral endoscope image stitching.
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    Medical Image Encryption Based on Josephus Traversing and Hyperchaotic Lorenz System
    YANG Na (杨娜), ZHANG Shuxia (张淑霞), BAI Mudan (白牡丹), LI Shanshan (李珊珊)
    2024, 29 (1):  91-108.  doi: 10.1007/s12204-022-2555-x
    Abstract ( 43 )   PDF (8081KB) ( 33 )  
    This study proposes a new medical image encryption scheme based on Josephus traversing and hyperchaotic Lorenz system. First, a chaotic sequence is generated through hyperchaotic system. This hyperchaotic sequence is used in the scrambling and diffusion stages of the algorithm. Second, in the scrambling process, the image is initially confused by Josephus scrambling, and then the image is further confused by Arnold map. Finally, generated hyperchaos sequence and exclusive OR operation is used for the image to carry on the positive and reverse diffusion to change the pixel value of the image and further hide the effective information of the image. In addition, the information of the plaintext image is used to generate keys used in the algorithm, which increases the ability of resisting plaintext attack. Experimental results and security analysis show that the scheme can effectively hide plaintext image information according to the characteristics of medical images, and is resistant to common types of attacks. In addition, this scheme performs well in the experiments of robustness, which shows that the scheme can solve the problem of image damage in telemedicine. It has a positive significance for the future research.
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    Ensemble Attention Guided Multi-SEANet Trained with Curriculum Learning for Noninvasive Prediction of Gleason Grade Groups from MRI
    SHEN Ao1,2‡ (沈傲), HU Jisu 2,3‡ (胡冀苏), JIN Pengfei4 (金鹏飞), ZHOU Zhiyong2 (周志勇), QIAN Xusheng 2,3 (钱旭升), ZHENG Yi2 (郑毅), BAO Jie 4 (包婕), WANG Ximing4∗ (王希明), DAI Yakang1,2∗ (戴亚康)
    2024, 29 (1):  109-119.  doi: 10.1007/s12204-022-2502-x
    Abstract ( 45 )   PDF (1406KB) ( 17 )  
    The Gleason grade group (GG) is an important basis for assessing the malignancy of prostate cancer, but it requires invasive biopsy to obtain pathology. To noninvasively evaluate GG, an automatic prediction method is proposed based on multi-scale convolutional neural network of the ensemble attention module trained with curriculum learning. First, a lesion-attention map based on the image of the region of interest is proposed in combination with the bottleneck attention module to make the network more focus on the lesion area. Second, the feature pyramid network is combined to make the network better learn the multi-scale information of the lesion area. Finally, in the network training, a curriculum based on the consistency gap between the visual evaluation and the pathological grade is proposed, which further improves the prediction performance of the network. Experimental results show that the proposed method is better than the traditional network model in predicting GG performance. The quadratic weighted Kappa is 0.471 1 and the positive predictive value for predicting clinically significant cancer is 0.936 9.
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    Weighted Heterogeneous Graph-Based Incremental Automatic Disease Diagnosis Method
    TIAN Yuanyuan (田圆圆), JIN Yanrui (金衍瑞), LI Zhiyuan (李志远), LIU Jinlei (刘金磊), LIU Chengliang (刘成良)
    2024, 29 (1):  120-130.  doi: 10.1007/s12204-022-2537-z
    Abstract ( 47 )   PDF (1080KB) ( 21 )  
    The objective of this study is to construct a multi-department symptom-based automatic diagnosis model. However, it is difficult to establish a model to classify plenty of diseases and collect thousands of diseasesymptom datasets simultaneously. Inspired by the thought of “knowledge graph is model”, this study proposes to build an experience-infused knowledge model by continuously learning the experiential knowledge from data, and incrementally injecting it into the knowledge graph. Therefore, incremental learning and injection are used to solve the data collection problem, and the knowledge graph is modeled and containerized to solve the large-scale multi-classification problems. First, an entity linking method is designed and a heterogeneous knowledge graph is constructed by graph fusion. Then, an adaptive neural network model is constructed for each dataset, and the data is used for statistical initialization and model training. Finally, the weights and biases of the learned neural network model are updated to the knowledge graph. It is worth noting that for the incremental process, we consider both the data and class increments. We evaluate the diagnostic effectiveness of the model on the current dataset and the anti-forgetting ability on the historical dataset after class increment on three public datasets. Compared with the classical model, the proposed model improves the diagnostic accuracy of the three datasets by 5%, 2%, and 15% on average, respectively. Meanwhile, the model under incremental learning has a better ability to resist forgetting.
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    Prediction of Pediatric Sepsis Using a Deep Encoding Network with Cross Features
    CHEN Xiao1,2 (陈潇), ZHANG Rui1,2 (张瑞), TANG Xinyi1,2 (汤心溢), QIAN Juan3∗ (钱娟)
    2024, 29 (1):  131-140.  doi: 10.1007/s12204-022-2499-1
    Abstract ( 55 )   PDF (1152KB) ( 19 )  
    Sepsis poses a serious threat to health of children in pediatric intensive care unit. The mortality from pediatric sepsis can be effectively reduced through in-time diagnosis and therapeutic intervention. The bacilliculture detection method is too time-consuming to receive timely treatment. In this research, we propose a new framework: a deep encoding network with cross features (CF-DEN) that enables accurate early detection of sepsis. Cross features are automatically constructed via the gradient boosting decision tree and distilled into the deep encoding network (DEN) we designed. The DEN is aimed at learning sufficiently effective representation from clinical test data. Each layer of the DEN filtrates the features involved in computation at current layer via attention mechanism and outputs the current prediction which is additive layer by layer to obtain the embedding feature at last layer. The framework takes the advantage of tree-based method and neural network method to extract effective representation from small clinical dataset and obtain accurate prediction in order to prompt patient to get timely treatment. We evaluate the performance of the framework on the dataset collected from Shanghai Children’s Medical Center. Compared with common machine learning methods, our method achieves the increase on F1-score by 16.06% on the test set.
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    Psychological Impact of the 2022 Round COVID-19 Pandemic on China’s College Students
    HONG Dongyang1,3 (洪冬羊), WANG Jinxia2,3 (王金霞), ZHANG Hongyang2,3 (张虹洋), CAO Ziyang2,3 (曹紫阳), YAN Zijun 2,3 (晏紫君), ZOU Lin2,3∗ (邹琳)
    2024, 29 (1):  141-149.  doi: 10.1007/s12204-022-2557-8
    Abstract ( 48 )   PDF (193KB) ( 17 )  
    In response to the new round of COVID-19 outbreaks since March 2022, universities with high outbreak rates around the country have taken quarantine measures to contain the epidemic. Evidence from previous coronavirus outbreaks has shown that people under quarantine are at risk for mental health disorders. To better understand the impacts of this round of COVID-19 quarantine on domestic college students and their responses, we conducted a systematic survey to assess the stress and anxiety, and to evaluate effective measurements in this population. We searched relevant documents and literature, and designed a questionnaire from six aspects, including psychological status, epidemic situation, study, daily life, sports, and interpersonal communication, with 51 items in total. We sent the questionnaire on the Wenjuanxing Web platform, from April 2 to 8, 2022. We evaluated the mental status according to parts of the Generalized Anxiety Disorder-7 (GAD-7) and Depression Anxiety Stress Scales-21 (DASS-21), and investigated the influencing risk factors and countermeasures. Statistical analysis was performed by using the Chi-square test and multi-variable logistic regression. In total, 508 college respondents were recruited in our survey, and the pooled prevalence of mild anxiety (GAD score  5, or DASS-21 anxiety score 8) or stress (DASS-21 pressure score 14) caused by the new round of COVID-19 pandemic quarantine was 19.69% (100/508). The prevalence of the anxiety or stress in college students with COVID-19 quarantine between different genders, regions, and majors was not significantly different. Independent risk factors for the mild anxiety or stress of undergraduates by COVID-19 quarantine included learning efficiency or duration [OR = 1.36, 95%CI (1.14—1.62), P = 0.001], based on the combined analysis of Chi-square test analysis with multi-variable logistic regression analysis. Interestingly, the mental well-beings before COVID-19 epidemic quarantine [OR = 0.22, 95%CI (0.13—0.36), P < 0.000 1], more low-intensity exercise [OR = 0.36, 95%CI (0.15—0.87), P = 0.02, high-intensity exercise as reference], and good sleep quality [OR = 0.14, 95%CI (0.07—0.30), P < 0.000 1: OR = 0.42, 95%CI (0.30—0.59), P < 0.000 1] are protective factors for alleviating the quarantinecaused anxiety or stress in Chinese college students for this round of COVID-19 epidemic quarantine. During the round of COVID-19 epidemic quarantine in 2022, a small number of college students have mild anxiety, affected by decreased learning efficiency or duration, which could be mitigated with low-intensity exercise and good sleep quality.
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    Social Network Analysis of COVID-19 Research and the Changing International Collaboration Structure
    QIN Ye1 (秦野), CHEN Rongrong2∗ (陈蓉蓉)
    2024, 29 (1):  150-160.  doi: 10.1007/s12204-022-2558-7
    Abstract ( 59 )   PDF (3126KB) ( 17 )  
    Research in Information Science and interdisciplinary areas suggested the formation of a growing network of international research collaboration. The massive transmission of COVID-19 worldwide especially after the identification of the Omicron variant could fundamentally alter the factors shaping the network’s development. This study employs network analysis methods to analyze the structure of the COVID-19 research collaboration from 2020 to 2022, using two major academic publication databases and the VOSviewer software. A novel temporal view is added by examining the dynamic changes of the network, and a fractional counting method is adopted as methodological improvements to previous research. Analysis reveals that the COVID-19 research network structure has undergone substantial changes over time, as collaborating countries and regions form and re-form new clusters. Transformations in the network can be partly explained by key developments in the pandemic and other social-political events. China as one of the largest pivots in the network formed a relatively distinct cluster, with potential to develop a larger Asia-Pacific collaboration cluster based on its research impact.
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