J Shanghai Jiaotong Univ Sci

Advances in Medicine-Engineering Crossover in Automated Anesthesia

XU Tianyi (徐天意), XIA Ming (夏明), JIANG Hong (姜虹)

2022, 27 (2): 137-143.
doi: 10.1007/s12204-021-2329-x

Abstract ( 325 )

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Medicine-engineering crossover refers to the cross-fertilization of multiple disciplines to meet clinical needs through various means, including engineering, which greatly promotes medical development. In the development of anesthesiology, improvements in anesthesia equipment and continuous innovation of anesthesia technology are all closely related to the integration of medicine and engineering. In recent years, the exploration and development of automated anesthesia equipment has led to closer integration of medicine, engineering, and other disciplines, including the development of robots in anesthesia, automated monitoring and alarm technology,automated perioperative management, and remote anesthesia. Herein, the current status of applications and development of medicine-engineering crossover in the field of automated anesthesia are discussed.

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Application of Digital Medicine in Addiction

WU Xiaojun (吴萧俊), DU Jiang (杜江), JIANG Haifeng (江海峰), ZHAO Min (赵敏)

2022, 27 (2): 144-152.
doi: 10.1007/s12204-021-2391-4

Abstract ( 311 )

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Digital medicine plays an important role in disease assessment, psychological intervention, and relapse management in mental illnesses. Patients with substance use disorders can be easily affected by the environment and negative emotions, inducing addiction and relapse. However, due to social discrimination, stigma, or economic issues, they are unwilling to go to the hospital for treatment, making it difficult for health workers to track their health changes. Additionally, mental health resources in China are insufficient. Digital medicine aims to solve these problems. This article reviews digital medicine in the field of addiction, hoping to provide a reference for the future exploration of more individualized and effective digital medicine.

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Risk Prediction Model of Gallbladder Disease in Shanghai Middle-Aged and Elderly People Based on Neural Networks

YUAN Xiaoqi (袁筱祺), ZHU Lelan (朱乐兰), XU Qiongfan(徐琼凡), GAO Wei (高玮)

2022, 27 (2): 153-159.
doi: 10.1007/s12204-021-2386-1

Abstract ( 307 )

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This paper discusses the risk factors related to gallbladder disease in Shanghai, improves the accuracy of risk prediction, and provides a theoretical basis for scientific diagnosis and universality of gallbladder disease.We selected 3 462 data of middle-aged and elderly health check-up patients in a general hospital in Shanghai,and divided into gallbladder disease group according to color doppler ultrasound diagnosis results. Single-factor analysis screened out 8 important risk factors, which were used as an analysis variable of multi-layer perceptron neural network and binary logistic regression to construct the prediction model of gallbladder disease. The prediction accuracy of the multi-layer perceptron neural network risk prediction model is 76%. The area under the receiver operating characteristic curve (AUC) is 0.82, the maximum Youden index is 0.44, the sensitivity is 79.51, and the specificity is 64.23. The prediction accuracy of the multi-layer perceptron neural network model is better than that of the binary logistic regression prediction model. The overall prediction accuracy of the binary logistic regression prediction model is 75.60%, the AUC is 0.81, the maximum Youden index is 0.42, the sensitivity is 74.48, and the specificity is 57.60. In the objective risk prediction of gallbladder disease in middle-aged and elderly people in Shanghai, the risk prediction model based on the multi-layer perceptron neural network has a better prediction performance than the binary logistic regression model, which provides a theoretical basis for preventive treatment and intervention.

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Spontaneous Language Analysis in Alzheimer’s Disease:Evaluation of Natural Language Processing Technique for Analyzing Lexical Performance

LIU Ning (刘宁), YUAN Zhenming* (袁贞明)

2022, 27 (2): 160-167.
doi: 10.1007/s12204-021-2384-3

Abstract ( 248 )

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Language disorder, a common manifestation of Alzheimer’s disease (AD), has attracted widespread attention in recent years. This paper uses a novel natural language processing (NLP) method, compared with latest deep learning technology, to detect AD and explore the lexical performance. Our proposed approach is based on two stages. First, the dialogue contents are summarized into two categories with the same category. Second,term frequency - inverse document frequency (TF-IDF) algorithm is used to extract the keywords of transcripts,and the similarity of keywords between the groups was calculated separately by cosine distance. Several deep learning methods are used to compare the performance. In the meanwhile, keywords with the best performance are used to analyze AD patients’ lexical performance. In the Predictive Challenge of Alzheimer’s Disease held by iFlytek in 2019, the proposed AD diagnosis model achieves a better performance in binary classification by adjusting the number of keywords. The F1 score of the model has a considerable improvement over the baseline of 75.4%, and the training process of which is simple and efficient. We analyze the keywords of the model and find that AD patients use less noun and verb than normal controls. A computer-assisted AD diagnosis model on small Chinese dataset is proposed in this paper, which provides a potential way for assisting diagnosis of AD and analyzing lexical performance in clinical setting.

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KDLPCCA-Based Projection for Feature Extraction in SSVEP-Based Brain-Computer Interfaces

HUANG Jiayang (黄嘉阳), YANG Pengfei* (杨鹏飞), WAN Bo (万波), ZHANG Zhiqiang (张志强)

2022, 27 (2): 168-175.
doi: 10.1007/s12204-021-2387-0

Abstract ( 224 )

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An electroencephalogram (EEG) signal projection using kernel discriminative locality preserving canonical correlation analysis (KDLPCCA)-based correlation with steady-state visual evoked potential (SSVEP) templates for frequency recognition is presented in this paper. With KDLPCCA, not only a non-linear correlation but also local properties and discriminative information of each class sample are considered to extract temporal and frequency features of SSVEP signals. The new projected EEG features are classified with classical machine learning algorithms, namely, K-nearest neighbors (KNNs), naive Bayes, and random forest classifiers. To demonstrate the effectiveness of the proposed method, 16-channel SSVEP data corresponding to 4 frequencies collected from 5 subjects were used to evaluate the performance. Compared with the state of the art canonical correlation analysis (CCA), experimental results show significant improvements in classification accuracy and information transfer rate (ITR), achieving 100% and 240 bits/min with 0.5 s sample block. The superior performance demonstrates that this method holds the promising potential to achieve satisfactory performance for high-accuracy SSVEP-based brain-computer interfaces.

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SeRN: A Two-Stage Framework of Registration for Semi-Supervised Learning for Medical Images

JIA Dengqiang* (贾灯强), LUO Xinzhe (罗鑫喆), DING Wangbin (丁王斌),HUANG Liqin (黄立勤), ZHUANG Xiahai (庄吓海)

2022, 27 (2): 176-189.
doi: 10.1007/s12204-021-2383-4

Abstract ( 253 )

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Significant breakthroughs in medical image registration have been achieved using deep neural networks (DNNs). However, DNN-based end-to-end registration methods often require large quantities of data or adequate annotations for training. To leverage the intensity information of abundant unlabeled images, unsupervised registration methods commonly employ intensity-based similarity measures to optimize the network parameters.However, finding a sufficiently robust measure can be challenging for specific registration applications. Weakly supervised registration methods use anatomical labels to estimate the deformation between images. High-level structural information in label images is more reliable and practical for estimating the voxel correspondence of anatomic regions of interest between images, whereas label images are extremely difficult to collect. In this paper, we propose a two-stage semi-supervised learning framework for medical image registration, which consists of unsupervised and weakly supervised registration networks. The proposed semi-supervised learning framework is trained with intensity information from available images, label information from a relatively small number of labeled images and pseudo-label information from unlabeled images. Experimental results on two datasets (cardiac and abdominal images) demonstrate the efficacy and efficiency of this method in intra- and inter-modality medical image registrations, as well as its superior performance when a vast amount of unlabeled data and a small set of annotations are available. Our code is publicly available at https://github.com/jdq818/SeRN.

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Interactive Liver Segmentation Algorithm Based on Geodesic Distance and V-Net

KANG Jie* (亢洁), DING Jumin (丁菊敏), LEI Tao (雷涛),FENG Shujie (冯树杰), LIU Gang (刘港)

2022, 27 (2): 190-201.
doi: 10.1007/s12204-021-2379-0

Abstract ( 255 )

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Convolutional neural networks (CNNs) are prone to mis-segmenting image data of the liver when the background is complicated, which results in low segmentation accuracy and unsuitable results for clinical use. To address this shortcoming, an interactive liver segmentation algorithm based on geodesic distance and V-net is proposed. The three-dimensional segmentation network V-net adequately considers the characteristics of the spatial context information to segment liver medical images and obtain preliminary segmentation results. An artificial algorithm based on geodesic distance is used to form artificial hard constraints to modify the image,and the superpixel piece created by the watershed algorithm is introduced as a sample point for operation, which significantly improves the efficiency of segmentation. Results from simulation of the liver tumor segmentation challenge (LiTS) dataset show that this algorithm can effectively refine the results of automatic liver segmentation,reduce user intervention, and enable a fast, interactive liver image segmentation that is convenient for doctors.

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Instruction Cues Increase Brain Network Complexity During Movement Preparation

WANG Ning (王宁), ZHANG Lipeng (张利朋), ZHANG Rui (张锐), MA Liuyang(马留洋),NIU Deyuan(牛得源), ZHANG Yankun (张彦昆), ZHAO Hui (赵辉), HU Yuxia* (胡玉霞)

2022, 27 (2): 202-210.
doi: 10.1007/s12204-021-2342-0

Abstract ( 226 )

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Instruction cues are widely employed for research on neural mechanisms during movement preparation.However, their influence on brain connectivity during movement has not received much attention. Herein, 15 healthy subjects completed two experimental tasks including either instructed or voluntary movements; meanwhile electroencephalogram (EEG) data were synchronously recorded. Based on source analysis and related literature,six movement-related brain regions were selected, including the left/right supplementary motor area (SMA),left/right inferior frontal gyrus (iFg), and left/right postcentral gyrus (pCg). After assuming 10 a priori models of regional brain connectivity, we evaluated the optimal connectivity model between brain regions for the two scenarios using the dynamic causality model (DCM). During voluntary movement, the movement originated in the SMA, passed through the iFg of the prefrontal lobe, and then returned to the main sensory cortex of the pCg. In the instructed movement, the movement originated in the iFg, and then was transmitted to the pCg and the SMA, as well as from the pCg to the SMA. In contrast to the preparation process of voluntary movement,there were long-range information interactions between the iFg and pCg. Further, almost the same brain regions were active during movement preparation under both voluntary and instructed movement tasks, which evidences certain similarities in dynamic brain connectivity, that is, the brain has direct connections between the bilateral SMA, bilateral pCg, and bilateral SMA, indicating that the both brain hemispheres work together during the movement preparation phase. The results suggest that the network during the preparation process of instructed movements is more complex than voluntary movements.

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Survey of EIT Image Reconstruction Algorithms

ZHANG Mingzhu(张明珠), MA Yixin* (马艺馨), HUANG Ningning (黄宁宁), GE Hao (葛浩)

2022, 27 (2): 211-218.
doi: 10.1007/s12204-021-2333-1

Abstract ( 193 )

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With the recent promotion of clinical applications of electrical impedance tomography (EIT) technology,more scholars have begun studying EIT technology. Although the principle of EIT technology seems simple,EIT image reconstruction is a non-linear and ill-posed problem that is quite difficult to solve because of its soft field characteristics and the inhomogeneous distribution of its sensitive field. What’s more, the EIT reconstruction algorithm requires further improvements in robustness, clarity, etc. The image-reconstruction algorithm and image quality are among the key challenges in the application of EIT technology; thus, more research is urgently needed to improve the performance of EIT technology and use it to solve a larger variety of clinical problems. In this paper, we pay special attention to the latest advances in the study of EIT image-reconstruction algorithms to provide a convenient reference for EIT beginners and researchers who are newly involved in research on EIT image reconstruction.

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Improvement of Physical Fitness Test Assessment Criteria Based on fNIRS Technology: Taking Pull-Up as an Example

GONG Bin(巩斌), YU Xianghua(禹香华), FANG Yu (方宇), WANG Zheng (王正), YANG Hao (杨皓), CHEN Guodong (陈国栋), L Ü Na (吕娜)

2022, 27 (2): 219-225.
doi: 10.1007/s12204-021-2367-4

Abstract ( 266 )

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Pull-up, as an important physical fitness test event of the “National Student Physical Health Standard”, is known as a difficult physical fitness test event. To improve the assessment criteria of pull-ups, this paper uses the functional near-infrared spectroscopy (fNIRS) to monitor the changes and activation of oxyhemoglobin (HbO) signals in the brain motor cortex of people with different body mass indexes (BMIs) during the pullup assessment. Then the relationship between BMIs and evaluation criteria is discussed. After collecting and analyzing experimental data of 18 recruited college students, it is found that the number of pull-ups performed by people with different BMIs is different when they reach the peak state of brain activation. The results of the study indicate that different assessment criteria should be adopted for different BMI groups. It is suggested that the BMI should be introduced as one of the test indexes in the examination of pull-ups event in “National Student Physical Health Standard”.

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Evaluation Value of Intravoxel Incoherent Motion Diffusion-Weighted Imaging on Early Efficacy of Magnetic Resonance-Guided High-Intensity Focused Ultrasound Ablation for Uterine Adenomyoma

TANG Na (唐纳), GU Jianjun (顾坚骏), YIN Xiaorui (尹肖睿), YU Rongjiang (虞容江),XU Yuantao (徐元涛), LI Xiang (李想), WANG Han* (王悍)

2022, 27 (2): 226-230.
doi: 10.1007/s12204-022-2405-x

Abstract ( 245 )

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To investigate the evaluation value of intravoxel incoherent motion diffusion-weighted imaging (IVIMDWI) on the early efficacy of magnetic resonance-guided high-intensity focused ultrasound (MRgFUS) ablation for uterine adenomyoma. The clinical and magnetic resonance imaging (MRI) data of 36 patients with uterine adenomyoma before and after MRgFUS treatment in our hospital from January 2018 to December 2018 were retrospectively analyzed. All the 36 patients underwent MRI examination one day before operation and immediately after operation using GE Discovery MR750 3.0T MRI, including conventional sequences (T1WI, T2WI,and T2 fat suppression sequences) plain scan, IVIM-DWI sequences with 9 b values, and contrast enhanced-MRI sequences. The IVIM-DWI quantitative parameters (true diffusion coefficient D, perfusion related diffusion coefficient D?, and perfusion fraction f) of double-exponential model were obtained by using GE ADW 4.7 functool,a postprocessor. SPSS 24.0 software was used to analyze the difference in parameter between the ablation and non-ablation areas of uterine adenomyoma. DWI signal in the ablation area of uterine adenomyoma was increased,and manifested as heterogeneous diffuse high signal, with low central signal and high edge signal. Values of D, D? and f in the ablation area of uterine adenomyoma were significantly lower than those in the non-ablation area,and there was statistical difference between the two (P <0.05). The areas under receiver operating characteristic (ROC) curve of D, D? and f values in the ablation area of uterine adenomyoma were 0.854, 0.898 and 0.924,respectively; the optimal thresholds for the diagnosis of ablation area of uterine adenomyoma were 0.81 × 10 ?3 mm2/s, 4.99×10 ?3 mm2/s and 0.24, respectively; the diagnostic sensitivity was 80.6%, 72.2% and 94.4%, respectively; and the specificity was 91.7%, 97.2% and 94.4%, respectively. IVIM-DWI has a certain clinical value in the evaluation on early efficacy of MRgFUS ablation of uterine adenomyosis.

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Cable-Driven Flexible Exoskeleton Robot for Abnormal Gait Rehabilitation

XU Ziwei (徐子薇), XIE Le* (谢叻)

2022, 27 (2): 231-239.
doi: 10.1007/s12204-021-2403-4

Abstract ( 224 )

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The number of people with abnormal gait in China has been increasing for years. Compared with traditional methods, lower limb rehabilitation robots which address problems such as longstanding human guidance may cause fatigue, and the training is lacking scientific and intuitive monitoring data. However, typical rigid rehabilitation robots are always meeting drawbacks like the enormous weight, the limitation of joint movement,and low comfort. The purpose of this research is to design a cable-driven flexible exoskeleton robot to assist in rehabilitation training of patients who have abnormal gait due to low-level hemiplegia or senility. The system consists of a PC terminal, a Raspberry Pi, and the actuator structure. Monitoring and training are realized through remote operation and interactive interface simultaneously. We designed an integrated and miniaturized driving control box. Inside the box, two driving cables on customized pulley-blocks with different radii can retract/release by one motor after transmitting the target position to the Raspberry Pi from the PC. The force could be transferred to the flexible suit to aid hip flexion and ankle plantar flexion. Furthermore, the passive elastic structure was intended to assist ankle dorsiflexion. We also adopted the predictable admittance controller,which uses the Prophet algorithm to predict the changes in the next five gait cycles from the current ankle angular velocity and obtain the ideal force curve through a functional relationship. The admittance controller can realize the desired force following. Finally, we finished the performance test and the human-subject experiment.Experimental data indicate that the exoskeleton can meet the basic demand of multi-joint assistance and improve abnormal postures. Meanwhile, it can increase the range of joint rotation and eliminate asymmetrical during walking.

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Topology Optimization Method for Calcaneal Prosthesis

LIU Xiaoying* (刘晓颖), YUE Yong(岳勇), WANG Chongning (王宠宁), HUANG Jiazan (黄家赞),HUANG Xianwei(黄贤伟), HAO Yanhua(郝艳华)

2022, 27 (2): 240-249.
doi: 10.1007/s12204-021-2324-2

Abstract ( 295 )

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With the development of economy and the progress of medical science and technology, artificial prosthesis replacement has become an important means to improve the dysfunction caused by human bone diseases.However, there are still some loose phenomena caused by stress shielding. To solve the complications of aseptic loosening after calcaneal prosthesis replacement, an optimal design method for the prosthesis was proposed. The prosthesis was designed and optimized according to the real bone shape and the replacement requirements by the combination of computed tomography (CT) technology, computer-aided design, finite element analysis, and power flow theory. CT data were imported into MIMICS and Geomagic Studios. UG was used to obtain the geometric model of the human skeleton. Then, the 3D finite element model of the prosthesis was established by combining the finite element software Abaqus, and a series of finite element analysis was carried out. The prosthesis was topologically optimized and filled with a porous structure. The prosthesis was implanted by computer simulation.Finally, the power flow method was used to compare the dynamic performance and energy transfer before and after the prosthesis replacement to verify the rationality of the prosthesis design. In this paper, this method was used to optimize the design of the calcaneal prosthesis, and the research shows that this method can reduce the stress shielding effect of the calcaneal prosthesis. From the case of calcaneal prosthesis optimization, this method is not only a supplement to the contemporary biomechanical theory but also can guide the design of bone prosthesis in bone prosthesis replacement surgery.

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A 12-bit 80 MS/s 2 mW SAR ADC with Deliberated Digital Calibration and Redundancy Schemes for Medical Imaging

HAN Gang* (韩刚), WU Bin (吴斌), PU Yilin (蒲钇霖)

2022, 27 (2): 250-255.
doi: 10.1007/s12204-021-2377-2

Abstract ( 288 )

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In this article, we presented a 12-bit 80MS/s low power successive approximation register (SAR)analog to digital converter (ADC) design. A simplified but effective digital calibration scheme was exploited to make the ADC achieve high resolution without sacrificing more silicon area and power efficiency. A modified redundancy technique was also adopted to guarantee the feasibility of the calibration and meantime ease the burden of the reference buffer circuit. The prototype SAR ADC can work up to a sampling rate of 80MS/s with the performance of > 10.5 bit equivalent number of bits (ENOB), < ±1 least significant bit (LSB) differential nonlinearity (DNL) & integrated nonlinearity (INL), while only consuming less than 2mA current from a 1.1V power supply. The calculated figure of merit (FoM) is 17.4 fJ/conversion-step. This makes it a practical and competitive choice for the applications where high dynamic range and low power are simultaneously required,such as portable medical imaging.

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Evaluation of a Novel Multimodal Guidance Device for Difficult Airway Endotracheal Intubation in Spontaneously Breathing Pigs

XIA Ming (夏明), XU Tianyi (徐天意), CAO Shuang (曹爽),ZHOU Ren (周韧), JIANG Hong* (姜虹)

2022, 27 (2): 256-263.
doi: 10.1007/s12204-021-2330-4

Abstract ( 258 )

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End-expiratory carbon dioxide concentrations can be used to assist endotracheal intubation. The novel multimodal endotracheal intubation guidance device combined visualization with an end-expiratory carbon dioxide concentration vectorization algorithm to achieve more accurate placement in difficult airways. The feasibility of a novel multimodal guidance device for the endotracheal intubation of difficult airways was verified in spontaneously breathing Bama miniature pigs. The glottic exposure time, insertion time, and total intubation time were not significantly different between the fiberoptic bronchoscope group and the multimodal guidance device group in regard to the endotracheal intubation of difficult airways. There were also no significant differences in intubation attempts, first success rate, and total success rate. Animals in both groups experienced hypoxemia, hypotension,and esophageal intubation during endotracheal intubation, but there were also no significant differences in the incidence of adverse events between the two devices. The effect on changes in hemodynamics, heart rate, and oxygen saturation during intubation showed no significant difference between the two devices. The results of the present study demonstrated the feasibility and effectiveness of the initial prototype of a multimodal guidance device for the endotracheal intubation of difficult airways in pigs, which is expected to further assist in adequately positioning the airway during difficult endotracheal intubations with spontaneous breathing.

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Macrophage Polarization in Skin Wound Healing: Progress in Biology and Therapeutics

CHEN Lu (陈露), CHENG Liying (程丽英), CHEN Tian (陈田), ZHANG Yuguang (张余光), ZHANG Jianming* (张建明)

2022, 27 (2): 264-280.
doi: 10.1007/s12204-021-2276-6

Abstract ( 208 )

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The morphological and functional barriers caused by pathological scars are extremely painful for patients. Up to now, pathological scar poses a big unmet medical challenge for plastic surgeons and dermatologists worldwide. Macrophage polarization has shown a non-negligible effect on wound healing and scar formation. However, the role of macrophages in wound healing and pathological scar formation is still controversial. To summarize the latest data on probing biological functions of macrophage polarization in wound healing and scar formation and target macrophages in wound healing, we particularly paid attention to studies on different groups of macrophages, the transition among those groups, and modulators regulating the transition process. A comprehensive understanding of macrophage polarization in wound healing is certain to facilitate the development of new and efficient therapeutic modalities for pathological scar.

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