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28 January 2025, Volume 30 Issue 1 Previous Issue   
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Medicine-Engineering Interdisciplinary


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Medicine-Engineering Interdisciplinary

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Video-Based Detection of Epileptic Spasms in IESS: Modeling, Detection, and Evaluation DING Lihui1，2(丁黎辉), FU Lijun1，3* (付立军), YANG Guang4(杨光), WAN Lin4，5 (万林), CHANG Zhijun7(常志军) 2025, 30 (1):  1-9.  doi: 10.1007/s12204-024-2789-x Abstract ( 158 )   PDF (711KB) ( 27 )   Behavioral scoring based on clinical observations remains the gold standard for screening, diagnosing,and evaluating infantile epileptic spasm syndrome (IESS). The accurate identification of seizures is crucial for clinical diagnosis and assessment. In this study, we propose an innovative seizure detection method based on video feature recognition of patient spasms. To capture the temporal characteristics of the spasm behavior presented in the videos effectively, we incorporate asymmetric convolution and convolution–batch normalization–ReLU (CBR) modules. Specifically within the 3D-ResNet residual blocks, we split the larger convolutional kernels into two asymmetric 3D convolutional kernels. These kernels are connected in series to enhance the ability of the convolutional layers to extract key local features, both horizontally and vertically. In addition, we introduce a 3D convolutional block attention module to enhance the spatial correlations between video frame channels efficiently. To improve the generalization ability, we design a composite loss function that combines cross-entropy loss with triplet loss to balance the classification and similarity requirements. We train and evaluate our method using the PLA IESS-VIDEO dataset, achieving an average seizure recognition accuracy of 90.59%, precision of 90.94%, and recall of 87.64%. To validate its generalization capability further, we conducted external validation using six different patient monitoring videos compared with assessments by six human experts from various medical centers. The final test results demonstrate that our method achieved a recall of 0.647 6, surpassing the average level achieved by human experts (0.559 5), while attaining a high F1-score of 0.721 9. These findings have substantial significance for the long-term assessment of patients with IESS. References | Related Articles | Metrics

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Augmented Reality Navigation Using Surgical Guides Versus Conventional Techniques in Pedicle Screw Placement KONG Huiyang1 (孔会扬),WANG Shuyi1* (王殊轶),ZHANG Can2 (张璨),CHEN Zan2，3 (陈赞) 2025, 30 (1):  10-17.  doi: 10.1007/s12204-023-2689-5 Abstract ( 162 )   PDF (1105KB) ( 13 )   The aim of this study was to assess the potential of surgical guides as a complementary tool to augmented reality (AR) in enhancing the safety and precision of pedicle screw placement in spinal surgery. Four trainers were divided into the AR navigation group using surgical guides and the free-hand group. Each group consisted of a novice and an experienced spine surgeon. A total of 80 pedicle screws were implanted. First, the AR group reconstructed the 3D model and planned the screw insertion route according to the computed tomography data of L2 lumbar vertebrae. Then, the Microsoft HoloLensTM 2 was used to identify the vertebral model, and the planned virtual path was superimposed on the real cone model. Next, the screw was placed according to the projected trajectory. Finally, Micron Tracker was used to measure the deviation of screws from the preoperatively planned trajectory, and pedicle screws were evaluated using the Gertzbein-Robbins scale. In the AR group, the linear deviations of the experienced doctor and the novice were (1.59±0.39) mm and (1.73±0.52) mm respectively, and the angle deviations were 2.72◦ ± 0.61◦ and 2.87◦ ± 0.63◦ respectively. In the free-hand group, the linear deviations of the experienced doctor and the novice were (2.88 ± 0.58) mm and (5.25 ± 0.62) mm respectively, and the angle deviations were 4.41◦ ± 1.18◦ and 7.15◦ ± 1.45◦ respectively. Both kinds of deviations between the two groups were significantly different (P < 0.05). The screw accuracy rate was 95% in the AR navigation group and 77.5% in the free-hand group. The results of this study indicate that the integration of surgical guides and AR is an innovative technique that can substantially enhance the safety and precision of spinal surgery and assist inexperienced doctors in completing the surgery. References | Related Articles | Metrics

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Direct Ink Writing Method of Fractal Wearable Flexible Sensor Based on Conductive Graphene/Polydimethylsiloxane Ink CHEN Junling1，2，3 (陈俊伶), GAO Feiyang1,3 (高飞扬), ZHANG Liming1，3* (张黎明), ZHENG Xiongfei1，3(郑雄飞) 2025, 30 (1):  18-26.  doi: 10.1007/s12204-023-2687-7 Abstract ( 117 )   PDF (1711KB) ( 6 )   Flexible electronic technology has laid the foundation for complex human-computer interaction system, and has attracted great attention in the field of human motion detection and soft robotics. Graphene has received an extensive attention due to its excellent electrical conductivity; however, how to use it to fabricate wearable flexible sensors with complex structures remains challenging. In this study, we studied the rheological behavior of graphene/polydimethylsiloxane ink and proposed an optimal graphene ratio, which makes the ink have a good printability and conductivity at the same time. Then, based on the theory of Peano fractal layout, we proposed a two-dimensional structure that can withstand multi-directional tension by replacing the traditional arris structure with the arc structure. After that, we manufactured circular arc fractal structure sensor by adjusting ink composition and printing structure through direct ink writing method. Finally, we evaluated the detection performance and repeatability of the sensor. This method provides a simple and effective solution for fabricating wearable flexible sensors and exhibits the potential to fabricate 3D complex flexible electronic devices. References | Related Articles | Metrics

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Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot ZHAO Yanfei1，2，3(赵艳飞), XIAO Peng4 (肖鹏), WANG Jingchuan1，2，3* (王景川), GUO Rui4*(郭锐） 2025, 30 (1):  27-33.  doi: 10.1007/s12204-023-2678-8 Abstract ( 117 )   PDF (995KB) ( 11 )   Mobile robots represented by smart wheelchairs can assist elderly people with mobility difficulties. This paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots, which can assist users to implement accurate navigation (e.g., docking) in the environment without prior maps. In order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms, this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic goals. At last, comparative experiments were carried out in the real environment. Results show that our method is superior in terms of safety, comfort and docking accuracy. References | Related Articles | Metrics

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Augmented Reality Based Navigation System for Endoscopic Transnasal Optic Canal Decompression FU Hang1 (傅航),XU Jiangchang1 (许江长), LI Yinwei2，4* (李寅炜),ZHOU Huifang2，4 (周慧芳),CHEN Xiaojun1，3* (陈晓军) 2025, 30 (1):  34-42.  doi: 10.1007/s12204-024-2722-3 Abstract ( 93 )   PDF (2193KB) ( 7 )   Endoscopic transnasal optic nerve decompression surgery plays a crucial role in minimal invasive treatment of complex traumatic optic neuropathy. However, a major challenge faced during the procedure is the inability to visualize the optic nerve intraoperatively. To address this issue, an endoscopic image-based augmented reality surgical navigation system is developed in this study. The system aims to virtually fuse the optic nerve onto the endoscopic images, assisting surgeons in determining the optic nerve’s position and reducing surgical risks. First, a calibration algorithm based on a checkerboard grid of immobile points is proposed, building upon existing calibration methods. Additionally, to tackle accuracy issues associated with augmented reality technology, an optical navigation and visual fusion compensation algorithm is proposed to improve the intraoperative tracking accuracy. To evaluate the system’s performance, model experiments were meticulously designed and conducted. The results confirm the accuracy and stability of the proposed system, with an average tracking error of (0.99 ± 0.46) mm. This outcome demonstrates the effectiveness of the proposed algorithm in improving the augmented reality surgical navigation system’s accuracy. Furthermore, the system successfully displays hidden optic nerves and other deep tissues, thus showcasing the promising potential for future applications in orbital and maxillofacial surgery. References | Related Articles | Metrics

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Low Latency Soft Fiberoptic Choledochoscope Robot Control System ZHOU Hanwei1 (周涵巍),ZHU Xinping1 (朱心平),MA Youwei2 (马有为),WANG Kundong1* (王坤东) 2025, 30 (1):  43-52.  doi: 10.1007/s12204-024-2709-0 Abstract ( 71 )   PDF (1220KB) ( 7 )   Soft fiberoptic choledochoscope is an important tool for the diagnose and surgical treatment of biliary disease. However, the traditional soft fiberoptic choledochoscope is hard to operate, due to the low position accuracy. Based on the conventional soft fiberoptic choledochoscope, an electrical soft fiberoptic choledochoscope robot with a low latency was developed. In order to improve the controllability of the conventional choledochoscope, the wire traction mechanism and the rotation mechanism are used to bend and rotate the scope, so as to control its movement orientation. The dead band compensation model and control algorithm of the wire traction mechanism are developed to improve the accuracy of the orientation control. The human-computer interaction system complex motion control system are developed based on ARM emedded system and FPGA. Thanks to the highspeed synchronization channel between FPGA and peripheral, the design of low latency whole-procedure surgical mode was established and verified. Combined with a micro image sensor, real-time video back transmission was realized. The performance of the robot prototype was verified by animal experiment in vivo on a pig. The robot has an extremely low operating latency of no more than 0.402 ms, and a high bending positioning accuracy of ±1.43◦ error margin within 99.7% confidence interval, which guarantees the safety of biliary surgery. References | Related Articles | Metrics

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Applications of Artificial Intelligence in Cardiac Electrophysiology and Clinical Diagnosis with Magnetic Resonance Imaging and Computational Modeling Techniques ZHAN Heqing1 (詹何庆), HAN Guilai1 (韩贵来), WEI Chuan’an1 (魏传安), LI Zhiqun2* (李治群) 2025, 30 (1):  53-65.  doi: 10.1007/s12204-023-2628-5 Abstract ( 174 )   PDF (232KB) ( 13 )   The underlying electrophysiological mechanisms and clinical treatments of cardiovascular diseases, which are the most common cause of morbidity and mortality worldwide, have gotten a lot of attention and been widely explored in recent decades. Along the way, techniques such as medical imaging, computing modeling, and artificial intelligence (AI) have always played significant roles in above studies. In this article, we illustrated the applications of AI in cardiac electrophysiological research and disease prediction. We summarized general principles of AI and then focused on the roles of AI in cardiac basic and clinical studies incorporating magnetic resonance imaging and computing modeling techniques. The main challenges and perspectives were also analyzed. References | Related Articles | Metrics

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Electrocardiogram Signal Denoising Using Optimized Adaptive Hybrid Filter with Empirical Wavelet Transform BALASUBRAMANIAN S1*, NARUKA Mahaveer Singh2, TEWARI Gaurav3 2025, 30 (1):  66-80.  doi: 10.1007/s12204-023-2591-1 Abstract ( 100 )   PDF (1496KB) ( 3 )   Cardiovascular diseases are the world’s leading cause of death; therefore cardiac health of the human heart has been a fascinating topic for decades. The electrocardiogram (ECG) signal is a comprehensive noninvasive method for determining cardiac health. Various health practitioners use the ECG signal to ascertain critical information about the human heart. In this article, swarm intelligence approaches are used in the biomedical signal processing sector to enhance adaptive hybrid filters and empirical wavelet transforms (EWTs). At first, the white Gaussian noise is added to the input ECG signal and then applied to the EWT. The ECG signals are denoised by the proposed adaptive hybrid filter. The honey badge optimization (HBO) algorithm is utilized to optimize the EWT window function and adaptive hybrid filter weight parameters. The proposed approach is simulated by MATLAB 2018a using the MIT-BIH dataset with white Gaussian, electromyogram and electrode motion artifact noises. A comparison of the HBO approach with recursive least square-based adaptive filter, multichannel least means square, and discrete wavelet transform methods has been done in order to show the efficiency of the proposed adaptive hybrid filter. The experimental results show that the HBO approach supported by EWT and adaptive hybrid filter can be employed efficiently for cardiovascular signal denoising. References | Related Articles | Metrics

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Experimental Investigation of Possibility of Simultaneously Monitoring Lung Perfusion/Cardiomotility and Ventilation via Thoracic Impedance Measurement BAI Zixuan 1(白子轩), MA Yixin1，2∗(马艺馨), KONG Zhibin3(孔志斌),XUE Shan4 (薛珊) 2025, 30 (1):  81-90.  doi: 10.1007/s12204-023-2639-2 Abstract ( 77 )   PDF (808KB) ( 5 )   Impedance pneumography has a significant advantage for continuous and noninvasive monitoring of respiration, compared with conventional flowmeter-based ventilation measurement technologies. While thoracic impedance is sensitive to pulmonary ventilation, it is also sensitive to physiological activities such as blood flow and cardiomotility, in addition, body movement/posture. This paper explores the possibility of simultaneously monitoring pulmonary ventilation, blood circulation and cardiomotility by bioimpedance measurement. Respiratory, blood perfusion and cardiomotility signals are extracted using the wavelet method from thoracic impedance data measured in breath-holding and tidal breathing statuses, to investigate signal strength and their dependency. This research provides a foundation for the development of bedside devices to monitor various physiological activities. References | Related Articles | Metrics

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Histological Image Diagnosis of Breast Cancer Based on Multi-Attention Convolution Neural Network XU Wangwang1，2 (徐旺旺), XU Liangfeng1，2 (许良凤), LIU Ninghui3(刘宁徽), LU Na3(律娜) 2025, 30 (1):  91-106.  doi: 10.1007/s12204-024-2705-4 Abstract ( 91 )   PDF (1715KB) ( 7 )   Breast cancer is a serious and high morbidity disease in women, and it is the main cause of cancer death in China. However, getting tested and diagnosed early can reduce the risk of cancer. At present, there are clinical examinations, imaging screening and biopsies, among which histopathological examination is the gold standard. However, the process is complicated and time-consuming, and misdiagnosis may exist. This paper puts forward a classification framework based on deep learning, introducing multi-attention mechanism, selecting kernel convolution instead of ordinary convolution, and using different weights and combinations to pay attention to the accuracy index and growth rate of the model. In addition, we also compared the learning rate regulators. Error function can fine-tune the learning rate to achieve good performance, using label softening to reduce the loss error caused by model error recognition in the label, and assigning different category weights in the loss function to balance the positive and negative samples. We used the BreakHis data set to automatically classify histological images into benign and malignant, four categories and eight subtypes. Experimental results showed that the accuracy of binary classifications ranged from 98.23% to 99.50%, and that of multipl classifications ranged from 97.89% to 98.11%. References | Related Articles | Metrics

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LOBO Optimization-Tuned Deep-Convolutional Neural Network for Brain Tumor Classification Approach A. Sahaya Anselin Nisha1* , NARMADHA R.1 , AMIRTHALAKSHMI T. M.2,BALAMURUGAN V.1, VEDANARAYANAN V.1 2025, 30 (1):  107-114.  doi: 10.1007/s12204-023-2625-8 Abstract ( 82 )   PDF (1079KB) ( 3 )   The categorization of brain tumors is a significant issue for healthcare applications. Perfect and timely identification of brain tumors is important for employing an effective treatment of this disease. Brain tumors possess high changes in terms of size, shape, and amount, and hence the classification process acts as a more difficult research problem. This paper suggests a deep learning model using the magnetic resonance imaging technique that overcomes the limitations associated with the existing classification methods. The effectiveness of the suggested method depends on the coyote optimization algorithm, also known as the LOBO algorithm, which optimizes the weights of the deep-convolutional neural network classifier. The accuracy, sensitivity, and specificity indices, which are obtained to be 92.40%, 94.15%, and 91.92%, respectively, are used to validate the effectiveness of the suggested method. The result suggests that the suggested strategy is superior for effectively classifying brain tumors. References | Related Articles | Metrics

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Significant Retest Effects in Spatial Working Memory Task MA Xianda1,2,3‡ (马显达), LAN Zhaohui1,2,3‡ (兰兆辉),CHEN Zhitang1,2,3 (陈志堂), MONISHA M L4, HE Xinyi1,2,3 (何欣怡), LI Weidong1,2,3* (李卫东) 2025, 30 (1):  115-120.  doi: 10.1007/s12204-023-2585-z Abstract ( 91 )   PDF (566KB) ( 1 )   Working memory is a core cognitive function that supports goal-directed behavior and complex thought. We developed a spatial working memory and attention test on paired symbols (SWAPS) which has been proved to be a useful and valid tool for spatial working memory and attention studies in the fields of cognitive psychology, education, and psychiatry. The repeated administration of working memory capacity tests is common in clinical and research settings. Studies suggest that repeated cognitive tests may improve the performance scores also known as retest effects. The systematic investigation of retest effects in SWAPS is critical for interpreting scientific results, but it is still not fully developed. To address this, we recruited 77 college students aged 18—21 years and used SWAPS comprising 72 trials with different memory loads, learning time, and delay span. We repeated the test once a week for five weeks to investigate the retest effects of SWAPS. There were significant retest effects in the first two tests: the accuracy of the SWAPS tests significantly increased, and then stabilized. These findings provide useful information for researchers to appropriately use or interpret the repeated working memory tests. Further experiments are still needed to clarify the factors that mediate the retest effects, and find out the cognitive mechanism that influences the retest effects. References | Related Articles | Metrics

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Positional Information is a Strong Supervision for Volumetric Medical Image Segmentation ZHAO Yinjie1 (赵寅杰), HOU Runpingg1 (侯润萍), ZENG Wanqin2 (曾琬琴), QIN Yulei1 (秦玉磊), SHEN Tianle2 (沈天乐), XU Zhiyong2 (徐志勇), FU Xiaolong2* (傅小龙), SHEN Hongbin1* (沈红斌) 2025, 30 (1):  121-129.  doi: 10.1007/s12204-023-2614-y Abstract ( 122 )   PDF (481KB) ( 1 )   Medical image segmentation is a crucial preliminary step for a number of downstream diagnosis tasks. As deep convolutional neural networks successfully promote the development of computer vision, it is possible to make medical image segmentation a semi-automatic procedure by applying deep convolutional neural networks to finding the contours of regions of interest that are then revised by radiologists. However, supervised learning necessitates large annotated data, which are difficult to acquire especially for medical images. Self-supervised learning is able to take advantage of unlabeled data and provide good initialization to be finetuned for downstream tasks with limited annotations. Considering that most self-supervised learning especially contrastive learning methods are tailored to natural image classification and entail expensive GPU resources, we propose a novel and simple pretext-based self-supervised learning method that exploits the value of positional information in volumetric medical images. Specifically, we regard spatial coordinates as pseudo labels and pretrain the model by predicting positions of randomly sampled 2D slices in volumetric medical images. Experiments on four semantic segmentation datasets demonstrate the superiority of our method over other self-supervised learning methods in both semisupervised learning and transfer learning settings. Codes are available at https://github.com/alienzyj/PPos. References | Related Articles | Metrics

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Brain Age Detection of Alzheimer’s Disease Magnetic Resonance Images Based on Mutual Information - Support Vector Regression LIU Yuchuan1 (刘玉川), LI Hao1 (李浩), TANG Yulong1 (唐宇龙), LIANG Dujuan2 (梁杜娟), TAN Jia3 (谭佳), FU Yue1 (符玥), LI Yongming4∗ (李勇明) 2025, 30 (1):  130-135.  doi: 10.1007/s12204-023-2590-2 Abstract ( 96 )   PDF (635KB) ( 2 )   Brain age is an effective biomarker for diagnosing Alzheimer’s disease (AD). Aimed at the issue that the existing brain age detection methods are inconsistent with the biological hypothesis that AD is the accelerated aging of the brain, a mutual information - support vector regression (MI-SVR) brain age prediction model is proposed. First, the age deviation is introduced according to the biological hypothesis of AD. Second, fitness function is designed based on mutual information criterion. Third, support vector regression and fitness function are used to obtain the predicted brain age and fitness value of the subjects, respectively. The optimal age deviation is obtained by maximizing the fitness value. Finally, the proposed method is compared with some existing brain age detection methods. Experimental results show that the brain age obtained by the proposed method has better separability, can better reflect the accelerated aging of AD, and is more helpful for improving the diagnostic accuracy of AD. References | Related Articles | Metrics

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Medical Image Encryption Based on Fisher-Yates Scrambling and Filter Diffusion HUANG Jiaxin (黄佳鑫), GUO Yali (郭亚丽), GAO Ruoyun (高若云),LI Shanshan∗ (李珊珊) 2025, 30 (1):  136-152.  doi: 10.1007/s12204-023-2618-7 Abstract ( 139 )   PDF (8076KB) ( 9 )   A medical image encryption is proposed based on the Fisher-Yates scrambling, filter diffusion and S-box substitution. First, chaotic sequence associated with the plaintext is generated by logistic-sine-cosine system, which is used for the scrambling, substitution and diffusion processes. The three-dimensional Fisher-Yates scrambling, S-box substitution and diffusion are employed for the first round of encryption. The chaotic sequence is adopted for secondary encryption to scramble the ciphertext obtained in the first round. Then, three-dimensional filter is applied to diffusion for further useful information hiding. The key to the algorithm is generated by the combination of hash value of plaintext image and the input parameters. It improves resisting ability of plaintext attacks. The security analysis shows that the algorithm is effective and efficient. It can resist common attacks. In addition, the good diffusion effect shows that the scheme can solve the differential attacks encountered in the transmission of medical images and has positive implications for future research. References | Related Articles | Metrics

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Ensemble Learning-Based Mortality Prediction After Acute Myocardial Infarction YAN Mingxuan1 (颜铭萱), MIAO Yutong2，3 (苗雨桐), SHENG Shuqian1 (盛淑茜), GAN Xiaoying1 (甘小莺), HE Ben2 (何 奔), SHEN Lan2，3* (沈 兰) 2025, 30 (1):  153-165.  doi: 10.1007/s12204-023-2611-1 Abstract ( 98 )   PDF (710KB) ( 9 )   A mortality prediction model based on small acute myocardial infarction (AMI) patients coherent with low death rate is established. In total, 1 639 AMI patients are selected as research objects who received treatment in seven tertiary and secondary hospitals in Shanghai between January 1, 2016 and January 1, 2018. Among them, 72 patients deceased during the two-year follow-up. Models are established with ensemble learning framework and machine learning algorithms based on 51 physiological indicators of the patient. Shapley additive explanations algorithm and univariate test with point-biserial and phi correlation coefficients are employed to determine significant features and rank feature importance. Based on 5-fold cross validation experiment and external validation, prediction model with self-paced ensemble framework and random forest algorithm achieves the best performance with area under receiver operating characteristic curve (AUROC) score of 0.911 and recall of 0.864. Both feature ranking methods showed that ejection fractions, serum creatinine (admission), hemoglobin and Killip class are the most important features. With these top-ranked features, the simplified prediction model is capable of achieving a comparable result with AUROC score of 0.872 and recall of 0.818. This work proposes a new method to establish mortality prediction models for AMI patients based on self-paced ensemble framework, which allows models to achieve high performance with small scale of patients coherent with low death rate. It will assist in medical decision and prognosis as a new reference. References | Related Articles | Metrics

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Adjacent Segment Biomechanical Changes After Implantation of Cage Plus Plate or Zero-Profile Device in Different Segmental Anterior Cervical Discectomy and Fusion YE Peng (叶鹏), FU Rongchang∗ (富荣昌), WANG Zhaoyao (王召耀) 2025, 30 (1):  166-174.  doi: 10.1007/s12204-023-2633-8 Abstract ( 116 )   PDF (929KB) ( 10 )   Cage plus plate (CP) and zero-profile (Zero-P) devices are widely used in anterior cervical discectomy and fusion (ACDF). This study aimed to compare adjacent segment biomechanical changes after ACDF when using Zero-P device and CP in different segments. First, complete C1—C7 cervical segments were constructed and validated. Meanwhile, four surgery models were developed by implanting the Zero-P device or CP into C4—C5 or C5—C6 segments based on the intact model. The segmental range of motion (ROM) and maximum value of the intradiscal pressure of the surgery models were compared with those of the intact model. The implantation of CP and Zero-P devices in C4—C5 segments decreased ROM by about 91.6% and 84.3%, respectively, and increased adjacent segment ROM by about 8.3% and 6.82%, respectively. The implantation of CP and Zero-P devices in C5—C6 segments decreased ROM by about 93.3% and 89.9%, respectively, while increasing adjacent segment ROM by about 4.9% and 4%, respectively. Furthermore, the implantation of CP and Zero-P devices increased the intradiscal pressure in the adjacent segments of C4—C5 segments by about 4.5% and 6.7%, respectively. The implantation of CP and Zero-P devices significantly increased the intradiscal pressure in the adjacent segments of C5—C6 by about 54.1% and 15.4%, respectively. In conclusion, CP and Zero-P fusion systems can significantly reduce the ROM of the fusion implant segment in ACDF while increasing the ROM and intradiscal pressure of adjacent segments. Results showed that Zero-P fusion system is the best choice for C5—C6 segmental ACDF. However, further studies are needed to select the most suitable cervical fusion system for C4—C5 segmental ACDF. Therefore, this study provides biomechanical recommendations for clinical surgery. References | Related Articles | Metrics

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Vibration Transmission Characteristics of Shoe Sole Based on Mechanical Mobility and Vibration Transmissibility WU Xuyang1 (吴旭阳), LIU Xiaoying1 (刘晓颖), HAO Yanhua1 (郝艳华), LIU Changhuang1 (刘长煌), HUANG Xianwei2 (黄贤伟) 2025, 30 (1):  175-186.  doi: 10.1007/s12204-023-2587-x Abstract ( 109 )   PDF (2284KB) ( 6 )   It is particularly important to explore the response and transmission characteristics of shoe sole when exposed to foot-transmitted vibration (FTV) in daily life. In this study, based on mechanical mobility and vibration transmissibility, the vibration response and transmission characteristics of ordinary sole and multicellular structure sole under three excitation modes were analyzed with finite element analysis. The analysis results of the ordinary sole are as follows: The distribution and transmission of vibration energy of ordinary sole are more related to the excitation position and mode-shape; the phalange region is more violent in vibration response to vibration and transmission of vibration. In addition, the analysis results of multi-cellular structure soles show that different types of multi-cellular structure soles have different effects on the equivalent mechanical mobility and the equivalent vibration transmissibility, among which Grid type has the greatest influence. So, this study can help prevent foot injury and provide guidance for the optimal design of the sole. References | Related Articles | Metrics

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Biomechanical Analysis of Scoliosis Orthopedic Force Loading with Human Avoidance Effect ZHU Ye1 (朱晔), REN Dong1 (任东), ZHANG Shuang2 (张爽), CAO Qian3 (曹倩) 2025, 30 (1):  187-196.  doi: 10.1007/s12204-023-2620-0 Abstract ( 98 )   PDF (1836KB) ( 2 )   Due to the lack of human avoidance analysis, the orthosis cannot accurately apply orthopedic force during orthopedic, resulting in poor orthopedic effect. Therefore, the relationship between the human body’s active avoidance ability and force application is studied to achieve accurate loading of orthopedic force. First, a high-precision scoliosis model was established based on computed tomography data, and the relationship between orthopedic force and Cobb angle was analyzed. Then 9 subjects were selected for avoidance ability test grouped by body mass index calculation, and the avoidance function of different groups was fitted. The avoidance function corrected the application of orthopedic forces. The results show that the optimal correction force calculated by the finite element method was 60 N. The obese group had the largest avoidance ability, followed by the standard group and the lean group. When the orthopedic force was 60 N, the Cobb angle was reduced from 33.77◦ to 20◦, the avoidance ability of the standard group at 50 N obtained from the avoidance function was 20.28% and 10.14 N was actively avoided. Therefore, when 50 N was applied, 60.14 N was actually generated, which can achieve the orthopedic effect of 60 N numerical simulation analysis. The avoidance effect can take the active factors of the human body into consideration in the orthopedic process, so as to achieve a more accurate application of orthopedic force, and provide data reference for clinicians in the orthopedic process. References | Related Articles | Metrics

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Coordination Design of a Power-Assisted Ankle Exoskeleton Robot Based on Active-Passive Combined Drive HE Guisong (贺贵松), HUANG Xuegong* (黄学功),LI Feng（李峰) 2025, 30 (1):  197-208.  doi: 10.1007/s12204-023-2589-8 Abstract ( 90 )   PDF (2026KB) ( 11 )   With the continuous escalation of modern war, soldiers need to transport more combat materials to the combat area. The limited load-bearing capacity of soldiers seriously restricts their carrying capacity and mobility. It is urgent to develop a power-assisted exoskeleton robot suitable for individual combat. In the past, most power-assisted exoskeleton robots were driven by motors. This driving method has an excellent powerassisted effect, but the endurance is often insufficient. In view of this shortcoming, this study designed an ankle exoskeleton robot based on an active-passive combined drive through simulation analysis of human motion. It used OpenSim software to simulate and verify that the addition of spring could achieve a good effect. At the same time, according to the gait characteristics of the human body, the gait planning of an exoskeleton robot was carried out. Afterwards, theoretical analysis explained that the cooperation among spring, motor and wearer could be realized in this gait. Finally, the assisting ability and driving coordination of the active-passive combination driven ankle exoskeleton robot were verified through experiments. References | Related Articles | Metrics