生物医学工程学杂志 Pub Date : 2025-06-25 DOI: 10.7507/1001-5515.202408035

Yan Lu, Juan Chen, Ting Zhang, Shu Yan, Dongzi Xu, Zhaolian Ouyang

{"title":"[Analysis of the global registration status of clinical trials for artificial intelligence medical device].","authors":"Yan Lu, Juan Chen, Ting Zhang, Shu Yan, Dongzi Xu, Zhaolian Ouyang","doi":"10.7507/1001-5515.202408035","DOIUrl":"10.7507/1001-5515.202408035","url":null,"abstract":"The rapid development of artificial intelligence technology is driving profound changes in medical practice, particularly in the field of medical device application. Based on data from the U.S. clinical trials registry, this study analyzes the global registration landscape of clinical trials involving artificial intelligence-based medical devices, aiming to provide a reference for their clinical research and application. A total of 2 494 clinical trials related to artificial intelligence medical devices have been registered worldwide, with participation from 66 countries or regions. The United States leads with 908 trials, while for other countries or regions, including China, each has fewer than 300 trials. Germany, the United States, and Belgium serve as central hubs for international collaboration. Among the sponsors, 63.96% are universities or hospitals, 22.36% are enterprises, and the remainder includes individuals, government agencies and others. Of all trials, 79.99% are interventional studies, 94.67% place no restrictions on participant gender, and 69.69% exclude children. The targeted diseases are primarily neurological and mental disorders. This study systematically reveals the global distribution characteristics and research trends of artificial intelligence medical device clinical trials, offering valuable data support and practical insights for advancing international collaboration, resource allocation, and policy development in this field.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"42 3","pages":"512-519"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12236199/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144498287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Fatigue driving detection based on prefrontal electroencephalogram asymptotic hierarchical fusion network]. [基于前额叶脑电图渐近层次融合网络的疲劳驾驶检测]。

生物医学工程学杂志 Pub Date : 2025-06-25 DOI: 10.7507/1001-5515.202407083

Jiazheng Sun, Weimin Li, Ningling Zhang, Cai Chen, Shengzhe Wang, Fulai Peng

{"title":"[Fatigue driving detection based on prefrontal electroencephalogram asymptotic hierarchical fusion network].","authors":"Jiazheng Sun, Weimin Li, Ningling Zhang, Cai Chen, Shengzhe Wang, Fulai Peng","doi":"10.7507/1001-5515.202407083","DOIUrl":"10.7507/1001-5515.202407083","url":null,"abstract":"Fatigue driving is one of the leading causes of traffic accidents, posing a significant threat to drivers and road safety. Most existing methods focus on studying whole-brain multi-channel electroencephalogram (EEG) signals, which involve a large number of channels, complex data processing, and cumbersome wearable devices. To address this issue, this paper proposes a fatigue detection method based on frontal EEG signals and constructs a fatigue driving detection model using an asymptotic hierarchical fusion network. The model employed a hierarchical fusion strategy, integrating an attention mechanism module into the multi-level convolutional module. By utilizing both cross-attention and self-attention mechanisms, it effectively fused the hierarchical semantic features of power spectral density (PSD) and differential entropy (DE), enhancing the learning of feature dependencies and interactions. Experimental validation was conducted on the public SEED-VIG dataset. The proposed model achieved an accuracy of 89.80% using only four frontal EEG channels. Comparative experiments with existing methods demonstrate that the proposed model achieves high accuracy and superior practicality, providing valuable technical support for fatigue driving monitoring and prevention.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"42 3","pages":"544-551"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12236220/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144498307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Application of multi-scale spatiotemporal networks in physiological signal and facial action unit measurement]. [多尺度时空网络在生理信号和面部动作单元测量中的应用]。

生物医学工程学杂志 Pub Date : 2025-06-25 DOI: 10.7507/1001-5515.202408026

Siyuan Xu, Sunjie Zhang

{"title":"[Application of multi-scale spatiotemporal networks in physiological signal and facial action unit measurement].","authors":"Siyuan Xu, Sunjie Zhang","doi":"10.7507/1001-5515.202408026","DOIUrl":"10.7507/1001-5515.202408026","url":null,"abstract":"Multi-task learning (MTL) has demonstrated significant advantages in the field of physiological signal measurement. This approach enhances the model's generalization ability by sharing parameters and features between similar tasks, even in data-scarce environments. However, traditional multi-task physiological signal measurement methods face challenges such as feature conflicts between tasks, task imbalance, and excessive model complexity, which limit their application in complex environments. To address these issues, this paper proposes an enhanced multi-scale spatiotemporal network (EMSTN) based on Eulerian video magnification (EVM), super-resolution reconstruction and convolutional multilayer perceptron. First, EVM is introduced in the input stage of the network to amplify subtle color and motion changes in the video, significantly improving the model's ability to capture pulse and respiratory signals. Additionally, a super-resolution reconstruction module is integrated into the network to enhance the image resolution, thereby improving detail capture and increasing the accuracy of facial action unit (AU) tasks. Then, convolutional multilayer perceptron is employed to replace traditional 2D convolutions, improving feature extraction efficiency and flexibility, which significantly boosts the performance of heart rate and respiratory rate measurements. Finally, comprehensive experiments on the Binghamton-Pittsburgh 4D Spontaneous Facial Expression Database (BP4D+) fully validate the effectiveness and superiority of the proposed method in multi-task physiological signal measurement.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"42 3","pages":"552-559"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12236218/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144498288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Detection of motor intention in patients with consciousness disorder based on electroencephalogram and functional near infrared spectroscopy combined with motor brain-computer interface paradigm]. [基于脑电图和功能近红外光谱结合运动脑机接口范式的意识障碍患者运动意图检测]。

生物医学工程学杂志 Pub Date : 2025-06-25 DOI: 10.7507/1001-5515.202502027

Xiaoke Chai, Nan Wang, Jiuxiang Song, Yi Yang

{"title":"[Detection of motor intention in patients with consciousness disorder based on electroencephalogram and functional near infrared spectroscopy combined with motor brain-computer interface paradigm].","authors":"Xiaoke Chai, Nan Wang, Jiuxiang Song, Yi Yang","doi":"10.7507/1001-5515.202502027","DOIUrl":"10.7507/1001-5515.202502027","url":null,"abstract":"Clinical grading diagnosis of disorder of consciousness (DOC) patients relies on behavioral assessment, which has certain limitations. Combining multi-modal technologies and brain-computer interface (BCI) paradigms can assist in identifying patients with minimally conscious state (MCS) and vegetative state (VS). This study collected electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals under motor BCI paradigms from 14 DOC patients, who were divided into two groups based on clinical scores: 7 in the MCS group and 7 in the VS group. We calculated event-related desynchronization (ERD) and motor decoding accuracy to analyze the effectiveness of motor BCI paradigms in detecting consciousness states. The results showed that the classification accuracies for left-hand and right-hand movement tasks using EEG were 93.28% and 76.19% for the MCS and VS groups, respectively; the classification precisions using fNIRS were 53.72% and 49.11% for these groups. When combining EEG and fNIRS features, the classification accuracies for left-hand and right-hand movement tasks in the MCS and VS groups were 95.56% and 87.38%, respectively. Although there was no statistically significant difference in motor decoding accuracy between the two groups, significant differences in ERD were observed between different consciousness states during left-hand movement tasks ( P < 0.001). This study demonstrates that motor BCI paradigms can assist in assessing the level of consciousness, with EEG being more sensitive for evaluating residual motor intention intensity. Moreover, the ERD feature of motor intention intensity is more sensitive than BCI classification accuracy.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"42 3","pages":"447-454"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12236222/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144498293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Evaluation methods for the rehabilitation efficacy of bidirectional closed-loop motor imagery brain-computer interface active rehabilitation training systems]. [双向闭环运动意象脑机接口主动康复训练系统康复效果评价方法]。

生物医学工程学杂志 Pub Date : 2025-06-25 DOI: 10.7507/1001-5515.202407097

He Pan, Peng Ding, Fan Wang, Tianwen Li, Lei Zhao, Wenya Nan, Anmin Gong, Yunfa Fu

引用次数: 0

[A portable steady-state visual evoked potential brain-computer interface system for smart healthcare]. [用于智能医疗的便携式稳态视觉诱发电位脑机接口系统]。

生物医学工程学杂志 Pub Date : 2025-06-25 DOI: 10.7507/1001-5515.202412051

Yisen Zhu, Zhouyu Ji, Shuran Li, Haicheng Wang, Yunfa Fu, Hongtao Wang

{"title":"[A portable steady-state visual evoked potential brain-computer interface system for smart healthcare].","authors":"Yisen Zhu, Zhouyu Ji, Shuran Li, Haicheng Wang, Yunfa Fu, Hongtao Wang","doi":"10.7507/1001-5515.202412051","DOIUrl":"10.7507/1001-5515.202412051","url":null,"abstract":"This paper realized a portable brain-computer interface (BCI) system tailored for smart healthcare. Through the decoding of steady-state visual evoked potential (SSVEP), this system can rapidly and accurately identify the intentions of subjects, thereby meeting the practical demands of daily medical scenarios. Firstly, an SSVEP stimulation interface and an electroencephalogram (EEG) signal acquisition software were designed, which enable the system to execute multi-target and multi-task operations while also incorporating data visualization functionality. Secondly, the EEG signals recorded from the occipital region were decomposed into eight sub-frequency bands using filter bank canonical correlation analysis (FBCCA). Subsequently, the similarity between each sub-band signal and the reference signals was computed to achieve efficient SSVEP decoding. Finally, 15 subjects were recruited to participate in the online evaluation of the system. The experimental results indicated that in real-world scenarios, the system achieved an average accuracy of 85.19% in identifying the intentions of the subjects, and an information transfer rate (ITR) of 37.52 bit/min. This system was awarded third prize in the Visual BCI Innovation Application Development competition at the 2024 World Robot Contest, validating its effectiveness. In conclusion, this study has developed a portable, multifunctional SSVEP online decoding system, providing an effective approach for human-computer interaction in smart healthcare.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"42 3","pages":"455-463"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12236205/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144498283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Analysis of the global competitive landscape in artificial intelligence medical device research]. [人工智能医疗器械研究全球竞争格局分析]。

生物医学工程学杂志 Pub Date : 2025-06-25 DOI: 10.7507/1001-5515.202407046

Juan Chen, Lizi Pan, Junyu Long, Nan Yang, Fei Liu, Yan Lu, Zhaolian Ouyang

{"title":"[Analysis of the global competitive landscape in artificial intelligence medical device research].","authors":"Juan Chen, Lizi Pan, Junyu Long, Nan Yang, Fei Liu, Yan Lu, Zhaolian Ouyang","doi":"10.7507/1001-5515.202407046","DOIUrl":"10.7507/1001-5515.202407046","url":null,"abstract":"The objective of this study is to map the global scientific competitive landscape in the field of artificial intelligence (AI) medical devices using scientific data. A bibliometric analysis was conducted using the Web of Science Core Collection to examine global research trends in AI-based medical devices. As of the end of 2023, a total of 55 147 relevant publications were identified worldwide, with 76.6% published between 2018 and 2024. Research in this field has primarily focused on AI-assisted medical image and physiological signal analysis. At the national level, China (17 991 publications) and the United States (14 032 publications) lead in output. China has shown a rapid increase in publication volume, with its 2023 output exceeding twice that of the U.S.; however, the U.S. maintains a higher average citation per paper (China: 16.29; U.S.: 35.99). At the institutional level, seven Chinese institutions and three U.S. institutions rank among the global top ten in terms of publication volume. At the researcher level, prominent contributors include Acharya U Rajendra, Rueckert Daniel and Tian Jie, who have extensively explored AI-assisted medical imaging. Some researchers have specialized in specific imaging applications, such as Yang Xiaofeng (AI-assisted precision radiotherapy for tumors) and Shen Dinggang (brain imaging analysis). Others, including Gao Xiaorong and Ming Dong, focus on AI-assisted physiological signal analysis. The results confirm the rapid global development of AI in the medical device field, with \"AI + imaging\" emerging as the most mature direction. China and the U.S. maintain absolute leadership in this area-China slightly leads in publication volume, while the U.S., having started earlier, demonstrates higher research quality. Both countries host a large number of active research teams in this domain.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"42 3","pages":"496-503"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12236202/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144498286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[The analysis of invention patents in the field of artificial intelligent medical devices]. 【人工智能医疗器械领域发明专利分析】。

生物医学工程学杂志 Pub Date : 2025-06-25 DOI: 10.7507/1001-5515.202407044

Ting Zhang, Juan Chen, Yan Lu, Dongzi Xu, Shu Yan, Zhaolian Ouyang

{"title":"[The analysis of invention patents in the field of artificial intelligent medical devices].","authors":"Ting Zhang, Juan Chen, Yan Lu, Dongzi Xu, Shu Yan, Zhaolian Ouyang","doi":"10.7507/1001-5515.202407044","DOIUrl":"10.7507/1001-5515.202407044","url":null,"abstract":"The emergence of new-generation artificial intelligence technology has brought numerous innovations to the healthcare field, including telemedicine and intelligent care. However, the artificial intelligent medical device sector still faces significant challenges, such as data privacy protection and algorithm reliability. This study, based on invention patent analysis, revealed the technological innovation trends in the field of artificial intelligent medical devices from aspects such as patent application time trends, hot topics, regional distribution, and innovation players. The results showed that global invention patent applications had remained active, with technological innovations primarily focused on medical image processing, physiological signal processing, surgical robots, brain-computer interfaces, and intelligent physiological parameter monitoring technologies. The United States and China led the world in the number of invention patent applications. Major international medical device giants, such as Philips, Siemens, General Electric, and Medtronic, were at the forefront of global technological innovation, with significant advantages in patent application volumes and international market presence. Chinese universities and research institutes, such as Zhejiang University, Tianjin University, and the Shenzhen Institute of Advanced Technology, had demonstrated notable technological innovation, with a relatively high number of patent applications. However, their overseas market expansion remained limited. This study provides a comprehensive overview of the technological innovation trends in the artificial intelligent medical device field and offers valuable information support for industry development from an informatics perspective.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"42 3","pages":"504-511"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12236223/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144498317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[A study on electroencephalogram characteristics of depression in patients with aphasia based on resting state and emotional Stroop task]. [基于静息状态和情绪Stroop任务的失语患者抑郁的脑电图特征研究]。

生物医学工程学杂志 Pub Date : 2025-06-25 DOI: 10.7507/1001-5515.202503034

Siyuan Ding, Yan Zhu, Chang Shi, Banghua Yang

引用次数: 0

[Advances in low-dose cone-beam computed tomography image reconstruction methods based on deep learning]. [基于深度学习的低剂量锥束ct图像重建方法研究进展]。

生物医学工程学杂志 Pub Date : 2025-06-25 DOI: 10.7507/1001-5515.202409021

Jiangyuan Shi, Ying Song, Guangjun Li, Sen Bai

引用次数: 0

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