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6th International Conference on Biomedical and Health Informatics, ICBHI 2024

作者： Wang, Po-Xiang Jiang, Ching-Fen Program of Smart Healthcare and Bioinformatics I-Shou University Kaohsiung City Taiwan

ISBN: (纸本)9783031863226

Sarcopenia is characterized by a persistent reduction in skeletal muscle mass and function, which, if undetected and untreated, can lead to serious consequences such as increased fall risk and long-term immobility. Although sarcopenia has gained attention in recent years, there are currently no effective pharmacological treatments. Muscle loss can only be mitigated in the early stages through resistance training and nutritional interventions. However, precise detection methods used in hospitals are often costly and inaccessible to the general population. Traditional screening methods, like the Sarcopenia Self-Screening Questionnaire (SARC-F), proposed by the Asian Working Group for Sarcopenia, rely on subjective patient reports and lack objectivity. This study aims to address this gap by developing a rapid and convenient sarcopenia screening device that can be used at home or in community settings. Leveraging Internet of Things (IoT) technology, we developed a surface electromyography (sEMG) system to measure the muscle activity of the gastrocnemius, tibialis anterior, and quadriceps during walking and standing. The muscle signals were wirelessly transmitted using the MQTT protocol for backend analysis, enabling real-time gait and muscle strength monitoring. Twenty-eight healthy elderly individuals from the community participated in this study. To validate the effectiveness of the device, traditional sarcopenia-related physical measurements were obtained using standard methods. We first classified participants into four sarcopenia risk levels using the above data. In the meantime, we used deep image gait analysis software (GaitBEST) to classify it into four risk levels. Preliminary results show that these two classification levels had a slight discrepancy. However, the sEMG signals obtained using the developed device reflect the risk level determined by gait pattern, indicating the necessity of further analysis to explore the relation between gait and skeletal muscl

关键词： Electromyography

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Non-contact Respiratory Rate Detection Using 24 GHz Continuous-Wave Radar 6th

Non-contact Respiratory Rate Detection Using 24 GHz Continu...

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6th International Conference on Biomedical and Health Informatics, ICBHI 2024

作者： Chen, Yun-Chien Jiang, Ching-Fen Graduate Degree Program of Smart Healthcare and Bioinformatics I-Shou University Kaohsiung City Taiwan

ISBN: (纸本)9783031863226

Respiratory rate is an important vital sign that reflects health status. In hospital, traditional methods of measuring respiratory rate include manual counting and using a respiratory belt, both of which may pose a risk of contact infection. This study conducted clinical trial in medical ward of hospital, presented a non-contact method based on a 24 GHz continuous-wave radar to detect the displacement of the chest wall during breathing. By processing the radar signal, we were able to obtain the respiratory rate. However, during clinical validation, uncertainty in the daily measurement time of vital sign, which serve as the gold standard, can lead to comparison error. To overcome the interference caused by the issue, this study proposed a validation method that calculates the average of radar-detected respiratory rate over specific time interval and uses the standard deviation of these time interval as an indicator of the stability of the radar-detected respiratory rate value. Statistical results show that the radar-detected respiratory rate derived in this study is consistent with the hospital-measured respiratory rate (p = 0.17) and exhibits significant differences across different subjects (p © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Noninvasive medical procedures

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A virtual positioning system for external beam radiotherapy

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Virtual Reality 2023年第3期27卷 2569-2582页

作者： Sung, Wen-Hsu Jiang, Ching-Fen Su, Tai-Sin Sun, Shuh-Ping Department of Physical Therapy and Assistive Technology National Yang Ming Chiao Tung University Taipei Taiwan Graduate Program of Smart Healthcare and Bioinformatics I-Shou University Kaohsiung Taiwan Department of Biomedical Engineering I-Shou University Kaohsiung Taiwan Department of Digital Media Design I-Shou University Kaohsiung Taiwan

In radiation therapy, positioning patients to ensure the accuracy of the setup of a procedure is a routine and labour-intensive process that substantially determines the efficacy of treatment. In this study, we propose a virtual positioning system that can simulate the positioning process with a visible beam path under the broad view of a life-like patient-positioning platform to obviate problems with excessively narrow view. This system integrates image processing, computer graphics, and virtual reality to encompass a 3D treatment target reconstructed from medical images of different modalities in a virtual scene. An innovative evaluation method is further proposed to verify the efficacy of the system using a full-scale 3D solid anthropometric model, and a treatment target showed an accuracy of 99% in calibration and a mean compatibility of 95% with an actual measurement. The proposed virtual positioning system provides a training and education platform for radiation therapy. The methodology through which the system was developed is also disclosed as a promising approach to improve the efficiency and safety of the position verification process for existing systems. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： Radiotherapy

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Comparing the prediction performance of machine learning and deep learning methods for 3c-like protease cleavage sites 23

Comparing the prediction performance of machine learning and...

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7th International Conference on Medical and Health Informatics, ICMHI 2023

作者： Huang, Lan-Ying Chen, Chi-Wei Chang, Kai-Po Chu, Yen-Wei Medical Biotechnology National Chung Hsing University Taichung402 Taiwan Department of Pathology School of Medicine China Medical University Taichung402 Taiwan Department of Pathology China Medical University Hospital Taichung402 Taiwan Graduate Degree Program of Smart Healthcare and Bioinformatics I-Shou University Kaohsiung82445 Taiwan Department of Biomedical Engineering I-Shou University Kaohsiung82445 Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung402 Taiwan Institute of Molecular Biology National Chung Hsing University Taichung402 Taiwan Agricultural Biotechnology Center National Chung Hsing University Taichung402 Taiwan Biotechnology Center National Chung Hsing University Taichung402 Taiwan Translational Medicine National Chung Hsing University Taichung402 Taiwan Rong Hsing Research Center for Translational Medicine National Chung Hsing University Taichung402 Taiwan Taichung402 Taiwan

ISBN: (纸本)9798400700712

The impact of COVID-19 on the human body after infection requires further investigation. The protease carried by the virus may interact with cellular proteins, thereby affecting the regulatory pathways. Machine learning can provide predictive information to aid the research design in such studies. This study examined the effectiveness of different encoding methods for training models using traditional machine learning and deep learning methods. Using the advantages of deep learning methods for high-dimensional data analysis, amino acid properties were extracted and classified. Viral polyprotein data were used as the training set and human protein substrates were collected from the literature as the independent test set. In the one-dimensional convolutional neural network model, the accuracy of the independent test set was the highest, at 0.832. © 2023 ACM.

关键词： Coronavirus

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Using ensemble learning and hierarchical strategy to predict the outcomes of ESWL for upper ureteral stone treatment

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Computers in Biology and Medicine 2024年 179卷 108904-108904页

作者： Chen, Chi-Wei Liu, Wayne-Young Huang, Lan-Ying Chu, Yen-Wei Graduate Degree Program of Smart Healthcare & Bioinformatics I-Shou University Kaohsiung City Taiwan Department of Biomedical Engineering I-Shou University Kaohsiung City Taiwan Doctoral Program in Medical Biotechnology National Chung Hsing University Taichung City Taiwan Department of Urology Jen-Ai Hospital Taichung City Taiwan Graduate Institute of Genomics and Bioinformatics National Chung Hsing University Taichung City Taiwan Institute of Molecular Biology National Chung Hsing University Taichung City Taiwan Agricultural Biotechnology Center National Chung Hsing University Taichung City Taiwan Rong Hsing Research Center for Translational Medicine Taichung City Taiwan Ph. D Program in Medical Biotechnology National Chung Hsing University Taichung City Taiwan Taichung402 Taiwan

Urinary tract stones are a common and frequently recurring medical issue. Accurately predicting the success rate after surgery can help avoid ineffective medical procedures and reduce unnecessary healthcare costs. This study collected data from patients with upper ureter stones who underwent extracorporeal shock wave lithotripsy, including cases of successful as well as unsuccessful stone removal after the first and second lithotripsy procedures, and constructed prediction systems for the outcomes of the first and second lithotripsy procedures. Features were extracted from three categories of information: patient characteristics, stone characteristics, and extracorporeal shock wave lithotripsy machine data, and additional features were created using Feature Creation. Finally, the impact of features on the models was analyzed using six methods to calculate feature importance. Our prediction model for the first lithotripsy, selected from among 43 methods and seven ensemble learning techniques, achieves an AUC of 0.91. For the second lithotripsy, the AUC reaches 0.76. The results indicate that the detailed and binary information provided by patients regarding their history of stone experiences contributes differently to the predictive accuracy of the first and second lithotripsy procedures. The prediction tool is available at https://***/ks. © 2024

关键词： Machine learning

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