Takahiro Yamane, Kazuya Hirano, K. Hirai, D. Ousaka, Noriko Sakano, Mizuki Morita, Susumu Oozawa, S. Kasahara
{"title":"Trial of Sportswear Type ECG Sensor Device for Cardiac Safety Management during Marathon Running","authors":"Takahiro Yamane, Kazuya Hirano, K. Hirai, D. Ousaka, Noriko Sakano, Mizuki Morita, Susumu Oozawa, S. Kasahara","doi":"10.14326/abe.11.151","DOIUrl":"https://doi.org/10.14326/abe.11.151","url":null,"abstract":"","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66998956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Development of a System for Detecting Pulse Irregularities of Atrial Fibrillation from Palm Images Using Videoplethysmography","authors":"Shinichiro Okabe, Junichi Sugiyama, Takuya Kaihara","doi":"10.14326/abe.11.162","DOIUrl":"https://doi.org/10.14326/abe.11.162","url":null,"abstract":"","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66999303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Koji Yokoyama, Goshiro Yamamoto, Chang Liu, Osamu Sugiyama, Luciano H. O. Santos, T. Kuroda
{"title":"Recognition of Instrument Passing and Group Attention for Understanding Intraoperative State of Surgical Team","authors":"Koji Yokoyama, Goshiro Yamamoto, Chang Liu, Osamu Sugiyama, Luciano H. O. Santos, T. Kuroda","doi":"10.14326/abe.11.37","DOIUrl":"https://doi.org/10.14326/abe.11.37","url":null,"abstract":"","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66999569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Automatic Data Mapping for Interoperability of OpenEMR Medical Practice Management Software Using the Fast Healthcare Interoperability Resources","authors":"Hammam Mahfuzh Sujudi, Lukman Heryawan","doi":"10.14326/abe.11.186","DOIUrl":"https://doi.org/10.14326/abe.11.186","url":null,"abstract":"","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"28 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66999609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Shibata, S. Inada, K. Nakazawa, T. Ashihara, Naoki Tomii, M. Yamazaki, H. Honjo, Hiroshi Seno, I. Sakuma
{"title":"Mechanism of Ventricular Fibrillation: Current Status and Problems","authors":"N. Shibata, S. Inada, K. Nakazawa, T. Ashihara, Naoki Tomii, M. Yamazaki, H. Honjo, Hiroshi Seno, I. Sakuma","doi":"10.14326/abe.11.117","DOIUrl":"https://doi.org/10.14326/abe.11.117","url":null,"abstract":"","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66999130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Hosokawa, A. Naganawa, Takeshi Seki, K. Oka, N. Manabe, K. Haruma, J. Yoshino
{"title":"Development and Clinical Verification of a Small Intestine Motility Measurement System Using an Ileus Tube","authors":"S. Hosokawa, A. Naganawa, Takeshi Seki, K. Oka, N. Manabe, K. Haruma, J. Yoshino","doi":"10.14326/abe.11.228","DOIUrl":"https://doi.org/10.14326/abe.11.228","url":null,"abstract":"","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66999287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sinan Zhang, Dai Ito, Ryo Ogura, Takanori Tominaga, Y. Ono
{"title":"Acute Effect of Treadmill Walking under Optic Flow Stimulation on Gait Function in Individuals with Stroke and Healthy Controls","authors":"Sinan Zhang, Dai Ito, Ryo Ogura, Takanori Tominaga, Y. Ono","doi":"10.14326/abe.11.179","DOIUrl":"https://doi.org/10.14326/abe.11.179","url":null,"abstract":"","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66999460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. Nagaoka, T. Kozuka, Takahiro Yamada, H. Habe, M. Nemoto, M. Tada, K. Abe, H. Handa, Hisashi Yoshida, Kazunari Ishii, Yuichi Kimura
Objective: The objective of the current study was to develop a novel, artificial intelligence (AI)-based system to diagnose coronavirus disease (COVID-19) using computed tomography (CT) slice images. Prior research has demonstrated that, if not focused on the lungs, AI diagnoses COVID-19 using information outside the lungs. The inclusion of CT training data from multiple facilities and CT models may also cause AI to diagnose COVID-19 with features that are irrelevant to COVID-19. Thus, the objective of the current study was to evaluate a combination of lung mask images and CT slice images from a single facility, using a single CT model, and use AI to differentiate COVID-19 from other types of pneumonia based solely on information related to the lungs. Method: By superimposing lung mask images on image feature output using an existing AI structure, it was possible to exclude image features other than those around the lungs. The results of this model were also compared with the slice image findings from which only the lung region was extracted. The system adopted an ensemble approach. The outputs of multiple AIs were averaged to differentiate COVID-19 cases from other types of pneumonia, based on CT slice images. Results: The system evaluated 132 scans of COVID-19 cases and 62 scans of non-COVID-19 cases taken at the single facility using a single CT model. The initial sensitivity, specificity, and accuracy of our system, using a threshold value of 0.50, was shown to be 95%, 53%, and 81%, respectively. Setting the threshold value to 0.84 adjusted the sensitivity and specificity to clinically usable values of 76% and 84%, respectively. Conclusion: The system developed in the current study was able to differentiate between pneumonia due to COVID-19 and other types of pneumonia with sufficient accuracy for use in clinical practice. This was accomplished without the inclusion of images of clinically meaningless regions and despite the application of more stringent conditions, compared to prior studies.
{"title":"A Deep Learning System to Diagnose COVID-19 Pneumonia Using Masked Lung CT Images to Avoid AI-generated COVID-19 Diagnoses that Include Data outside the Lungs","authors":"T. Nagaoka, T. Kozuka, Takahiro Yamada, H. Habe, M. Nemoto, M. Tada, K. Abe, H. Handa, Hisashi Yoshida, Kazunari Ishii, Yuichi Kimura","doi":"10.14326/abe.11.76","DOIUrl":"https://doi.org/10.14326/abe.11.76","url":null,"abstract":"Objective: The objective of the current study was to develop a novel, artificial intelligence (AI)-based system to diagnose coronavirus disease (COVID-19) using computed tomography (CT) slice images. Prior research has demonstrated that, if not focused on the lungs, AI diagnoses COVID-19 using information outside the lungs. The inclusion of CT training data from multiple facilities and CT models may also cause AI to diagnose COVID-19 with features that are irrelevant to COVID-19. Thus, the objective of the current study was to evaluate a combination of lung mask images and CT slice images from a single facility, using a single CT model, and use AI to differentiate COVID-19 from other types of pneumonia based solely on information related to the lungs. Method: By superimposing lung mask images on image feature output using an existing AI structure, it was possible to exclude image features other than those around the lungs. The results of this model were also compared with the slice image findings from which only the lung region was extracted. The system adopted an ensemble approach. The outputs of multiple AIs were averaged to differentiate COVID-19 cases from other types of pneumonia, based on CT slice images. Results: The system evaluated 132 scans of COVID-19 cases and 62 scans of non-COVID-19 cases taken at the single facility using a single CT model. The initial sensitivity, specificity, and accuracy of our system, using a threshold value of 0.50, was shown to be 95%, 53%, and 81%, respectively. Setting the threshold value to 0.84 adjusted the sensitivity and specificity to clinically usable values of 76% and 84%, respectively. Conclusion: The system developed in the current study was able to differentiate between pneumonia due to COVID-19 and other types of pneumonia with sufficient accuracy for use in clinical practice. This was accomplished without the inclusion of images of clinically meaningless regions and despite the application of more stringent conditions, compared to prior studies.","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66999931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Satoshi Miura, Masaki Seki, Yuta Koreeda, Yang Cao, K. Kawamura, Yo Kobayashi, M. Fujie, T. Miyashita
{"title":"Virtual Shadow Drawing System Using Augmented Reality for Laparoscopic Surgery","authors":"Satoshi Miura, Masaki Seki, Yuta Koreeda, Yang Cao, K. Kawamura, Yo Kobayashi, M. Fujie, T. Miyashita","doi":"10.14326/abe.11.87","DOIUrl":"https://doi.org/10.14326/abe.11.87","url":null,"abstract":"","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"80 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67000013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Currently, the shortage of care workers for the elderly has become a big problem, and more streamlined care operations are needed. In care facilities, care workers are required to use their subjective experience to detect anomalies in physical condition of care receivers, including serious or insignificant deterioration or behavioral and psychological symptoms of dementia, which can decrease the work efficiency. Therefore, we aim to create a model using objective data for detecting anomalies in physical condition. In this study, data from 13 subjects in a care facility were collected, and isolation forest models were constructed for each subject. The subject ʼ s anomalies in physical condition were documented in a care record by a nurse and used as reference for model evaluation. Recall and specificity were used to evaluate the model, expressed as the per-centage of detection success for abnormal or normal conditions. Data collected for 1 to 60 days were used to train the isolation models, and the relationship between the amount of training data and model performance was simulated. Heart rate, respiratory rate, and time of getting out of bed were collected from a sensor placed on the subject ʼ s bed and used as the model features. In addition, dietary intake information was collected from the care record. Analysis of the evaluation results showed recall and specificity of 45.6 ± 46.7% and 83.88 ± 6.06%, re-spectively, for the model constructed using training data of 60 days. For future studies, we will continue to collect data and increase the number of participants to improve the robustness and accuracy of the proposed anomaly detection system.
{"title":"Research on an Anomaly Detection Method for Physical Condition Change of Elderly People in Care Facilities","authors":"Maho Shiotani, Katsuhisa Yamaguchi","doi":"10.14326/abe.11.10","DOIUrl":"https://doi.org/10.14326/abe.11.10","url":null,"abstract":"Currently, the shortage of care workers for the elderly has become a big problem, and more streamlined care operations are needed. In care facilities, care workers are required to use their subjective experience to detect anomalies in physical condition of care receivers, including serious or insignificant deterioration or behavioral and psychological symptoms of dementia, which can decrease the work efficiency. Therefore, we aim to create a model using objective data for detecting anomalies in physical condition. In this study, data from 13 subjects in a care facility were collected, and isolation forest models were constructed for each subject. The subject ʼ s anomalies in physical condition were documented in a care record by a nurse and used as reference for model evaluation. Recall and specificity were used to evaluate the model, expressed as the per-centage of detection success for abnormal or normal conditions. Data collected for 1 to 60 days were used to train the isolation models, and the relationship between the amount of training data and model performance was simulated. Heart rate, respiratory rate, and time of getting out of bed were collected from a sensor placed on the subject ʼ s bed and used as the model features. In addition, dietary intake information was collected from the care record. Analysis of the evaluation results showed recall and specificity of 45.6 ± 46.7% and 83.88 ± 6.06%, re-spectively, for the model constructed using training data of 60 days. For future studies, we will continue to collect data and increase the number of participants to improve the robustness and accuracy of the proposed anomaly detection system.","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66998403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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