Objective: Investigating consistent methods and metrics for classifying Detrusor Overactivity (DO) events and developing an automated robust method for clinical measurements calculation from cystometry data in persons with spinal cord injury (SCI).Methods and procedures: A two-stage method for was proposed to detect DO events. In the first stage, DO peaks were detected using local linear models combined with thresholding criteria derived from clinical definitions and known artifacts. In the second stage, a segmentation method was proposed to detect the start and end time points of each DO event, marking the DO activity periods. As a result, complete clinical measurements, including bladder compliance, can be estimated automatically. The method was developed and tested on 77 anonymized urodynamic samples from SCI individuals (40 DO-positive, 37 DO-negative) with 158 annotated DO events.Results: On test data, in terms of the patient-level diagnosis of DO, the proposed method achieved an accuracy of 100%. Individual DO event detection achieved an average precision of 0.94 and recall of 0.72. Detrusor activity period identification showed a precision of 0.86 and a recall of 0.88. The task of automated bladder compliance estimation showed that the point-value-based method yields a lower median absolute error (MAE) compared to the proposed line-fitting-based method, with a MAE of 5.20 and 7.14 ml/cmH2O, respectively. Finally, for classifying bladder function into normal, low and severely low compliance, the proposed method had an accuracy of 88%.Conclusion: Our proposed local model fitting with thresholding based on clinical knowledge, achieved accurate automated results for cytometry data, which will enable objective assessment of routinely performed examinations.Clinical and Translational Impact Statement—This work proposes a fully automated detrusor overactivity diagnosis and feature extraction method. Empowering medical teams to consistently assess urodynamic studies while aiding disease characterization and enhancing clinical decision-making for SCI patients. Furthermore, it provides a mathematically defined method for extending the pipeline to other populations and standardizing clinical assessments.Category: Clinical Engineering, Medical Devices and Systems.
{"title":"Automated Evaluation of Urodynamic Examinations Through Local Linear Models: Validation on Spinal Cord Injury Individuals","authors":"Wensi Zhang;Jürgen Pannek;Jens Wöllner;Robert Riener;Diego Paez-Granados","doi":"10.1109/JTEHM.2025.3544486","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3544486","url":null,"abstract":"Objective: Investigating consistent methods and metrics for classifying Detrusor Overactivity (DO) events and developing an automated robust method for clinical measurements calculation from cystometry data in persons with spinal cord injury (SCI).Methods and procedures: A two-stage method for was proposed to detect DO events. In the first stage, DO peaks were detected using local linear models combined with thresholding criteria derived from clinical definitions and known artifacts. In the second stage, a segmentation method was proposed to detect the start and end time points of each DO event, marking the DO activity periods. As a result, complete clinical measurements, including bladder compliance, can be estimated automatically. The method was developed and tested on 77 anonymized urodynamic samples from SCI individuals (40 DO-positive, 37 DO-negative) with 158 annotated DO events.Results: On test data, in terms of the patient-level diagnosis of DO, the proposed method achieved an accuracy of 100%. Individual DO event detection achieved an average precision of 0.94 and recall of 0.72. Detrusor activity period identification showed a precision of 0.86 and a recall of 0.88. The task of automated bladder compliance estimation showed that the point-value-based method yields a lower median absolute error (MAE) compared to the proposed line-fitting-based method, with a MAE of 5.20 and 7.14 ml/cmH2O, respectively. Finally, for classifying bladder function into normal, low and severely low compliance, the proposed method had an accuracy of 88%.Conclusion: Our proposed local model fitting with thresholding based on clinical knowledge, achieved accurate automated results for cytometry data, which will enable objective assessment of routinely performed examinations.<bold><i>Clinical and Translational Impact Statement—</i></b>This work proposes a fully automated detrusor overactivity diagnosis and feature extraction method. Empowering medical teams to consistently assess urodynamic studies while aiding disease characterization and enhancing clinical decision-making for SCI patients. Furthermore, it provides a mathematically defined method for extending the pipeline to other populations and standardizing clinical assessments.Category: Clinical Engineering, Medical Devices and Systems.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"111-122"},"PeriodicalIF":3.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10897996","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pilots face complex working environments during flight missions, which can easily lead to excessive workload and affect flight safety. Physiological signals are commonly used to evaluate a pilot’s workload because they are objective and can directly reflect physiological mental states. However, existing methods have shortcomings in temporal modeling, making it challenging to fully capture the dynamic characteristics of physiological signals over time. Moreover, fusing features of data from different modalities is also difficult.To address these problems, we proposed a temporal relation modeling and multimodal adversarial alignment network (TRM-MAAN) for pilot workload evaluation. Specifically, a Transformer-based temporal relationship modeling module was used to learn complex temporal relationships for better feature extraction. In addition, an adversarial alignment-based multi-modal fusion module was applied to capture and integrate multi-modal information, reducing distribution shifts between different modalities. The performance of the proposed TRM-MAAN method was evaluated via experiments of classifying three workload states using electroencephalogram (EEG) and electromyography (EMG) recordings of eight healthy pilots.Experimental results showed that the classification accuracy and F1 score of the proposed method were significantly better than the baseline model across different subjects, with an average recognition accuracy of $91.90~pm ~1.72%$ and an F1 score of $91.86~pm ~1.75%$ .This work provides essential technical support for improving the accuracy and robustness of pilot workload evaluation and introduces a promising way for enhancing flight safety, offering broad application prospects. Clinical and Translational Impact Statement: The proposed scheme provides a promising solution for workload evaluation based on electrophysiological signals, with potential applications in aiding the clinical monitoring of fatigue, mental status, cognitive psychology, and other disorders.
{"title":"Temporal Relation Modeling and Multimodal Adversarial Alignment Network for Pilot Workload Evaluation","authors":"Xinhui Li;Ao Li;Wenyu Fu;Xun Song;Fan Li;Qiang Ma;Yong Peng;Zhao LV","doi":"10.1109/JTEHM.2025.3542408","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3542408","url":null,"abstract":"Pilots face complex working environments during flight missions, which can easily lead to excessive workload and affect flight safety. Physiological signals are commonly used to evaluate a pilot’s workload because they are objective and can directly reflect physiological mental states. However, existing methods have shortcomings in temporal modeling, making it challenging to fully capture the dynamic characteristics of physiological signals over time. Moreover, fusing features of data from different modalities is also difficult.To address these problems, we proposed a temporal relation modeling and multimodal adversarial alignment network (TRM-MAAN) for pilot workload evaluation. Specifically, a Transformer-based temporal relationship modeling module was used to learn complex temporal relationships for better feature extraction. In addition, an adversarial alignment-based multi-modal fusion module was applied to capture and integrate multi-modal information, reducing distribution shifts between different modalities. The performance of the proposed TRM-MAAN method was evaluated via experiments of classifying three workload states using electroencephalogram (EEG) and electromyography (EMG) recordings of eight healthy pilots.Experimental results showed that the classification accuracy and F1 score of the proposed method were significantly better than the baseline model across different subjects, with an average recognition accuracy of <inline-formula> <tex-math>$91.90~pm ~1.72%$ </tex-math></inline-formula> and an F1 score of <inline-formula> <tex-math>$91.86~pm ~1.75%$ </tex-math></inline-formula>.This work provides essential technical support for improving the accuracy and robustness of pilot workload evaluation and introduces a promising way for enhancing flight safety, offering broad application prospects. Clinical and Translational Impact Statement: The proposed scheme provides a promising solution for workload evaluation based on electrophysiological signals, with potential applications in aiding the clinical monitoring of fatigue, mental status, cognitive psychology, and other disorders.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"85-97"},"PeriodicalIF":3.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10890988","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Objective: Mild cognitive impairment (MCI) is characterized by early symptoms of attentional decline and may be distinguished through motor learning results. A relationship was reported between dexterous hand movements and cognitive function in older adults. Therefore, this study focuses on motor learning involving dexterous hand movements. As motor learning engages two distinct types of attention, external and internal, we aimed to develop an evaluation method that separates these attentional functions within motor learning. The objective of this study was to develop and verify the effectiveness of this evaluation method. The effectiveness was assessed by comparing two motor learning variables between a normal cognitive (NC) and MCI groups. Method: To evaluate motor learning through dexterous hand movements, we utilized the iWakka device. Two types of visual tracking tasks, repeat and random, were designed to evaluate motor learning from different aspects. The tracking errors in both tasks were quantitatively measured, and the initial and final improvement rates during motor learning were defined as the evaluation variables. The study included 28 MCI participants and 40 NC participants, and the effectiveness of the proposed method was verified by comparing results between the groups. Results: The repeat task revealed a significantly lower learning rate in MCI participants (p <0.01). In contrast, no significant difference was observed between MCI and NC participants in the random task (p =0.67). Conclusion: The evaluation method proposed in this study demonstrated the possibility of obtaining evaluation variables that indicate the characteristics of MCI. Clinical Impact: The methods proposed in this work are clinically relevant because the proposed evaluation system can make evaluation variables for discriminating cognitive decline in MCI. That it, the proposed approach can also be used to provide discrimination for cognitive decline in MCI.
{"title":"Quantification of Motor Learning in Hand Adjustability Movements: An Evaluation Variable for Discriminant Cognitive Decline","authors":"Kazuya Toshima;Yu Chokki;Toshiaki Wasaka;Tsukasa Tamaru;Yoshifumi Morita","doi":"10.1109/JTEHM.2025.3540203","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3540203","url":null,"abstract":"Objective: Mild cognitive impairment (MCI) is characterized by early symptoms of attentional decline and may be distinguished through motor learning results. A relationship was reported between dexterous hand movements and cognitive function in older adults. Therefore, this study focuses on motor learning involving dexterous hand movements. As motor learning engages two distinct types of attention, external and internal, we aimed to develop an evaluation method that separates these attentional functions within motor learning. The objective of this study was to develop and verify the effectiveness of this evaluation method. The effectiveness was assessed by comparing two motor learning variables between a normal cognitive (NC) and MCI groups. Method: To evaluate motor learning through dexterous hand movements, we utilized the iWakka device. Two types of visual tracking tasks, repeat and random, were designed to evaluate motor learning from different aspects. The tracking errors in both tasks were quantitatively measured, and the initial and final improvement rates during motor learning were defined as the evaluation variables. The study included 28 MCI participants and 40 NC participants, and the effectiveness of the proposed method was verified by comparing results between the groups. Results: The repeat task revealed a significantly lower learning rate in MCI participants (p <0.01). In contrast, no significant difference was observed between MCI and NC participants in the random task (p =0.67). Conclusion: The evaluation method proposed in this study demonstrated the possibility of obtaining evaluation variables that indicate the characteristics of MCI. Clinical Impact: The methods proposed in this work are clinically relevant because the proposed evaluation system can make evaluation variables for discriminating cognitive decline in MCI. That it, the proposed approach can also be used to provide discrimination for cognitive decline in MCI.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"75-84"},"PeriodicalIF":3.7,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10879071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-29DOI: 10.1109/JTEHM.2025.3536441
Yuhao Tang;Ye Yuan;Fei Tao;Minghao Tang
In clinical practice, interpreting medical images and composing diagnostic reports typically involve significant manual workload. Therefore, an automated report generation framework that mimics a doctor’s diagnosis better meets the requirements of medical scenarios. Prior investigations often overlook this critical aspect, primarily relying on traditional image captioning frameworks initially designed for general-domain images and sentences. Despite achieving some advancements, these methodologies encounter two primary challenges. First, the strong noise in blurred medical images always hinders the model of capturing the lesion region. Second, during report writing, doctors typically rely on terminology for diagnosis, a crucial aspect that has been neglected in prior frameworks. In this paper, we present a novel approach called Cross-modal Augmented Transformer (CAT) for medical report generation. Unlike previous methods that rely on coarse-grained features without human intervention, our method introduces a “locate then generate” pattern, thereby improving the interpretability of the generated reports. During the locate stage, CAT captures crucial representations by pre-aligning significant patches and their corresponding medical terminologies. This pre-alignment helps reduce visual noise by discarding low-ranking content, ensuring that only relevant information is considered in the report generation process. During the generation phase, CAT utilizes a multi-modality encoder to reinforce the correlation between generated keywords, retrieved terminologies and regions. Furthermore, CAT employs a dual-stream decoder that dynamically determines whether the predicted word should be influenced by the retrieved terminology or the preceding sentence. Experimental results demonstrate the effectiveness of the proposed method on two datasets.Clinical impact: This work aims to design an automated framework for explaining medical images to evaluate the health status of individuals, thereby facilitating their broader application in clinical settings.Clinical and Translational Impact Statement: In our preclinical research, we develop an automated system for generating diagnostic reports. This system mimics manual diagnostic methods by combining fine-grained semantic alignment with dual-stream decoders.
{"title":"Cross-Modal Augmented Transformer for Automated Medical Report Generation","authors":"Yuhao Tang;Ye Yuan;Fei Tao;Minghao Tang","doi":"10.1109/JTEHM.2025.3536441","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3536441","url":null,"abstract":"In clinical practice, interpreting medical images and composing diagnostic reports typically involve significant manual workload. Therefore, an automated report generation framework that mimics a doctor’s diagnosis better meets the requirements of medical scenarios. Prior investigations often overlook this critical aspect, primarily relying on traditional image captioning frameworks initially designed for general-domain images and sentences. Despite achieving some advancements, these methodologies encounter two primary challenges. First, the strong noise in blurred medical images always hinders the model of capturing the lesion region. Second, during report writing, doctors typically rely on terminology for diagnosis, a crucial aspect that has been neglected in prior frameworks. In this paper, we present a novel approach called Cross-modal Augmented Transformer (CAT) for medical report generation. Unlike previous methods that rely on coarse-grained features without human intervention, our method introduces a “locate then generate” pattern, thereby improving the interpretability of the generated reports. During the locate stage, CAT captures crucial representations by pre-aligning significant patches and their corresponding medical terminologies. This pre-alignment helps reduce visual noise by discarding low-ranking content, ensuring that only relevant information is considered in the report generation process. During the generation phase, CAT utilizes a multi-modality encoder to reinforce the correlation between generated keywords, retrieved terminologies and regions. Furthermore, CAT employs a dual-stream decoder that dynamically determines whether the predicted word should be influenced by the retrieved terminology or the preceding sentence. Experimental results demonstrate the effectiveness of the proposed method on two datasets.Clinical impact: This work aims to design an automated framework for explaining medical images to evaluate the health status of individuals, thereby facilitating their broader application in clinical settings.Clinical and Translational Impact Statement: In our preclinical research, we develop an automated system for generating diagnostic reports. This system mimics manual diagnostic methods by combining fine-grained semantic alignment with dual-stream decoders.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"33-48"},"PeriodicalIF":3.7,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10857391","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The high volume of emergency room patients often necessitates head CT examinations to rule out ischemic, hemorrhagic, or other organic pathologies. A system that enhances the diagnostic efficacy of head CT imaging in emergency settings through structured reporting would significantly improve clinical decision making. Currently, no AI solutions address this need. Thus, our research aims to develop an automatic radiology reporting system by directly analyzing brain anomalies in head CT data. We propose a multi-branch CNN-LSTM fusion network-driven system for enhanced radiology reporting in emergency settings. We preprocessed head CT scans by resizing all slices, selecting those with significant variability, and applying PCA to retain 95% of the original data variance, ultimately saving the most representative five slices for each scan. We linked the reports to their respective slice IDs, divided them into individual captions, and preprocessed each. We performed an 80-20 split of the dataset for ten times, with 15% of the training set used for validation. Our model utilizes a pretrained VGG16, processing groups of five slices simultaneously, and features multiple end-to-end LSTM branches, each specialized in predicting one caption, subsequently combined to form the ordered reports after a BERT-based semantic evaluation. Our system demonstrates effectiveness and stability, with the postprocessing stage refining the syntax of the generated descriptions. However, there remains an opportunity to empower the evaluation framework to more accurately assess the clinical relevance of the automatically-written reports. Part of future work will include transitioning to 3D and developing an improved version based on vision-language models.
{"title":"Multi-Branch CNN-LSTM Fusion Network-Driven System With BERT Semantic Evaluator for Radiology Reporting in Emergency Head CTs","authors":"Selene Tomassini;Damiano Duranti;Abdallah Zeggada;Carlo Cosimo Quattrocchi;Farid Melgani;Paolo Giorgini","doi":"10.1109/JTEHM.2025.3535676","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3535676","url":null,"abstract":"The high volume of emergency room patients often necessitates head CT examinations to rule out ischemic, hemorrhagic, or other organic pathologies. A system that enhances the diagnostic efficacy of head CT imaging in emergency settings through structured reporting would significantly improve clinical decision making. Currently, no AI solutions address this need. Thus, our research aims to develop an automatic radiology reporting system by directly analyzing brain anomalies in head CT data. We propose a multi-branch CNN-LSTM fusion network-driven system for enhanced radiology reporting in emergency settings. We preprocessed head CT scans by resizing all slices, selecting those with significant variability, and applying PCA to retain 95% of the original data variance, ultimately saving the most representative five slices for each scan. We linked the reports to their respective slice IDs, divided them into individual captions, and preprocessed each. We performed an 80-20 split of the dataset for ten times, with 15% of the training set used for validation. Our model utilizes a pretrained VGG16, processing groups of five slices simultaneously, and features multiple end-to-end LSTM branches, each specialized in predicting one caption, subsequently combined to form the ordered reports after a BERT-based semantic evaluation. Our system demonstrates effectiveness and stability, with the postprocessing stage refining the syntax of the generated descriptions. However, there remains an opportunity to empower the evaluation framework to more accurately assess the clinical relevance of the automatically-written reports. Part of future work will include transitioning to 3D and developing an improved version based on vision-language models.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"61-74"},"PeriodicalIF":3.7,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10856282","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-22DOI: 10.1109/JTEHM.2025.3532801
Hsiu-Lin Chen;Bor-Shing Lin;Chieh-Miao Chang;Hao-Wei Chung;Shu-Ting Yang;Bor-Shyh Lin
High-risk infants in the neonatal intensive care unit often encounter the problems with hemodynamic instability, and the poor blood circulation may cause shock or other sequelae. But the appearance of shock is not easy to be noticed in the initial stage, and most of the clinical judgments are subjectively dependent on the experienced physicians. Therefore, how to effectively evaluate the neonatal blood circulation state is important for the treatment in time. Although some instruments, such as laser Doppler flow meter, can estimate the information of blood flow, there is still lack of monitoring systems to evaluate the neonatal blood circulation directly. Based on the technique of near-infrared spectroscopy, an intelligent neonatal blood perfusion assessment system was proposed in this study, to monitor the changes of hemoglobin concentration and tissue oxygen saturation simultaneously and further estimate the neonatal blood perfusion. Several indexes were defined from the changes of hemoglobin parameters under applying and relaxing pressure to obtain the neonatal perfusion information. Moreover, the neural network-based classifier was also used to effectively classify the groups with different blood perfusion states. From the experimental results, the difference between the groups with different blood perfusion states could exactly be reflected on several defined indexes and could be effectively recognized by using the technique of neural network. Clinical and Translational Impact Statement—An intelligent neonatal blood perfusion assessment system was proposed to monitor the changes of hemoglobin concentration and tissue oxygen saturation simultaneously and further estimate the neonatal blood perfusion (Category: Preclinical Research)
{"title":"Intelligent Neonatal Blood Perfusion Assessment System Based on Near-Infrared Spectroscopy","authors":"Hsiu-Lin Chen;Bor-Shing Lin;Chieh-Miao Chang;Hao-Wei Chung;Shu-Ting Yang;Bor-Shyh Lin","doi":"10.1109/JTEHM.2025.3532801","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3532801","url":null,"abstract":"High-risk infants in the neonatal intensive care unit often encounter the problems with hemodynamic instability, and the poor blood circulation may cause shock or other sequelae. But the appearance of shock is not easy to be noticed in the initial stage, and most of the clinical judgments are subjectively dependent on the experienced physicians. Therefore, how to effectively evaluate the neonatal blood circulation state is important for the treatment in time. Although some instruments, such as laser Doppler flow meter, can estimate the information of blood flow, there is still lack of monitoring systems to evaluate the neonatal blood circulation directly. Based on the technique of near-infrared spectroscopy, an intelligent neonatal blood perfusion assessment system was proposed in this study, to monitor the changes of hemoglobin concentration and tissue oxygen saturation simultaneously and further estimate the neonatal blood perfusion. Several indexes were defined from the changes of hemoglobin parameters under applying and relaxing pressure to obtain the neonatal perfusion information. Moreover, the neural network-based classifier was also used to effectively classify the groups with different blood perfusion states. From the experimental results, the difference between the groups with different blood perfusion states could exactly be reflected on several defined indexes and could be effectively recognized by using the technique of neural network. Clinical and Translational Impact Statement—An intelligent neonatal blood perfusion assessment system was proposed to monitor the changes of hemoglobin concentration and tissue oxygen saturation simultaneously and further estimate the neonatal blood perfusion (Category: Preclinical Research)","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"23-32"},"PeriodicalIF":3.7,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10849653","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-14DOI: 10.1109/JTEHM.2025.3529748
Saurabh Jain;Bijoy Dripta Barua Chowdhury;Jarrod M. Mosier;Vignesh Subbian;Kate Hughes;Young-Jun Son
For over 40 years, airway management simulation has been a cornerstone of medical training, aiming to reduce procedural risks for critically ill patients. However, existing simulation technologies often lack the versatility and realism needed to replicate the cognitive and physical challenges of complex airway management scenarios. We developed a novel Virtual Reality (VR)-based simulation system designed to enhance immersive airway management training and research. This system integrates physical and virtual environments with an external sensory framework to capture high-fidelity data on user performance. Advanced calibration techniques ensure precise positional tracking and realistic physics-based interactions, providing a cohesive mixed-reality experience. Validation studies conducted in a dedicated medical training center demonstrated the system’s effectiveness in replicating real-world conditions. Positional calibration accuracy was achieved within 0.1 cm, with parameter calibrations showing no significant discrepancies. Validation using Pre- and post-simulation surveys indicated positive feedback on training aspects, perceived usefulness, and ease of use. These results suggest that the system offers a significant improvement in procedural and cognitive training for high-stakes medical environments.
{"title":"Design and Development of an Integrated Virtual Reality (VR)-Based Training System for Difficult Airway Management","authors":"Saurabh Jain;Bijoy Dripta Barua Chowdhury;Jarrod M. Mosier;Vignesh Subbian;Kate Hughes;Young-Jun Son","doi":"10.1109/JTEHM.2025.3529748","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3529748","url":null,"abstract":"For over 40 years, airway management simulation has been a cornerstone of medical training, aiming to reduce procedural risks for critically ill patients. However, existing simulation technologies often lack the versatility and realism needed to replicate the cognitive and physical challenges of complex airway management scenarios. We developed a novel Virtual Reality (VR)-based simulation system designed to enhance immersive airway management training and research. This system integrates physical and virtual environments with an external sensory framework to capture high-fidelity data on user performance. Advanced calibration techniques ensure precise positional tracking and realistic physics-based interactions, providing a cohesive mixed-reality experience. Validation studies conducted in a dedicated medical training center demonstrated the system’s effectiveness in replicating real-world conditions. Positional calibration accuracy was achieved within 0.1 cm, with parameter calibrations showing no significant discrepancies. Validation using Pre- and post-simulation surveys indicated positive feedback on training aspects, perceived usefulness, and ease of use. These results suggest that the system offers a significant improvement in procedural and cognitive training for high-stakes medical environments.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"49-60"},"PeriodicalIF":3.7,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10841389","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Methamphetamine use disorder (MUD) is a substance use disorder. Because MUD has become more prevalent due to the COVID-19 pandemic, alternative ways to help the efficiency of mass screening of MUD are important. Previous studies used electroencephalogram (EEG), heart rate variability (HRV), and galvanic skin response (GSR) aberrations during the virtual reality (VR) induction of drug craving to accurately separate patients with MUD from the healthy controls. However, whether these abnormalities present without induction of drug-cue reactivity to enable separation between patients and healthy subjects remains unclear. Here, we propose a clinically comparable intelligent system using the fusion of 5–channel EEG, HRV, and GSR data during resting state to aid in detecting MUD. Forty-six patients with MUD and 26 healthy controls were recruited and machine learning methods were employed to systematically compare the classification results of different fusion models. The analytic results revealed that the fusion of HRV and GSR features leads to the most accurate separation rate of 79%. The use of EEG, HRV, and GSR features provides more robust information, leading to relatively similar and enhanced accuracy across different classifiers. In conclusion, we demonstrated that a clinically applicable intelligent system using resting-state EEG, ECG, and GSR features without the induction of drug cue reactivity enhances the detection of MUD. This system is easy to implement in the clinical setting and can save a lot of time on setting up and experimenting while maintaining excellent accuracy to assist in mass screening of MUD.
甲基苯丙胺使用障碍（Methamphetamine use disorder， MUD）是一种物质使用障碍。由于COVID-19大流行使MUD变得更加普遍，因此提高MUD大规模筛查效率的替代方法非常重要。先前的研究使用虚拟现实（VR）诱导药物渴望时的脑电图（EEG）、心率变异性（HRV）和皮肤电反应（GSR）畸变来准确区分MUD患者和健康对照组。然而，这些异常是否没有引起药物提示反应，从而使患者与健康受试者分离，目前尚不清楚。在这里，我们提出了一种临床可比较的智能系统，该系统使用静息状态下的5通道EEG， HRV和GSR数据融合来帮助检测MUD。选取46例MUD患者和26例健康对照者，采用机器学习方法系统比较不同融合模型的分类结果。分析结果表明，HRV和GSR特征的融合使分离准确率达到79%。EEG、HRV和GSR特征的使用提供了更鲁棒的信息，导致不同分类器之间相对相似和提高的准确性。总之，我们证明了一个临床适用的智能系统，利用静息状态EEG， ECG和GSR特征，而不诱导药物线索反应性，可以增强对MUD的检测。该系统易于在临床环境中实施，可以节省大量的设置和实验时间，同时保持良好的准确性，以协助大规模筛查MUD。
{"title":"Fusion Model Using Resting Neurophysiological Data to Help Mass Screening of Methamphetamine Use Disorder","authors":"Chun-Chuan Chen;Meng-Chang Tsai;Eric Hsiao-Kuang Wu;Shao-Rong Sheng;Jia-Jeng Lee;Yung-En Lu;Shih-Ching Yeh","doi":"10.1109/JTEHM.2024.3522356","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3522356","url":null,"abstract":"Methamphetamine use disorder (MUD) is a substance use disorder. Because MUD has become more prevalent due to the COVID-19 pandemic, alternative ways to help the efficiency of mass screening of MUD are important. Previous studies used electroencephalogram (EEG), heart rate variability (HRV), and galvanic skin response (GSR) aberrations during the virtual reality (VR) induction of drug craving to accurately separate patients with MUD from the healthy controls. However, whether these abnormalities present without induction of drug-cue reactivity to enable separation between patients and healthy subjects remains unclear. Here, we propose a clinically comparable intelligent system using the fusion of 5–channel EEG, HRV, and GSR data during resting state to aid in detecting MUD. Forty-six patients with MUD and 26 healthy controls were recruited and machine learning methods were employed to systematically compare the classification results of different fusion models. The analytic results revealed that the fusion of HRV and GSR features leads to the most accurate separation rate of 79%. The use of EEG, HRV, and GSR features provides more robust information, leading to relatively similar and enhanced accuracy across different classifiers. In conclusion, we demonstrated that a clinically applicable intelligent system using resting-state EEG, ECG, and GSR features without the induction of drug cue reactivity enhances the detection of MUD. This system is easy to implement in the clinical setting and can save a lot of time on setting up and experimenting while maintaining excellent accuracy to assist in mass screening of MUD.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"1-8"},"PeriodicalIF":3.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10816093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-13DOI: 10.1109/JTEHM.2024.3516335
{"title":"IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE","authors":"","doi":"10.1109/JTEHM.2024.3516335","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3516335","url":null,"abstract":"","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"C3-C3"},"PeriodicalIF":3.7,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10799104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-13DOI: 10.1109/JTEHM.2024.3513733
{"title":">IEEE Journal on Translational Engineering in Medicine and Biology publication information","authors":"","doi":"10.1109/JTEHM.2024.3513733","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3513733","url":null,"abstract":"","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"C2-C2"},"PeriodicalIF":3.7,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10799063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：