IEEE Journal of Translational Engineering in Health and Medicine-Jtehm最新文献

{"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}

{"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}

{"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}

{"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}

{"title":"List of Reviewers","authors":"","doi":"10.1109/JTEHM.2024.3507892","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3507892","url":null,"abstract":"","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"739-739"},"PeriodicalIF":3.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10794571","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810324","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}

{"title":"MRI-Driven Longitudinal Studies of Hippocampal Alterations During the Initial Cognitive Decline","authors":"Qunxi Dong;Yuhang Sheng;Junru Zhu;Honghong Liu;Zhigang Li;Jingyu Liu;Yalin Wang;Bin Hu","doi":"10.1109/JTEHM.2024.3510429","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3510429","url":null,"abstract":"Based on available magnetic resonance imaging (MRI) studies, hippocampal alteration is one of the hallmarks during cognitive decline. However, the longitudinal hippocampal morphometric changes during the initial cognitive decline are unclear. Exploring a validated biomarker with high clinical relevance is urgent. This work proposed an automated MRI-driven longitudinal hippocampal alteration analysis system (LHAAS), which consists of hippocampal segmentation, reconstruction, registration, multivariate morphometric feature extraction, and longitudinal analysis of hippocampal morphometric and volumetric differences between groups. LHAAS was applied on two groups: cognitive unimpaired (CU) participants who maintained cognitive unimpaired (non-Progressors), and participants who converted to MCI during the following four years (Progressors). LHAAS can detect and visualize subtle deformations in the bilateral hippocampus of CU progressors four years before they show initial cognitive decline. For CU progressors, hippocampal atrophy initially occurs at the CA1 subregion and then along with disease progression, spreading to the CA2-3 and Subiculum subregion, exhibiting a left-greater-than-right trend. The volumetric analyses showed similar results. Besides, hippocampal subregions highly correlated with clinical measurement were identified by correlation analysis. LHAAS can accurately reflect the small hippocampal subregional atrophy at preclinical AD. This proposed system can track the longitudinal hippocampal alterations in the early stages of AD and provide insights for early intervention. Clinical and Translational Impact Statement: LHAAS offers early detection of subtle hippocampal alterations at preclinical AD. This advance enables pathological research and timely interventions to potentially improve patient outcomes in clinical implementation.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"98-110"},"PeriodicalIF":3.7,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772627","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553306","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}

{"title":"Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA","authors":"Lok Hua Lee;Cyrus Su Hui Ho;Yee Ling Chan;Gabrielle Wann Nii Tay;Cheng-Kai Lu;Tong Boon Tang","doi":"10.1109/JTEHM.2024.3506556","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3506556","url":null,"abstract":"While functional near-infrared spectroscopy (fNIRS) had previously been suggested for major depressive disorder (MDD) diagnosis, the clinical application to predict antidepressant treatment response (ATR) is still unclear. To address this, the aim of the current study is to investigate MDD ATR at three response levels using fNIRS and micro-ribonucleic acids (miRNAs). Our proposed algorithm includes a custom inter-subject variability reduction based on the principal component analysis (PCA). The principal components of extracted features are first identified for non-responders’ group. The first few components that sum up to 99% of explained variance are discarded to minimize inter-subject variability while the remaining projection vectors are applied on all response groups (24 non-responders, 15 partial-responders, 13 responders) to obtain their relative projections in feature space. The entire algorithm achieved a better performance through the radial basis function (RBF) support vector machine (SVM), with 82.70% accuracy, 78.44% sensitivity, 86.15% precision, and 91.02% specificity, respectively, when compared with conventional machine learning approaches that combine clinical, sociodemographic and genetic information as the predictor. The performance of the proposed custom algorithm suggests the prediction of ATR can be improved with multiple features sources, provided that the inter-subject variability is properly addressed, and can be an effective tool for clinical decision support system in MDD ATR prediction. Clinical and Translational Impact Statement—The fusion of neuroimaging fNIRS features and miRNA profiles significantly enhances the prediction accuracy of MDD ATR. The minimally required features also make the personalized medicine more practical and realizable","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"9-22"},"PeriodicalIF":3.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10767732","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993375","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}

{"title":"Laminar Fluid Ejection for Olfactory Drug Delivery: A Proof of Concept Study","authors":"Thomas M. Morin;Nick Allan;Joshua Coutts;Jacob M. Hooker;Morgan Langille;Arron Metcalfe;Andrew Thamboo;James Jackson;Manu Sharma;Tim Rees;Kenza Enright;Ken Irving","doi":"10.1109/JTEHM.2024.3503498","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3503498","url":null,"abstract":"Focal intranasal drug delivery to the olfactory cleft is a promising avenue for pharmaceuticals targeting the brain. However, traditional nasal sprays often fail to deliver enough medication to this specific area. We present a laminar fluid ejection (LFE) method for precise delivery of medications to the olfactory cleft. Using a 3D-printed model of the nasal passages, we determined the precise velocity and angle of insertion needed to deposit fluid at the olfactory cleft. Then, we conducted three proof-of-concept in-vivo imaging studies to confirm olfactory delivery in humans. First, we used Technetium-99 (a radiolabeled tracer) and methylene blue (a laboratory-made dye) to visualize olfactory deposition. Both tracers showed successful deposition. In a separate study, we used functional MRI (fMRI), to compare our LFE method with a conventional nasal spray while delivering insulin. From the fMRI results, we qualitatively observed focal decreases in brain activity in prefrontal cortex following insulin delivery. Overall, these preliminary results suggest that LFE offers a targeted approach to olfactory drug delivery, opening opportunities for access to the brain.Clinical and Translational Impact Statement - Focal deposition at the olfactory cleft is a promising target for delivering medication to the brain. We present in-human tests of a laminar fluid ejection method for intranasal drug delivery and demonstrate improvements over conventional nasal spray.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"727-738"},"PeriodicalIF":3.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10759073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777614","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}

{"title":"Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients","authors":"Farnaz Khodami;Amanda S. Mahoney;James L. Coyle;Ervin Sejdić","doi":"10.1109/JTEHM.2024.3497895","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3497895","url":null,"abstract":"Patients with nasogastric (NG) tubes require careful monitoring due to the potential impact of the tube on their ability to swallow safely. This study aimed to investigate the utility of high-resolution cervical auscultation (HRCA) signals in assessing swallowing functionality of patients using feeding tubes. HRCA, capturing swallowing vibratory and acoustic signals, has been explored as a surrogate for videofluoroscopy image analysis in previous research. In this study, we analyzed HRCA signals recorded from patients with NG tubes to identify swallowing kinematic events within this group of subjects. Machine learning architectures from prior research endeavors, originally designed for participants without NG tubes, were fine-tuned to accomplish three tasks in the target population: estimating the duration of upper esophageal sphincter opening, estimating the duration of laryngeal vestibule closure, and tracking the hyoid bone. The convolutional recurrent neural network proposed for the first task predicted the onset of upper esophageal sphincter opening and closure for 67.61% and 82.95% of patients, respectively, with an error margin of fewer than three frames. The hybrid model employed for the second task successfully predicted the onset of laryngeal vestibule closure and reopening for 79.62% and 75.80% of patients, respectively, with the same error margin. The stacked recurrent neural network identified hyoid bone position in test frames, achieving a 41.27% overlap with ground-truth outputs. By applying established algorithms to an unseen population, we demonstrated the utility of HRCA signals for swallowing assessment in individuals with NG tubes and showcased the generalizability of algorithms developed in our previous studies. Clinical impact: This study highlights the promise of HRCA signals for assessing swallowing in patients with NG tubes, potentially improving diagnosis, management, and care integration in both clinical and home healthcare settings.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"711-720"},"PeriodicalIF":3.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10752547","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713771","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}

{"title":"Non-Contact Monitoring of Inhalation-Exhalation (I:E) Ratio in Non-Ventilated Subjects","authors":"Paul S. Addison;Andre Antunes;Dean Montgomery;Ulf R. Borg","doi":"10.1109/JTEHM.2024.3496196","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3496196","url":null,"abstract":"The inhalation-exhalation (I:E) ratio, known to be an indicator of respiratory disease, is the ratio between the inhalation phase and exhalation phase of each breath. Here, we report on results from a non-contact monitoring method for the determination of the I:E ratio. This employs a depth sensing camera system that requires no sensors to be physically attached to the patient. A range of I:E ratios from 0.3 to 1.0 over a range of respiratory rates from 4 to 40 breaths/min were generated by healthy volunteers, producing a total of 3,882 separate breaths for analysis. Depth information was acquired using an Intel D415 RealSense camera placed at 1.1 m from the subjects’ torso. This data was processed in real-time to extract depth changes within the subjects’ torso region corresponding to respiratory activity. This was further converted into a respiratory signal from which the I:E ratio was determined (I:E\u0000<inline-formula> <tex-math>$_{mathrm {depth}}$ </tex-math></inline-formula>\u0000). I:Edepth was compared to spirometer data (I:E\u0000<inline-formula> <tex-math>$_{mathrm {spiro}}$ </tex-math></inline-formula>\u0000). A Bland Altman analysis produced a mean bias of –0.004, with limits of agreement [–0.234, 0.227]. A linear regression analysis produced a line of best fit given by I:E\u0000<inline-formula> <tex-math>$_{mathrm {depth}} = 1.004times $ </tex-math></inline-formula>\u0000 I:Espiro – 0.006, with 95% confidence intervals for the slope [0.988, 1.019] and intercept [–0.017, 0.004]. We have demonstrated the viability of a non-contact monitoring method for determining the I:E ratio on healthy subjects breathing without mechanical support. This measure may be useful in monitoring the deterioration in respiratory status and/or response to therapy within the patient population. Clinical and Translational Impact Statement - The I:E ratio is an indicator of disease severity in COPD and asthma. Non-contact continuous monitoring of I:E ratio will offer the clinician a powerful new tool for respiratory monitoring","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"721-726"},"PeriodicalIF":3.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750123","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736214","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}