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

{"title":"Projected AR Serious Game “Painting Discovery” for Shoulder Rehabilitation: Assessment With Technicians, Physiotherapists, and Patients","authors":"Giuseppe Turini;Marina Carbone;Sara Condino;Donato Gallone;Vincenzo Ferrari;Marco Gesi;Michelangelo Scaglione;Paolo Parchi;Rosanna Maria Viglialoro","doi":"10.1109/JTEHM.2025.3557250","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3557250","url":null,"abstract":"Objective: Motivation and adherence are crucial for effective rehabilitation, yet engagement remains a challenge in upper limb physiotherapy. Serious Games (SGs) have emerged as a promising tool to enhance patient motivation. This study evaluates Painting Discovery, a projected augmented reality (AR) SG for shoulder rehabilitation, assessing engagement, ergonomics, and its potential to differentiate motor performance between healthy and those with rheumatoid arthritis, bursitis, subacromial impingement, rotator cuff tear, or calcific tendinopathy. Additionally, it examines improvements in pathological subjects following physiotherapy. Method: Sixteen healthy and seven pathological subjects participated. Engagement, ergonomics, and satisfaction were assessed using Likert-scale questionnaires. Motor performance was evaluated through completion time, speed, acceleration, and normalized jerk. Four pathological subjects underwent pre- and post-physiotherapy assessments over six weeks. Results: SG was highly engaging and ergonomic, with no significant differences based on prior video game or AR experience. The pathological group had longer completion times (<inline-formula> <tex-math>$56.49~pm ~37.85$ </tex-math></inline-formula>s vs. <inline-formula> <tex-math>$39.02~pm ~24.21$ </tex-math></inline-formula>s, p < 0.001), lower acceleration (<inline-formula> <tex-math>$1.11~pm ~0.92$ </tex-math></inline-formula> m/s2 vs. <inline-formula> <tex-math>$0.79~pm ~0.56$ </tex-math></inline-formula> m/s2, p < 0.001), and higher jerk (<inline-formula> <tex-math>$6.68times 107~pm ~1.37times 108$ </tex-math></inline-formula> m/s3 vs. <inline-formula> <tex-math>$9.22times 106~pm ~2.51times 107$ </tex-math></inline-formula> m/s3, p = 0.025) then healthy subjects. After physiotherapy, completion time and normalized jerk indicated enhanced efficiency and control. Conclusions: Painting Discovery shows strong potential as an engaging, accessible rehabilitation tool. While effective in differentiating motor impairments, its small sample size and horizontal-plane movement focus limit broader conclusions. Future studies should expand participation, incorporate vertical-plane movements, and refine performance metrics for clinical validation.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"149-157"},"PeriodicalIF":3.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947717","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845494","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":"2024 Index IEEE Journal of Translational Engineering in Health and Medicine Vol. 12","authors":"","doi":"10.1109/JTEHM.2025.3551783","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3551783","url":null,"abstract":"","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"740-756"},"PeriodicalIF":3.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937338","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688092","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":"Detection of Chronic Musculoskeletal Pain Using Voice Characteristics","authors":"Masakazu Higuchi;Toshiko Iidaka;Chiaki Horii;Gaku Tanegashima;Hiroyuki Oka;Hiroshi Hashizume;Hiroshi Yamada;Munehito Yoshida;Sakae Tanaka;Noriko Yoshimura;Mitsuteru Nakamura;Shinichi Tokuno","doi":"10.1109/JTEHM.2025.3553892","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3553892","url":null,"abstract":"Physical pain, particularly musculoskeletal pain, negatively impacts the activities of daily life and quality of life of elderly people. Because pain is a subjective sensation and there are no standard assessment procedures to detect pain, we attempted to quantitatively determine the actual state of chronic pain caused by musculoskeletal organs and related factors based on questionnaires. First, we studied techniques for diagnosing diseases by monitoring the involuntary characteristics of the voice. Then, we applied the technique based on voice characteristics and proposed a voice index to detect chronic musculoskeletal pain. The voice index was derived based on the assumption that physiological changes due to chronic musculoskeletal pain also affect the vocal cords. Subjects in this study were adults, 65 years of age or older, with chronic pain in the musculoskeletal system (lumbar and/or knees). A large-scale population-based cohort study was conducted in 2019. Voice characteristics were extracted from the recorded voices of the subjects, and the characteristics with similar properties were organized into several principal components using principal component analysis. The principal components were further combined using logistic regression analysis to propose a voice index that discriminates between normal subjects and subjects suffering from chronic musculoskeletal pain. A discrimination accuracy of approximately 80% was obtained using the dataset corresponding to the participants with knee pain only, and a discrimination accuracy of approximately 70% was obtained during cross-validation of the same dataset. The proposed voice index may serve as a novel tool for detecting chronic musculoskeletal pain. Clinical impact: The voice-based pain detection holds clinical significance owing to its noninvasive nature, ease of administration, and potential to efficiently assess large populations within a short time frame.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"136-148"},"PeriodicalIF":3.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937750","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801054","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":"Cross-Database Evaluation of Deep Learning Methods for Intrapartum Cardiotocography Classification","authors":"Lochana Mendis;Debjyoti Karmakar;Marimuthu Palaniswami;Fiona Brownfoot;Emerson Keenan","doi":"10.1109/JTEHM.2025.3548401","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3548401","url":null,"abstract":"Continuous monitoring of fetal heart rate (FHR) and uterine contractions (UC), otherwise known as cardiotocography (CTG), is often used to assess the risk of fetal compromise during labor. However, interpreting CTG recordings visually is challenging for clinicians, given the complexity of CTG patterns, leading to poor sensitivity. Efforts to address this issue have focused on data-driven deep-learning methods to detect fetal compromise automatically. However, their progress is impeded by limited CTG training datasets and the absence of a standardized evaluation workflow, hindering algorithm comparisons. In this study, we use a private CTG dataset of 9,887 CTG recordings with pH measurements and 552 CTG recordings from the open-access CTU-UHB dataset to conduct a cross-database evaluation of six deep-learning models for fetal compromise detection. We explore the impact of input selection of FHR and UC signals, signal pre-processing, downsampling frequency, and the influence of removing intermediate pH samples from the training dataset. Our findings reveal that using only FHR and pre-processing FHR with artefact removal and interpolation provides a significant improvement to classification performance for some model architectures while excluding intermediate pH samples did not significantly improve performance for any model. From our comparison of the six models, ResNet exhibited the strongest fetal compromise classification performance across both databases at a downsampling rate of 1Hz. Finally, class activation maps from highly contributing signal regions in the ResNet model aligned with clinical knowledge of compromised FHR patterns, highlighting the model’s interpretability. These insights may serve as a standardized reference for developing and comparing future works in this domain. Clinical and Translational Impact: This study provides a standardized workflow for comparing deep-learning methods for CTG classification. Ensuring new methods show generalizability and interpretability will improve their robustness and applicability in clinical settings.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"123-135"},"PeriodicalIF":3.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10912500","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706796","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":"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}

{"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}

{"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}

{"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}

{"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}

{"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}