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

{"title":"The Need for Demonstrated Clinical Translational Evidence in Submissions to the IEEE Journal of Translational Engineering in Health and Medicine","authors":"Gerard Boyle;Arturo Forner-Cordero;Anubha Kalra;Vaclav Kremen;Alfonso Maria Ponsiglione;Mahsa Ranji;Sharad Sinha;Xiangyang Tang;Po Yang;Richard B. Reilly","doi":"10.1109/JTEHM.2026.3680558","DOIUrl":"https://doi.org/10.1109/JTEHM.2026.3680558","url":null,"abstract":"The IEEE J<sc>ournal of</small> T<sc>ranslational</small> E<sc>ngineering in</small> H<sc>ealth and</small> M<sc>edicine</small> (JTEHM) exists at the intersection of biomedical engineering and clinical practice. Published articles go beyond laboratory proof-of-concept to provide tangible, real-world evidence of translation into clinical settings. This editorial provides the rationale for manuscripts submitted to IEEE JTEHM to demonstrate evidence of clinical translation. It also provides examples of acceptable forms of evidence and offers guidance to authors on how to meet this expectation. Clinical and Impact—By requiring demonstrated clinical translational evidence IEEE JTEHM endeavours to publish high-quality research with scientific novelty and practical clinical impact. This expectation strengthens the journal’s aim to accelerate the adoption of innovative solutions into healthcare systems and ultimately deliver quantifiable benefits to patients.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"14 ","pages":"vi-xi"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11521032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147937502","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":"Innovative Pediatric Simulator for Difficult Airway Management Training","authors":"Marta Mencarelli;Luca Puggelli;Roberto Baggi;Stefano Avenali;Francesca Amoretti;Monica Carfagni;Yary Volpe;Paola Serio","doi":"10.1109/JTEHM.2026.3677892","DOIUrl":"https://doi.org/10.1109/JTEHM.2026.3677892","url":null,"abstract":"Objective: Pediatric airway management presents unique challenges, particularly in patients with congenital conditions that increase the risk of difficult intubation. Simulation offers a safe environment for clinicians to practice, yet current pediatric mannequins often lack pathological realism. This study aims to develop and validate a high-fidelity, patient-specific pediatric airway mannequin simulating difficult intubation associated with Crouzon syndrome. Technology or Method: A modular training mannequin was developed through CT-based modeling and additive manufacturing at the T3Ddy Laboratory, a collaboration between AOU Meyer Children’s Hospital–IRCCS and the University of Florence. Anatomical structures were segmented from CT and MRI scans of a 19-month-old patient with Crouzon syndrome. These 3-D models guided the design of a realistic airway, including nasal and oral access, a flexible tongue, and a movable jaw. Rigid parts were 3-D-printed; soft tissues were cast in silicones of varying hardness. Design choices were validated through iterative testing and refinement. Results: The simulator was evaluated during a pediatric bronchoscopy training course. Physicians assessed realism, tactile feedback, and usability via a Likert-scale questionnaire. Results indicated strong agreement on the mannequin’s effectiveness in replicating challenging intubation scenarios, particularly those requiring fiberoptic-guided techniques. Conclusion: This modular, patient-specific pediatric airway simulator provides a realistic, high-fidelity platform for training clinicians in difficult intubation techniques. Its anatomically accurate design enhances procedural confidence and skill acquisition, particularly for managing complex cases like those associated with Crouzon syndrome.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"14 ","pages":"179-187"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11456528","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606193","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":"Gait Event Detection From Thigh Segment Kinematics in Children With Crouch Gait","authors":"Jordan Dembsky;Noah Rubin;Thomas C. Bulea","doi":"10.1109/JTEHM.2026.3679585","DOIUrl":"https://doi.org/10.1109/JTEHM.2026.3679585","url":null,"abstract":"Objective: Wearable exoskeletons can improve walking in children with movement disorders by delivering precisely timed and scaled torques based on discrete gait phase. Most single-joint systems segment the gait cycle using underfoot force-sensitive resistors (FSRs), but FSRs are suboptimal for exoskeleton use in pathological gait due to fragile hardware and inconsistent foot loading/unloading. Thigh kinematics may provide more robust gait event detection (GED), yet this approach has not been evaluated in children with crouch gait, a key target population for pediatric exoskeleton therapy. This study aimed to: 1) assess feasibility of GED using thigh kinematics from ground-truth motion capture, 2) evaluate GED using a thigh-mounted inertial measurement unit (IMU), and 3) validate this method with a wearable knee exoskeleton in children with crouch gait.Methods and procedures: A novel thigh segment kinematics algorithm (TSKA) was developed to detect initial contact (IC) and terminal contact (TC) during overground walking. Algorithm performance was assessed using IMU-derived gait events compared to motion capture ground truth, then evaluated with a wearable knee exoskeleton.Results: Mean IMU-based timing errors for IC and TC were 38 and 84 ms, respectively. IC predictions occurred earlier than ground truth, whereas TC predictions varied. During exoskeleton walking, IMU-based GED significantly improved timing accuracy compared to FSR-based detection.Conclusion: The thigh-based kinematic algorithm, tuned for each individual with six or fewer strides, accurately detected gait events in children with crouch gait and outperformed FSR-based GED in an exoskeleton. These findings support further development for real-time exoskeleton control and gait assessment in individuals with pathological gait.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"14 ","pages":"188-197"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11458653","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665273","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":"Neural Architecture Search-Based ECG Classification for Low-Power Edge Deployment in IoT Healthcare Systems","authors":"Xiaolin Li;Long Nguyen;Sean Lim;Guoxin Wang;Rajesh Panicker;Declan Delaney;Ganesh Neelakanta Iyer;Deepu John","doi":"10.1109/JTEHM.2026.3701554","DOIUrl":"https://doi.org/10.1109/JTEHM.2026.3701554","url":null,"abstract":"Objective: To develop and validate a deployment-centric ECG arrhythmia classification framework for low-power wearable edge devices, with emphasis on efficient on-device screening and reduced communication burden in remote cardiac monitoring workflows, optimized for seamless integration into clinical remote monitoring workflows using resource-constrained wearable IoT edge devices.Methods and Procedures: We design a clinically-oriented wearable-to-phone workflow in which the edge device performs on-device ECG inference and transmits actionable anomaly summaries to a smartphone via Bluetooth Low Energy (BLE). This approach reduces latency, energy cost, and privacy risks compared to continuous raw-signal transmission. A NAS process tailored to arrhythmia detection automatically explores candidate architectures under diverse compute and memory constraints to identify feasible models for embedded platforms. Multiple deployment-ready variants are generated via quantization and pruning, and implemented on an ARM Cortex-M33-based system. System efficiency is quantified through end-to-end measurements of energy consumption and data throughput, ensuring its readiness for long-term ambulatory monitoring.Results: The deployed models achieve real-time inference on an ARM Cortex-M33 with as little as 21.7 kB RAM and 265k MACs. After optimization, energy consumption is reduced to 5.51 mJ per inference. Compared with the original model, RAM usage is reduced by 53.6%, with only a 0.5% absolute decrease in accuracy (97.9% to 97.4%). Hardware-in-the-loop power profiling confirms significant efficiency gains, validating the clinical feasibility of the proposed selective offloading strategy.Conclusion: A NAS-guided, deployment-centric pipeline-spanning architecture search, compression/mapping, and embedded power profiling enables accurate, real-time ECG classification within stringent wearable constraints. The selective-offloading strategy bridges the gap between laboratory algorithms and practical cardiac surveillance by providing a high-fidelity, low-power, and clinically-aware solution for continuous arrhythmia screening.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"14 ","pages":"285-297"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11556209","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442933","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":"Soft Tissue Radiodensity Asymmetry as Clinical Indicator for Diabetes and Hypertension in Aging","authors":"Marco Recenti;Alfonso M. Ponsiglione;Carlo Ricciardi;Michela Russo;Kyle J. Edmunds;Francesco Amato;Magnus K. Gislason;Ugo Carraro;Milan Chang;Paolo Gargiulo","doi":"10.1109/JTEHM.2026.3718689","DOIUrl":"https://doi.org/10.1109/JTEHM.2026.3718689","url":null,"abstract":"Objective: Chronic conditions like diabetes mellitus (DM) and hypertension (HTN) significantly impair physical functioning in older adults, leading to a reduced quality of life and increased risk for disability. This study evaluates the utility of soft tissue radiodensity asymmetry, derived from mid-thigh computed tomography (CT) imaging, as a biomarker for DM and HTN. Method: Using data from the AGES-Reykjavik study, the Nonlinear Trimodal Regression Analysis (NTRA) method was employed to extract 11 geometrical features from fat, muscle, and connective tissue radiodensity distributions derived from cross-sectional CT images of the mid-thigh. Asymmetry indices were calculated as the absolute differences between corresponding left and right leg parameters and were analyzed for their associations with DM and HTN. Results: Statistical analyses demonstrated that DM and HTN status altered the relationship between age and radiodensity asymmetry: DM significantly influenced muscle-specific asymmetry (<inline-formula> <tex-math>$Delta mu $ </tex-math></inline-formula> musc), while HTN showed directional trends across multiple tissue types that did not survive correction for sex, BMI, and multiple comparisons. Logistic regression identified fat tissue location asymmetry as a key predictor for DM, while connective tissue asymmetry parameters were significantly associated with HTN status. Machine learning models validated these findings, with Random Forest achieving 89.3% accuracy for DM classification and 88.1% for HTN, highlighting the robustness of these asymmetry features. Conclusions: These results establish soft tissue asymmetry as a promising novel biomarker for DM and HTN, reflecting metabolic and vascular dysfunctions. Integrating such measures into clinical practice could enhance predictive models and inform targeted interventions, improving health outcomes for aging populations.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"14 ","pages":"352-368"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11635813","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836541","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":"Explainable Knowledge-Guided Algorithm for Contrast Extravasation Detection on Computed Tomography","authors":"Tuan D. Pham;Maki Kitamura;Taichiro Tsunoyama","doi":"10.1109/JTEHM.2026.3681662","DOIUrl":"https://doi.org/10.1109/JTEHM.2026.3681662","url":null,"abstract":"Objective: To develop an explainable, knowledge-guided framework for automated detection of contrast media extravasation from sequential computed tomography (CT) images and to evaluate its potential to accelerate time-critical trauma triage while maintaining clinically acceptable sensitivity.Methods: A mathematical framework was formulated to explicitly encode three expert-derived diagnostic rules: 1) progressive increase of contrast outside anatomically plausible vessels, 2) appearance of contrast in non-vascular regions, and 3) localized irregularity of vessel caliber. Sequential two-dimensional CT slices were analyzed using a 2.5D formulation integrating temporal intensity evolution, anatomical plausibility, vessel morphology, and inter-slice continuity. The model outputs a confidence score and a binary alert. Model parameters and decision thresholds were initialized using a single representative clinical case guided by expert interpretation. Performance was evaluated against senior emergency surgeon assessment, emphasizing sensitivity and time-to-decision.Results: The proposed framework achieved clinically acceptable sensitivity for detection of contrast extravasation while substantially reducing time-to-decision relative to manual review. Early-trigger analysis demonstrated that positive cases were identified within the initial portion of the CT volume, supporting rapid screening and prioritization in emergency workflows.Conclusion: This study demonstrates the feasibility of translating expert clinical reasoning into an interpretable computational model for time-critical imaging tasks. The knowledge-guided design enables rapid automated screening while preserving transparency and clinician oversight. The framework shows promise as a decision-support tool for accelerating trauma triage, with future work focused on prospective validation and broader multi-center evaluation. Clinical Impact: The proposed knowledge-guided algorithm enables rapid extravasation alerts on trauma CT, supporting earlier triage and prioritization for angiography or surgery within existing emergency imaging workflows.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"14 ","pages":"198-209"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11475740","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665480","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":"Innovative Wearable Platform for Synchronized Biosignals Acquisition: A Proof of Concept in a Cuff-Less Blood Pressure Monitoring Case Study","authors":"Alessio Serrani;Andrea Aliverti","doi":"10.1109/JTEHM.2026.3687981","DOIUrl":"https://doi.org/10.1109/JTEHM.2026.3687981","url":null,"abstract":"Objective: This study evaluates the performance of a fully wireless multi-node wearable platform equipped with a sub-microsecond synchronization engine in a clinically relevant scenario. The prototype system is used in a cuff-less blood pressure monitoring application, based on pulse arrival time derived from synchronized ECG and PPG signals. Methods: The system integrates a custom 2.4 GHz synchronization protocol and Bluetooth Low Energy for data transmission. Nineteen healthy subjects completed a treadmill protocol designed to induce transient hemodynamic perturbations representative of daily-life physical activities. PAT values extracted from ECG and PPG signals acquired before and after exercise were compared with reference systolic blood pressure (SBP) measurements, intermittently sampled using a validated oscillometric device. The experimental protocol and analysis were designed to reflect realistic home monitoring scenarios, including limited user interaction during measurements. Results: Immediately after exercise, significant deviations from baseline were observed in computed PAT (<inline-formula> <tex-math>$plt 10^{-18}$ </tex-math></inline-formula>) and SBP (<inline-formula> <tex-math>$plt 10^{-7}$ </tex-math></inline-formula>). PAT recovery trend was accurately modeled by a mono-exponential function (<inline-formula> <tex-math>$R^{2}=0.98$ </tex-math></inline-formula>). Recovery indices derived from PAT and SBP were strongly correlated (Pearson’s <inline-formula> <tex-math>$r=0.93$ </tex-math></inline-formula>, <inline-formula> <tex-math>$plt 0.001$ </tex-math></inline-formula>) with high concordance (<inline-formula> <tex-math>$rho _{c}=0.885$ </tex-math></inline-formula>), although Bland-Altman analysis revealed subject-specific variability (LoA: -34.36% / 37.53%). Conclusion: The proposed platform enables continuous synchronized multi-signal acquisition for extracting PAT dynamics and tracking blood pressure fluctuations under realistic home-monitoring conditions, with minimal additional user interaction. These results support the operational feasibility of wirelessly synchronized architectures for cardiovascular monitoring in daily-life scenarios, promoting integration into remote health assessment workflows beyond traditional intermittent cuff-based measurements.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"14 ","pages":"234-247"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11495148","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147937500","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":"Development of a Realistic Physical Phantom for Laparoscopic and Robotic-Assisted Sacrocolpopexy Training and Associated","authors":"Marina Carbone;Rosanna M. Viglialoro;Sara Condino;Nadia Cattari;Giulia Pagnanelli;Matteo Bianchi;Paolo Mannella;Vincenzo Ferrari;Andrea Giannini;Tommaso Simoncini","doi":"10.1109/JTEHM.2026.3684747","DOIUrl":"https://doi.org/10.1109/JTEHM.2026.3684747","url":null,"abstract":"Objective: Robotic-assisted sacrocolpopexy is the gold standard for treating advanced pelvic organ prolapse, but its most critical step, presacral dissection, carries a significant risk of vascular and ureteral injury. Traditional training relies heavily on intraoperative exposure, yet no simulator currently provides an anatomically realistic, reusable, and cost-effective platform for practicing this high-risk procedure. Method: To address this gap, we developed a modular phantom designed for training in laparoscopic and robotic sacrocolpopexy procedures. A hierarchical task analysis was conducted to identify the critical steps involved in presacral dissection, informing the design of the simulator. The phantom incorporates a reusable pelvic base and a stratified sacral area pad that reproduces the vertebrae, anterior longitudinal ligament, vascular structures, ureters, visceral fat, and peritoneum. Fabrication employed 3D printing, silicone molding, and layered composite materials to strike a balance between anatomical fidelity and durability. Results: Five expert gynecologic surgeons validated the simulator using structured questionnaires, hierarchical task analysis-based task assessment. The model demonstrated excellent content validity (S-CVI = 0.877), strong inter-rater agreement, and high internal consistency (Cronbach’s alpha = 0.925). Ten of thirteen critical surgical tasks were completed by all participants, including realistic dissection and ligament exposure. Face validity ratings confirmed the realism of anatomy and haptic feedback, though ureteral identification was less consistent. Conclusion: The developed phantom enables realistic, structured training in presacral dissection and mesh fixation, supporting skill acquisition outside the operating room. Clinical Impact—By providing a cost-effective, reusable, and anatomically faithful simulator, this work contributes to safer training in gynecology and pelvic floor surgery, with potential integration into competency-based curricula and dry-lab facilities.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"14 ","pages":"225-233"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11482612","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796204","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":"Analyzing Gait Pattern Associated With Neuropsychiatric Symptoms in Parkinson’s Disease by a Comprehensive Approach","authors":"Michela Russo;Carlo Ricciardi;Martina Mestizia;Federico Di Filippo;Noemi Pisani;Giuseppe de Biasi;Alfonso M. Ponsiglione;Paolo Barone;Marianna Amboni;Maria Romano","doi":"10.1109/JTEHM.2026.3700914","DOIUrl":"10.1109/JTEHM.2026.3700914","url":null,"abstract":"Objective: Parkinson’s disease (PD) is characterized by motor and non-motor symptoms. Among non-motor symptoms, neuropsychiatric symptoms (NPS), including depression, anxiety, cognitive decline and psychosis, are common and affect quality of life. The present study investigates gait patterns in PD patients with and without NPS, focusing on kinematic, kinetic, and spatio-temporal variables extracted from gait analysis. Notably, the assessments were carried out in a real-world outpatient clinical environment, which strengthens the translational value of the findings. Method: Overall, 104 PD patients were assessed using an optoelectronic system to record spatio-temporal parameters, kinematic and kinetic signals during a single task and two different dual tasks. First, a preliminary statistical analysis was carried out and then, several machine learning algorithms were applied to classify patients with and without NPS based on gait data. Results: Statistical analyses revealed that PD patients with NPS had slower gait, greater instability and reduced movement control. Decision Tree and Support Vector Machine classifiers showed promising results, particularly for kinematic and kinetic features (with an accuracy of 91.3% and 80.3%, respectively). The most recurrent features on which the classifiers were trained almost entirely matched the significance found by statistical analyses. Conclusion: The present findings further expand the relationship between motor and non-motor symptoms in PD highlighting that the relationship between gait and mental symptoms may extend beyond cognitive status. Clinical Impact—The study highlights the value of gait analysis as a promising, non-invasive surrogate biomarker for the early identification of a distinct clinical phenotype characterized by an increased burden of neuropsychiatric symptoms in Parkinson’s disease. The high accuracy achieved by machine learning models trained on gait features for distinguishing Parkinsonian patients with and without neuropsychiatric symptoms supports the development of assistive diagnostic tools that can enhance clinical decision-making, reduce subjectivity in patient assessments, and enable personalized monitoring of disease progression. Since the analyses were carried out in a real-world outpatient setting, the proposed approach naturally lends itself to integration within multidisciplinary clinical workflows, fostering synergistic collaboration among neurologists, physiotherapists and biomedical engineers, and contributing to a more personalized, precise, and proactive model of care.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"14 ","pages":"274-284"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13278742/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148297440","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":"Toward Real-Time Detection of Intravenous Infiltration Using a Low-Cost Strain-Sensing Patch: Validation in Animal Models and Pediatric Clinical Monitoring","authors":"T. El-Khalili;S. Morgenstern;J. Fackler;R. Yang","doi":"10.1109/JTEHM.2026.3729926","DOIUrl":"https://doi.org/10.1109/JTEHM.2026.3729926","url":null,"abstract":"Objective: To develop and evaluate a low-cost, skin-safe strain-sensing patch and real-time-capable algorithm for early detection of intravenous (IV) infiltration during continuous monitoring. Methods: A custom strain sensor composed of discrete piezoresistive elements was encased between two transparent medical dressings and mounted adjacent to the IV site. Data were collected during controlled IV infiltrations in juvenile pigs and during inpatient monitoring in pediatric patients without infiltrations. A strictly causal, event-based detection algorithm was implemented using features derived from the strain signal and its first difference. Performance was evaluated using event sensitivity, time-to-detection (TTD), and false-positive alerts per hour (FP/hr). Candidate thresholds were derived from the animal training-set evidence distribution. The operating point was selected using training-data sensitivity, detection time, and control false-alert burden, then fixed before held-out testing. Results: Across 16 animal recordings comprising 35 infiltration events, the algorithm detected 30 events (event sensitivity = 0.86) with a median TTD of 57 seconds at the selected operating threshold. Approximately 86.7% of detected events occurred within two minutes of infiltration onset. On the held-out recording subset, 8 of 9 infiltration events were detected, with a median TTD of 53.5 seconds. Evaluation on 117.8 hours of pediatric inpatient control data demonstrated a threshold-dependent false-positive alert burden, operating at approximately 1.2 alerts per hour at the selected operating threshold. Conclusion: A strain-sensing patch integrated with a real-time-capable, event-based detection algorithm enabled early IV infiltration detection at low infused volumes while maintaining a false-positive alert burden that is acceptable for early-stage clinical evaluation.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"14 ","pages":"397-409"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11674199","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133416","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}