Technology and Health Care最新文献

{"title":"Exploratory evaluation of an AR-based functional assessment platform in healthy young adults: cross-sectional associations with muscle strength and pulmonary function.","authors":"Tae-Sung Park, Jae Hyun Lee, Myung Hun Jang, Sang-Hun Kim, Byeong-Ju Lee, Jin A Yoon, Yong Beom Shin, Myung-Jun Shin","doi":"10.1177/09287329261484333","DOIUrl":"https://doi.org/10.1177/09287329261484333","url":null,"abstract":"<p><p>BackgroundAugmented reality (AR)-based exercise systems can automatically quantify task performance; however, evidence linking AR-derived metrics with conventional clinical measures remains limited.ObjectiveTo explore associations between AR-derived functional task performance and conventional clinical measures in healthy young adults.MethodsThirty healthy young adults completed four AR-based tasks: shuttle run (SR), on-the-spot running (OSR), side jump (SJ), and standing long jump (SLJ). Performance was captured using a LiDAR-based sensing system. Clinical assessments included sit-to-stand tests, pulmonary function, respiratory muscle strength, hand grip strength, body composition, and isometric quadriceps strength. Correlation and regression analyses examined associations between AR-derived outcomes and clinical measures. Additional multivariable analyses for SLJ adjusted for sex and height.ResultsSLJ showed the broadest pattern of associations with clinical measures. After adjustment, SLJ remained significantly associated with 1-min sit-to-stand performance, percent-predicted forced vital capacity, hand grip strength, skeletal muscle mass index, skeletal muscle mass, lower limb muscle mass, and Biodex peak torque. Associations with absolute pulmonary function measures, respiratory muscle strength, and FEV₁/FVC were attenuated after adjustment. SR and OSR showed limited associations with selected measures, whereas SJ showed no significant associations.ConclusionIn healthy young adults, AR-derived SLJ performance showed the most consistent cross-sectional associations, primarily with muscle-related measures. Its association with percent-predicted forced vital capacity also remained significant after adjustment. These findings are hypothesis-generating and should not be interpreted as evidence of technical or clinical validation of the AR-based platform.Trial Registration: This study was approved by the Institutional Review Board of Pusan National University Hospital (IRB No. 2505-024-151) and registered with the Clinical Research Information Service (CRIS; KCT0010729).</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261484333"},"PeriodicalIF":1.3,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smartphone based clinical decision support system for early detection of neonatal jaundice using artificial intelligence with temporal convolutional network.","authors":"Mahmoud Ragab, Iyad Katib, Mohammed K Al-Hanawi, Sanaa A Sharaf, Mohamed Alqurashi, Abdullah Al-Malaise Al-Ghamdi","doi":"10.1177/09287329261481881","DOIUrl":"https://doi.org/10.1177/09287329261481881","url":null,"abstract":"<p><p>Physiological jaundice is present in the initial week of life in neonates owing to the rise in level of bilirubin thereby resulting in yellowish coloring of sclera and skin. Severe jaundice and lethal bilirubin levels may result from brain damage as bilirubin is located in the central nervous system. Present diagnostic techniques consist of time-consuming and a painful invasive blood test and non-invasive tests using expensive transcutaneous bilirubin meters. Then regular monitoring is important, numerous efforts are conducted to progress non-invasive devices for testing utilizing a smartphone camera. Various efforts have been deployed to automate the neonatal jaundice diagnosis applying dissimilar machine learning, image processing, and CV methods. With the rise of smartphone-based and computer vision applications in clinical environments, especially for early detection of diseases like neonatal jaundice, reliability becomes more critical. This paper develops an Advancing Early Jaundice Detection of Neonatal with a Smartphone-based Computer Vision System and Golden Jackal Optimizer algorithm (AEJDN-SCVGJO) for Clinical Decision-Making. The image pre-processing applies an adaptive median filter (AMF) to enhance image quality by removing the noise. For the feature extractor, the SE-DenseNet has been deployed. Moreover, the proposed AEJDN-SCVGJO model executes the temporal convolutional network (TCN) model for the classification process. Finally, the Golden Jackal Optimizer (GJO) adjusts the parameter value of the TCN model optimally and outcomes in higher solution of classification. To exhibit the enhanced execution of the presented AEJDN-SCVGJO methodology, a wide-ranging experimental investigation is made. The comparative outcomes reported the improvised characteristics of the AEJDN-SCVGJO model.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261481881"},"PeriodicalIF":1.3,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148889142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy improvement for heart rate monitoring from bed-based ballistocardiography signals with motion artifacts.","authors":"Zeya Wang, Zhumu Fu, Hua Wang, Xiutao Cui, Yeping Zheng, Qin Yu, Xin Chen, Qiqi Shao, Bin Feng","doi":"10.1177/09287329261481871","DOIUrl":"https://doi.org/10.1177/09287329261481871","url":null,"abstract":"<p><p>BackgroundBallistocardiography (BCG)-based heart rate (HR) monitoring faces accuracy degradation due to motion artifacts, limiting its practical deployment.ObjectiveThis study aims to enhance HR estimation reliability under motion-contaminated conditions while ensuring real-time performance.MethodsA hybrid system integrating adaptive filtering and enhanced continuous wavelet transform (CWT) is developed. The framework localizes motion segments (95.1% accuracy) and employs spectral reconstruction via magnitude-frequency nullification to restore HR from contaminated windows. Computational latency was evaluated on an embedded ARM platform to verify real-time feasibility.ResultsValidation using 6000 min of data demonstrated that the proposed method achieved an MAE of 2.94 BPM, comparable to the CNN-LSTM baseline (2.85 BPM), while reducing the average processing latency from 450.2 ms to 86.4 ms. Compared with conventional methods, the proposed framework reduced the MAE by 53.8% and improved monitoring stability by 35.6%. Bland-Altman analysis confirmed limits of agreement within [-4.77, 5.23] BPM, validating clinical reliability.ConclusionsThe proposed hybrid framework provides a computationally efficient and accurate solution for non-contact HR monitoring in motion-prone and bed-based clinical environments.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261481871"},"PeriodicalIF":1.3,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148889079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of the Adhesive Remnant Index (ARI) using quantitative 3D scanner analysis on <i>in vitro</i> samples.","authors":"Joanna Janiszewska-Olszowska, Marta Mazur, Maciej Jedliński, Artnora Ndokaj, Luca Iuliano, Paolo Minetola, Roman Ardan, Gianna Maria Nardi","doi":"10.1177/09287329261436643","DOIUrl":"10.1177/09287329261436643","url":null,"abstract":"<p><p>BackgroundResidual adhesive left on enamel after orthodontic bracket removal can compromise aesthetics and enamel integrity. The Adhesive Remnant Index (ARI) is commonly used to visually assess these residues, but its correlation with true adhesive volume has not been quantitatively validated.ObjectiveThis study aimed to validate the visual ARI using 3D optical scanning and test the hypothesis of no correlation between ARI scores and adhesive remnant volumes.MethodsSeventy-five extracted human third molars were bonded with molar tubes using BrackFix Adhesive. After 24 h, tubes were debonded, and ARI scores (0-3) were assessed visually under 6.0× magnification. Residual adhesive was scanned pre- (T0) and post-debonding (T1) using a structured-light 3D scanner (ATOS Compact). Volumes were calculated and analyzed with Kendall's tau correlation.ResultsA strong correlation was found between ARI scores and adhesive remnant volumes (Kendall's τ = 0.649; p < 0.001). Median volumes increased with ARI scores: 0.048 mm³ (score 0), 0.401 mm³ (score 1), 1.025 mm³ (score 2), and 2.893 mm³ (score 3).ConclusionVisual ARI scores significantly reflect the actual adhesive remnant volume as quantified by 3D scanning, supporting ARI's clinical validity for assessing residual adhesive after debonding.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"554-562"},"PeriodicalIF":1.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147786637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosis and classification of thalassemia disease using machine learning: Comparative analysis of traditional models and a novel hybrid approach.","authors":"Hakan Tekin, Ece Gülşah Abbasoğulları, Faruk Baturalp Günay","doi":"10.1177/09287329261448689","DOIUrl":"10.1177/09287329261448689","url":null,"abstract":"<p><p>Thalassemia is a hereditary blood disorder characterized by abnormal hemoglobin production. Common diagnostic methods include complete blood count, high-performance liquid chromatography, and hemoglobin electrophoresis. While physicians make the final diagnosis, advancements in artificial intelligence, specifically machine learning (ML) and deep learning, offer significant potential as auxiliary tools and decision support systems to reduce diagnostic errors. This study investigates ML algorithms for classifying thalassemia and its subtypes, including alpha (α) thalassemia and beta (<i>β</i>) thalassemia (minor, intermedia, and major). A synthetic training dataset of 1534 samples was generated based on the statistical properties and correlation structures of real clinical data. The models were then evaluated using an external real-world dataset of 349 patients from the Hematology Department of Atatürk University Research Hospital. Support Vector Machines (SVM), Logistic Regression (LR), XGBoost, Artificial Neural Networks (ANN), and a hybrid stacking model named ThalP were implemented. The ThalP model integrates the probability outputs of SVM, LR, and XGBoost through a neural network meta-classifier. Experimental results demonstrate that the proposed ThalP model achieved strong performance on the real clinical dataset with an accuracy of 83.1% and a macro-F1 score of 0.80. These findings indicate that ML-based hybrid models can serve as effective decision-support tools for classifying thalassemia subtypes using routine hematological parameters.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"621-640"},"PeriodicalIF":1.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13454410/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148139462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validity and reliability of an automated fall risk assessment system using a depth camera in community-dwelling adults: A proof-of-concept pilot study.","authors":"Sungbae Jo","doi":"10.1177/09287329261445778","DOIUrl":"10.1177/09287329261445778","url":null,"abstract":"<p><p>BackgroundClinical fall risk prediction often relies on subjective observation or simplistic metrics, despite the high costs associated with falls in older adults.ObjectiveThis proof-of-concept study evaluated the validity and reliability of a consumer-grade depth camera system as an objective alternative for automated fall risk assessment.MethodsThirty-nine community-dwelling adults performed Timed Up and Go (TUG), Five Times Sit-to-Stand (FTSS), and Tandem Stance (TST) tests. Concurrent measurements were taken by an automated depth camera and blinded physical therapists. Validity (concurrent, convergent, discriminative) and reliability were assessed.ResultsAutomated FTSS and TUG tests demonstrated strong concurrent validity with therapist measurements (r = 0.813 and 0.915) and high discriminative accuracy for fall history (AUC = 0.941 and 0.864). Depth camera-based FTSS vertical velocity was significantly lower in participants with a fall history (<i>p</i> < 0.001). TST sway metrics showed limited discriminative validity. The system showed good to excellent test-retest reliability. In an age-stratified analysis of older adults (≥65 years), AFTSS time and the AUC-weighted composite score demonstrated acceptable discrimination for retrospective fall history (AUC = 0.892 and 0.867, respectively)ConclusionsThe depth camera system showed promise as a valid and reliable tool for objective quantification of performance on fall-risk-related functional tests, particularly FTSS and TUG, when benchmarked against therapist-administered measurements. Discriminative findings against retrospective fall history should be interpreted as exploratory, and larger prospective studies are required before clinical screening thresholds can be recommended.Trial RegistrationClinicalTrials.gov (NCT06519864).</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"592-606"},"PeriodicalIF":1.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147786627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of a new airway opening device with automatic position adjustment in bronchoscopy: A multicenter, prospective, open-label, and randomized controlled study.","authors":"Minyue Sun, Yaowen Zhang, Xiangmei Yang, Hongmei Chen, Luo Yan, Dongmei Li, Zeng Qing, Tan Li, Yishi Li, Yuguo Zhou, Mu Jiang","doi":"10.1177/09287329261442664","DOIUrl":"10.1177/09287329261442664","url":null,"abstract":"<p><p>BackgroundAirway opening is crucial in management, and failure can lead to respiratory failure and death. Current manual methods have high failure rates, our team developed a device that automatically and precisely opens the patient's airway, replacing manual techniques.ObjectiveTo explore whether a new airway opening device with automatic position adjustment can effectively shorten the time for the tracheoscope to enter the airway while maintaining the airway open compared with the manual method of opening the airway.MethodsA multicenter, prospective, randomized controlled trial involved 400 adult patients undergoing bronchoscopy in three tertiary hospitals in China. Patients were randomly assigned to a control group using the head-up jaw lift method or to instrument test groups using the device at an angle of position (PA) of 90°, 95°, and 100°.ResultsA total of 357 patients completed the trial with a 100% success rate of bronchoscope entry. Median entry times for tracheoscope to enter the airway were 39.00 s for the control group, 33.00 s for PA 90° (P = 0.007), 31.00 s for PA 95° (P = 0.004), and 32.00 s for PA 100° (P = 0.005).ConclusionsThe new device can safely and effectively open the airway, shorten tracheoscope entry time, and maintain an open airway accurately.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"544-553"},"PeriodicalIF":1.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147786538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The application of overlapping microwave ablation in liver tumor therapy: A review.","authors":"Qi Wang, Shuicai Wu, Yubo Zhang, Huijing He, Weiwei Wu, Hongjian Gao","doi":"10.1177/09287329261436641","DOIUrl":"10.1177/09287329261436641","url":null,"abstract":"<p><p>Overlapping microwave ablation has been widely used in treating large liver tumors, and more and more researchers are focusing on this therapy strategy to expand its clinical application. This paper reviewed the therapeutic schemes, simulation techniques, <i>in vitro</i> experimental results and coagulation zone evaluation techniques for large tumor overlapping microwave ablation, to evaluate the effectiveness of different multi-antenna treatment schemes. The prospect and challenge of overlapping microwave ablation in the clinical treatment of large tumors were further discussed.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"527-543"},"PeriodicalIF":1.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147700626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-branch vision transformer with GA-optimized focal loss for ECG signal classification.","authors":"Zeynep Garip, Ekin Ekinci","doi":"10.1177/09287329261446510","DOIUrl":"10.1177/09287329261446510","url":null,"abstract":"<p><p>Background The classification of electrocardiogram (ECG) signals is a critical task in detecting cardiac arrhythmias. However, challenges such as class imbalance and the need to capture both local and global temporal patterns make this problem complex.ObjectiveIn this study, a hybrid deep learning model, called MBViT-FocalGA, is proposed for the classification of electrocardiogram ECG signals.MethodsThe proposed model combines a Multi-branch Vision Transformer (MBViT) architecture with a Focal Loss function optimized via a Genetic Algorithm (GA) called as MBViT-FocalGA. Thanks to three independent ViT branches operating at different patch sizes, the model can effectively learn both local and global timing patterns from ECG signals. Furthermore, the class imbalance problem frequently encountered in arrhythmia data is mitigated by optimizing the hyperparameters of the Focal Loss function with GA, improving the recognition of minority classes. The model's performance was evaluated on the MIT-BIH Arrhythmia Database in two different scenarios: (i) a two-class scenario combined into normal (N) and others (O), and (ii) a five-class scenario consisting of N, L, R, A, and V.ResultsExperimental results demonstrate that the MBViT-FocalGA model achieves superior classification performance with 98% overall accuracy and a high F1 score, outperforming classical deep learning models for both scenarios.ConclusionThese findings demonstrate the strong potential of transformer-based architectures and adaptive loss function optimizations in ECG classification.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"607-620"},"PeriodicalIF":1.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148018232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time microcalcification detection with SAAR algorithm.","authors":"Aleksandar Peulic, Zeljko Jovanovic, Marina Milosevic","doi":"10.1177/09287329261442665","DOIUrl":"10.1177/09287329261442665","url":null,"abstract":"<p><p>BackgroundDetecting breast cancer, especially identifying microcalcifications in mammograms, is challenging due to the need for high sensitivity and efficient processing. This study presents a novel algorithm, Sigmoidal Slope Analysis and Aspect Ratio Evaluation (SAAR), designed for real-time application on edge devices. By employing a multi-step adaptive process with sigmoidal functions, SAAR enhances intensity contrast and prioritizes regions of interest, enabling fast, accurate detection of microcalcifications.ObjectiveThis study aims to develop and validate an efficient, edge-device-compatible method for detecting microcalcifications in mammographic images. The goal is to provide a tool that enhances diagnostic efficiency through real-time processing, thereby supporting early breast cancer detection in both clinical and remote settings.MethodsThe SAAR algorithm utilizes an adaptive slope detection technique based on the sigmoid function, dynamically adjusting to local intensity features. This approach allows for greater adaptability to image variations. The algorithm prioritizes regions of interest through a multi-step adaptive process, enhancing intensity differences to focus on potential microcalcifications.ResultsTesting on established mammography databases, such as MIAS, demonstrates the algorithm's effectiveness, with improved sensitivity compared to conventional methods. Designed for edge devices, the algorithm leverages their real-time processing capabilities, offering lower latency and enhanced privacy.ConclusionsThe integration of SAAR with edge devices represents a promising advancement in breast cancer detection. The adaptive nature of SAAR, coupled with the real-time processing capabilities of edge devices, provides a robust solution for enhancing microcalcification detection efficiency and sensitivity in mammography.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"563-577"},"PeriodicalIF":1.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147822559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}