Informatics in Medicine Unlocked最新文献

{"title":"Disconnected connections: The impact of technoference on adolescent emotions and behavior","authors":"Tayyaba Ali,&nbsp;Sidra Iqbal","doi":"10.1016/j.imu.2025.101621","DOIUrl":"10.1016/j.imu.2025.101621","url":null,"abstract":"<div><div>Extensive parental use of electronic devices correlates with poorer parent-adolescent interactions, though research has not investigated any potential effects on adolescent behavior. This research investigated whether increased technoference is associated with higher levels of adolescents' internalizing and externalizing behaviors, along with diminished prosocial behaviors. 450 pakistani adolescents from public and private schools aged 11–17 completed the self-reported versions of The Technoference Scale and the Strengths and Difficulties Questionnaire. Results indicated that parental and adolescent technoference was positively correlated with internalizing and externalizing behavior problems, while negatively correlated with prosocial behavior. Strong association between parental and adolescent technoference was observed. Findings from this study highlight the significant influence of technoference on adolescent behavior, suggesting that managing technology within families is essential for promoting healthier behavioral patterns. The significant correlations between technoference and both internalizing and externalizing behaviors underscore the potential risks associated with excessive media use and disrupted family interactions.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101621"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends in delivery hospitalizations and the impact of ICD-9-CM to ICD-10-CM-PCS transition in Portugal between 2010 and 2018","authors":"Catarina de Paraíso Camarinha ,&nbsp;Maria Miguel Gomes Oliveira ,&nbsp;Cecília Elias ,&nbsp;Miguel de Araújo Nobre ,&nbsp;Leonor Bacelar Costa Nicolau ,&nbsp;Cristina Furtado ,&nbsp;Andreia Silva da Costa ,&nbsp;Paulo Jorge da Silva Nogueira","doi":"10.1016/j.imu.2025.101626","DOIUrl":"10.1016/j.imu.2025.101626","url":null,"abstract":"<div><h3>Background</h3><div>Hospital discharge data are essential for maternal health surveillance, clinical research, and healthcare resource allocation. In 2017, Portuguese hospitals transitioned from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) to the International Classification of Diseases, 10th edition, Clinical Modification and Procedure Coding System (ICD-10-CM/PCS), impacting the recording of delivery hospitalizations. This study examines trends in delivery hospitalizations from 2010 to 2018 and assesses the impact of the ICD-10-CM/PCS transition.</div></div><div><h3>Methods</h3><div>We conducted a register-based observational cross-sectional analysis using data from the National Hospital Discharge Database, covering delivery hospitalizations in public hospitals from January 1, 2010, to December 31, 2018. Delivery episodes were identified using diagnosis codes, normal delivery codes, diagnosis-related group (DRG) codes, and procedure codes. Statistical analyses included descriptive statistics, interrupted time series with segmented regression, and Prophet forecasting models to evaluate trends and the impact of the coding transition.</div></div><div><h3>Results</h3><div>A total of 673,978 delivery hospitalizations were recorded. The transition from ICD-9-CM to ICD-10-CM/PCS in 2017 had minimal overall impact on delivery trends. DRG codes consistently identified the majority of delivery episodes, with outcome of delivery codes and selected procedure codes showing varying trends. An increase in episodes identified by normal delivery codes and a significant decrease in episodes identified by procedure codes was observed immediately after the ICD-10 transition (p &lt; 0.001). The Prophet model indicated improved forecast accuracy for procedure codes when including the ICD-10 transition variable.</div></div><div><h3>Conclusion</h3><div>The transition to ICD-10-CM/PCS had a limited impact on overall delivery hospitalization trends but significantly affected procedure coding. These findings underscore the importance of considering coding system changes in healthcare data analyses. Further research should incorporate private hospital data and continuously monitor coding practices to ensure reliable health data for research and policy-making.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101626"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeB5-XNet: An explainable ensemble model for ocular disease classification using feature extraction and Grad-CAM","authors":"Geethanjali Kher ,&nbsp;Suyash Mehra ,&nbsp;Rajni Bala ,&nbsp;Ram Pal Singh","doi":"10.1016/j.imu.2025.101632","DOIUrl":"10.1016/j.imu.2025.101632","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Vision serves as a window to the world, enabling individuals to fully appreciate various dimensions of everyday life. Some eye diseases can lead to irreversible loss of vision. Developing an algorithm for a clinical decision support system that explains its predictions is essential to assist the limited number of ophthalmologists in managing the increasing patient load with severe ocular diseases. In contrast to earlier models that concentrated on single-disease classification without providing insights into predictions, this approach introduces DeB5-XNet, a novel explainable ensemble model for multi-categorical classification of ocular conditions.</div></div><div><h3>Methods:</h3><div>This study presents an ensemble model developed to categorize images into glaucoma (G), cataract (C), diabetic retinopathy (DR), and healthy condition labeled as Normal(N). This proposal operates on three levels: First, the images are enhanced using CLAHE in LAB color space, which improves the model’s predictive capability. Second, an ensemble model is constructed by concatenating features derived from pairs of seven pre-trained models, utilizing their diverse architectures to capture complex characteristics essential for accurate diagnosis. These extracted features are then fine-tuned using a consistent classifier. Third, it has been observed that trust in any diagnostic method is dependent on explainability. Therefore, the selected approach was validated, and its effectiveness was demonstrated using Grad-CAM. The performance of this diagnostic model was evaluated using recall, precision, F1-score, and accuracy metrics.</div></div><div><h3>Results:</h3><div>The ensemble models outperformed the individual models. DeB5-XNet, an ensemble model that extracted features from DenseNet121 and EfficientNetB5, achieved the highest test accuracy of 95%, notably reducing false negatives compared to standalone models. Remarkably, the model further demonstrated an F1-score of 97% for cataract, 100% for diabetic retinopathy, 90% for glaucoma, and 91% for normal cases.</div></div><div><h3>Conclusion:</h3><div>The proposed ensemble model, DeB5-XNet shows an improvement over the individual pre-trained models. The Grad-CAM technique demonstrates that the features used by the ensemble model for classification closely align with those identified by ophthalmologists for diagnostic purposes. This alignment strengthens the model’s reliability and potential usefulness in clinical settings.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"54 ","pages":"Article 101632"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An optimized data analytics pipeline for improving healthcare diagnosis using ensemble learning","authors":"Lomat Haider Chowdhury ,&nbsp;Shaira Tabassum ,&nbsp;Swakkhar Shatabda ,&nbsp;Ashir Ahmed","doi":"10.1016/j.imu.2025.101623","DOIUrl":"10.1016/j.imu.2025.101623","url":null,"abstract":"<div><div>Healthcare diagnosis is a process physicians follow before prescribing the patients. The medical doctors may make an early prediction by observing the physical signs and symptoms. Imposing a treatment without proper diagnosis cannot guarantee a cure and sometimes may lead the patient to a more detrimental scenario. However, the cost of healthcare diagnosis makes people indifferent to going through the process. Big data and machine learning are already in use to contribute to the healthcare diagnosis sector with the available data which is enormously growing through the digitalization of the system. Yet the difficulty remains since the raw data contains noise including missing values, outliers, and an imbalanced number of samples. These properties in a dataset make it challenging to implement any diagnosis model. A complete patient profile cannot be generated due to missing values, which may affect the final prediction. Outliers in a medical dataset represent extreme cases and rare conditions, or they may even be generated due to data entry errors. An excessive number of outliers may lead to a skewed and incorrect prediction. An imbalanced dataset makes it challenging to identify the minority classes appropriately and mostly generates a biased model for majority class instances. A combination of advanced preprocessing techniques and reliable model selection are required to address these challenges effectively. This paper proposes a data analytics pipeline on a Portable Health Clinic (PHC) dataset. The paper systematically evaluates different preprocessing methods for missing value imputation, outliers detection, and data balancing and offers a comprehensive preprocessing framework. Later, five state-of-the-art ensemble models for healthcare diagnosis were implemented along with a proposed ensemble machine learning model, KNN-XGBoost-SVM-Random Forest (KNN-X-SVM-R). The proposed model achieved an accuracy of 97.03% which supersedes all the other state-of-the-art models. To reaffirm the rectification of our model, we experimented with it on another COVID-19 routine blood test dataset. In both cases, our proposed model acquired better results regarding different performance measures. Validating the approach on a secondary dataset strengthens the robustness of the proposed methodology. The recommended preprocessing and modeling approach can be adopted to enhance diagnostic systems and improve patient outcomes.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101623"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Progressive multi-scale attention neural network for pneumonia classification in chest X-rays","authors":"Mohammad Reza Mahdiani","doi":"10.1016/j.imu.2025.101646","DOIUrl":"10.1016/j.imu.2025.101646","url":null,"abstract":"<div><div>We propose a novel Progressive Multi-Scale Attention Network (PMSAN) with an integrated Edge-Aware Loss function for improved pneumonia classification in chest X-rays. Unlike previous methods that overlook fine-grained edge information or fail to integrate multi-scale contextual features, our approach synergistically combines convolutional multi-scale feature extraction using depthwise separable convolutions with cross-layer feature fusion, Transformer blocks, advanced attention mechanisms<strong>,</strong> and a custom loss function that emphasizes diagnostically relevant edge details using Canny edge detection. Evaluated on the Kaggle chest X-ray pneumonia dataset—with optimal hyperparameters determined via extensive Optuna-based search—our model achieves a cross-validated accuracy of 97.3 % ± 0.4 % and an AUC of 0.995 <strong>±</strong> 0.002 on the test set. Ablation studies and statistical significance tests confirm the contributions of each component, while visualizations demonstrate the model's ability to focus on clinically relevant regions. These substantial performance gains, along with a significant reduction in misdiagnoses<strong>,</strong> underscore the clinical potential of our efficient and accurate approach in supporting radiologists and improving patient outcomes.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"55 ","pages":"Article 101646"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PolyNet: A self-attention based CNN model for classifying the colon polyp from colonoscopy image","authors":"Khaled Eabne Delowar ,&nbsp;Mohammed Borhan Uddin ,&nbsp;Md Khaliluzzaman ,&nbsp;Riadul Islam Rabbi ,&nbsp;Md Jakir Hossen ,&nbsp;M. Moazzam Hossen","doi":"10.1016/j.imu.2025.101654","DOIUrl":"10.1016/j.imu.2025.101654","url":null,"abstract":"<div><div>Colon polyps are small, precancerous growths in the colon that can indicate colorectal cancer (CRC), a disease that has a significant impact on public health. A colonoscopy is a medical procedure that helps detect colon polyps. However, the manual examination for identifying the type of polyps can be time-consuming, tedious, and prone to human error. Automatic classification of polyps through colonoscopy images can be more efficient. However, there are currently no specialized methods for the classification of polyps from colonoscopy; however, several state-of-the-art CNN models can classify polyps. We are introducing a new CNN-based model called PolyNet, a model that shows the best accuracy of the polyps classification from the multiple models and which also performs better than pre-trained models such as VGG16, ResNet50, DenseNetV3, MobileNetV3, and InceptionV3, as well as nine other customized CNN-based models for classification. This study provides a sensitivity analysis to demonstrate how slight modifications in the network's architecture can impact the balance between accuracy and performance. We examined different CNN architectures and developed a good convolutional neural network (CNN) model for correctly predicting colon polyps using the Kvasir dataset. The self-attention mechanism is incorporated in the best CNN model, i.e., PolypNet, to ensure better accuracy. To compare, DenseNetV3, MobileNet-V3, Inception-V3, VGG16, and ResNet50 get 73.87 %, 69.38 %, 61.12 %, 84.00 %, and 86.12 % of accuracy on the Kvasir dataset, while PolypNet with attention archives 86 % accuracy, 86 % precision, 85 % recall, and an 86 % F1-score.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"56 ","pages":"Article 101654"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative performance analysis of ensemble learning methods for fetal health classification","authors":"Tasnim Bill Zannah ,&nbsp;Sadia Islam Tonni ,&nbsp;Md. Alif Sheakh ,&nbsp;Mst. Sazia Tahosin ,&nbsp;Afjal Hossan Sarower ,&nbsp;Mahbuba Begum","doi":"10.1016/j.imu.2025.101656","DOIUrl":"10.1016/j.imu.2025.101656","url":null,"abstract":"<div><div>Fetal health monitoring is vital for early diagnosis and intervention during pregnancy, with cardiotocography (CTG) being a standard tool for assessing fetal well-being. However, CTG interpretation often suffers from subjectivity and inconsistency, motivating the need for automated, accurate, and interpretable diagnostic models. To address these challenges, we propose a robust machine learning framework that combines effective feature selection and ensemble learning with explainability. Specifically, Recursive Feature Elimination is used to reduce redundancy and identify the ten most discriminative features. An ensemble classifier, integrating Decision Tree, Random Forest, and Gradient Boosting, is developed to enhance classification accuracy. The model is trained and evaluated on a publicly available CTG dataset, achieving 99.56 % accuracy, 99.54 % precision, 99.59 % recall, and a 99.56 % F1 score. To ensure generalization, we conducted K-fold cross-validation and confusion matrix analysis. For interpretability, the framework incorporates Local Interpretable Model-agnostic Explanation, revealing influential features in each prediction. Compared to existing approaches, our model demonstrates superior performance and transparency, offering a practical and reliable decision-support system for fetal health assessment in clinical settings.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"56 ","pages":"Article 101656"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144105114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving resectable gastric cancer prognosis prediction: A machine learning analysis combining clinical features and body composition radiomics","authors":"Gianni S.S. Liveraro ,&nbsp;Maria E.S. Takahashi ,&nbsp;Fabiana Lascala ,&nbsp;Luiz R. Lopes ,&nbsp;Nelson A. Andreollo ,&nbsp;Maria C.S. Mendes ,&nbsp;Jun Takahashi ,&nbsp;José B.C. Carvalheira","doi":"10.1016/j.imu.2024.101608","DOIUrl":"10.1016/j.imu.2024.101608","url":null,"abstract":"<div><div>We evaluate the significance of body composition radiomics in predicting outcomes for resectable gastric cancer (GC) patients, as these parameters can enhance optimal surveillance strategies and risk-stratification models. Automated segmentation using deep learning algorithms was employed on CT images to analyze body composition in 276 GC patients, retrospectively recruited from the Clinical Hospital of the University of Campinas. Radiomics features of skeletal muscle (SM), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) were calculated. Body composition radiomics were integrated with clinicopathological factors using machine learning (ML) algorithms trained for patient outcome prediction. We compared results using Random Forest, Logistic Regression and Boosted Decision Tree algorithms. To identify the relevant features for the prognosis, recursive feature inclusion (RFI) was performed using SHAP Importance ranking. Our study uncovered novel body composition radiomic features that enhance patient prognosis, particularly the 90th percentile radiodensity value (HU) for SM and VAT. The ML model output also refined pathological staging: Stage II patients with a higher predicted mortality risk by the model had overall survival (OS) similar to Stage III patients, while Stage III patients with lower predicted risk showed OS comparable to Stage II. This approach demonstrates that the integration of clinical and radiomic features enhances the accuracy of pathological staging and offers more detailed information to refine treatment strategies for gastric cancer patients. Skeletal muscle and visceral adipose tissue radiodensity percentiles emerged as crucial determinants of patient outcomes.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101608"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping the key players in Kawasaki disease; role of inflammatory genes and protein-protein interactions","authors":"Wael Hafez ,&nbsp;Feras Al-Obeidat ,&nbsp;Asrar Rashid ,&nbsp;Afsheen Raza ,&nbsp;Nouran Hamza ,&nbsp;Nesma Ahmed ,&nbsp;Marwa M. Abdeljawad ,&nbsp;Raziya Kadwa ,&nbsp;Abdelhameed Elmesery ,&nbsp;Muneir Gador ,&nbsp;Dina Khair ,&nbsp;Gihan Zina ,&nbsp;fatema Abdulaal ,&nbsp;Mina Wassef Girgiss ,&nbsp;Maha Abdelhadi ,&nbsp;Ahmed Abdelrahman ,&nbsp;Mahmad Anwar Ibrahim ,&nbsp;Mohamed El Sherbiny","doi":"10.1016/j.imu.2025.101645","DOIUrl":"10.1016/j.imu.2025.101645","url":null,"abstract":"<div><h3>Background</h3><div>Kawasaki disease <strong>(KD)</strong> is a complex acquired condition characterized by systemic blood vessel inflammation that primarily affects children under five years of age. It is clinically diagnosed as a syndrome, making it susceptible to misdiagnoses. Severe complications such as myocardial damage and coronary artery abnormalities can be fatal; thus, early diagnosis is critical for preventing disease progression. Currently, no specific diagnostic test can distinguish KD from viral or bacterial infections. Additionally, the molecular mechanisms underlying the disease remain unclear, hindering the development of targeted therapies.</div></div><div><h3>Objective</h3><div>This study aimed to identify the genetic patterns and molecular mechanisms associated with KD using a comprehensive gene expression analysis.</div></div><div><h3>Methods</h3><div>RNA sequencing and microarray genomic datasets were retrieved from the NCBI Gene Expression Omnibus (GEO). Four datasets (GSE68004, GSE63881, GSE73461, and GSE73463) were used for the final analysis. These datasets compared patients with KD to healthy controls, and patients with acute KD to convalescent patients. Differentially expressed genes (DEGs) were identified in the datasets. Enrichment analysis was conducted, followed by protein-protein interaction (PPI) network analysis to identify hub genes. Heatmaps were generated to visualize gene expression patterns.</div></div><div><h3>Results</h3><div>Eighteen hub genes were identified in the KD versus control comparison, whereas 20 hub genes were identified in the acute versus convalescent analysis. These genes play key roles in inflammation, cytokine storm, innate immune modulation, and endothelial damage.</div></div><div><h3>Conclusion</h3><div>This study provides valuable insights into the molecular mechanisms underlying KD, and identifies potential diagnostic biomarkers and therapeutic targets.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"56 ","pages":"Article 101645"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EMG-based body–machine interface for targeted trunk muscle activation","authors":"Carolina Correia ,&nbsp;Andrea Bandini ,&nbsp;Silvestro Micera ,&nbsp;Sara Moccia","doi":"10.1016/j.imu.2025.101641","DOIUrl":"10.1016/j.imu.2025.101641","url":null,"abstract":"<div><div>Deficits in trunk control, commonly observed in individuals with neurological conditions, can significantly impair balance, posture, and functional movements. Body–machine interfaces (BoMIs) are promising tools for trunk rehabilitation, as they can provide real-time feedback on user movements and muscle activity, allowing for continuous monitoring and guidance during motor control training. However, research on BoMIs for trunk rehabilitation is limited, and current methods often lack precision in addressing trunk muscle deficits. This work introduces a BoMI that combines trunk electromyography (EMG) and motion data to selectively modulate trunk muscle activity during motor control tasks. The system utilizes machine learning to generate personalized trunk motion trajectories based on predefined EMG profiles. Each trajectory is displayed on a screen as a moving target, which users must follow by controlling the BoMI with their trunk movements. We hypothesize that by visually guiding users to track these generated trajectories, the BoMI could evoke the EMG patterns implicitly encoded within them. Tested with neurotypical individuals, the BoMI effectively elicited the desired trunk EMG profiles, achieving a mean similarity index of 0.82 ± 0.13, a correlation coefficient of 0.95 ± 0.03, and minimal timing mismatches. These results support the feasibility of using an EMG-based BoMI for precise trunk muscle training, which could potentially assist therapists in more efficiently monitoring and adjusting patients’ muscle engagement during interventions. Future work will focus on developing a control framework to dynamically adapt task difficulty to users’ needs, expanding the approach to include additional trunk muscles, and evaluating its translation to individuals with trunk muscle impairments.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"56 ","pages":"Article 101641"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}