Biomedical Engineering Letters最新文献

{"title":"Early warning score and feasible complementary approach using artificial intelligence-based bio-signal monitoring system: a review.","authors":"Dogeun Park, Kwangsub So, Sunil Kumar Prabhakar, Chulho Kim, Jae Jun Lee, Jong-Hee Sohn, Jong-Ho Kim, Sang-Hwa Lee, Dong-Ok Won","doi":"10.1007/s13534-025-00486-4","DOIUrl":"10.1007/s13534-025-00486-4","url":null,"abstract":"<p><p>Early warning score (EWS) have become an essential component of patient safety strategies in healthcare environments worldwide. These systems aim to identify patients at risk of clinical deterioration by evaluating vital signs and other physiological parameters, enabling timely intervention by rapid response teams. Despite proven benefits and widespread adoption, conventional EWS have limitations that may affect their ability to effectively detect and respond to patient deterioration. There is growing interest in integrating continuous multimodal monitoring technologies and advanced analytics, particularly artificial intelligence (AI) and machine learning (ML)-based approaches, to address these limitations and enhance EWS performance. This review provides a comprehensive overview of the current state and potential future directions of AI-based bio-signal monitoring in early warning system. It examines emerging trends and techniques in AI and ML for bio-signal analysis, exploring the possibilities and potential applications of various bio-signals such as electroencephalography, electrocardiography, electromyography in early warning system. However, significant challenges exist in developing and implementing AI-based bio-signal monitoring systems in early warning system, including data acquisition strategies, data quality and standardization, interpretability and explainability, validation and regulatory approval, integration into clinical workflows, and ethical and legal considerations. Addressing these challenges requires a multidisciplinary approach involving close collaboration between healthcare professionals, data scientists, engineers, and other stakeholders. Future research should focus on developing advanced data fusion techniques, personalized adaptive models, real-time and continuous monitoring, explainable and reliable AI, and regulatory and ethical frameworks. By addressing these challenges and opportunities, the integration of AI and bio-signals into early warning systems can enhance patient monitoring and clinical decision support, ultimately improving healthcare quality and safety. In conclusion, integrating AI and bio-signals into the early warning system represents a promising approach to improve patient care outcomes and support clinical decision-making. As research in this field continues to evolve, it is crucial to develop safe, effective, and ethically responsible solutions that can be seamlessly integrated into clinical practice, harnessing the power of innovative technology to enhance patient care and improve individual and population health and well-being.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 4","pages":"717-734"},"PeriodicalIF":3.2,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12226448/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144576647","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":"Insights into motor impairment assessment using myographic signals with artificial intelligence: a scoping review.","authors":"Wonbum Sohn, M Hongchul Sohn, Jongsang Son","doi":"10.1007/s13534-025-00483-7","DOIUrl":"10.1007/s13534-025-00483-7","url":null,"abstract":"<p><p>Myographic signals can effectively detect and assess subtle changes in muscle function; however, their measurement and analysis are often limited in clinical settings compared to inertial measurement units. Recently, the advent of artificial intelligence (AI) has made the analysis of complex myographic signals more feasible. This scoping review aims to examine the use of myographic signals in conjunction with AI for assessing motor impairments and highlight potential limitations and future directions. We conducted a systematic search using specific keywords in the Scopus and PubMed databases. After a thorough screening process, 111 relevant studies were selected for review. These studies were organized based on target applications (measurement modality, measurement location, and AI application task), sample demographics (age, sex, ethnicity, and pathology), and AI models (general approach and algorithm type). Among various myographic measurement modalities, surface electromyography was the most commonly used. In terms of AI approaches, machine learning with feature engineering was the predominant method, with classification tasks being the most common application of AI. Our review also noted a significant bias in participant demographics, with a greater representation of males compared to females and healthy individuals compared to clinical populations. Overall, our findings suggest that integrating myographic signals with AI has the potential to provide more objective and clinically relevant assessments of motor impairments.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 4","pages":"693-716"},"PeriodicalIF":3.2,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144576649","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":"Ipnet: informative patches learning for semi-supervised magnetic resonance image segmentation.","authors":"Guangxing Du, Rui Wu, Jinming Xu, Xiang Zeng, Shengwu Xiong","doi":"10.1007/s13534-025-00481-9","DOIUrl":"https://doi.org/10.1007/s13534-025-00481-9","url":null,"abstract":"<p><p>Semi-supervised learning has become a favorable method for medical image segmentation due to the high cost of obtaining labeled data in the field of medical image analysis. However, existing magnetic resonance images have low contrast, the scale and shape of organs vary greatly under different slice perspectives. Although existing methods have made some progress, they still cannot handle these challenging samples well. To this end, we propose a semi-supervised magnetic resonance images segmentation method based on informative patches learning (IPNet), which focuses on the learning of challenging regions. Specifically, we design a novel informative patch scoring strategy based on prediction uncertainty and category diversity, which can accurately identify challenging regions in samples. And to ensure that the informative patch is fully learned, the patch with the lowest score in one sample is replaced with the patch with the highest score in another sample to obtain a new pair of training samples. Furthermore, we introduce global and local consistency losses to supervise the new samples, guide the model to focus on the global and local features of the informative patches. To evaluate the effectiveness of the method, we conducted experiments on three magnetic resonance image datasets (ACDC, PROMISE 12 and LA datasets). Extensive experimental results demonstrate the effectiveness and superior performance of the proposed method.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 4","pages":"797-807"},"PeriodicalIF":3.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585256","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":"Machine learning detection of epileptic seizure onset zone from iEEG.","authors":"Nawara Mahmood Broti, Masaki Iwasaki, Yumie Ono","doi":"10.1007/s13534-025-00480-w","DOIUrl":"10.1007/s13534-025-00480-w","url":null,"abstract":"<p><p>Accurate identification of seizure onset zones (SOZ) is essential for the surgical treatment of epilepsy. This narrative review examines recent advances in machine learning approaches for SOZ localization using intracranial electroencephalography (iEEG) data. Existing studies are analyzed while addressing key questions: What machine learning techniques are used for SOZ localization? How effective are these methods? What are the limitations, and what solutions can drive further progress in the field? This narrative review examined peer-reviewed studies that employed machine learning techniques for SOZ localization using iEEG data. The selected studies were analyzed to identify trends in machine learning applications, performance metrics, benefits, and challenges associated with SOZ identification. The review highlights the increasing adoption of machine learning for SOZ localization, mostly with supervised approaches. Particularly support vector machine (SVM) using high frequency oscillation (HFO) biomarker feature being the most prevalent. High accuracy and sensitivity, especially in studies with smaller sample sizes are reported. However, patient-wise validation reveals limited generalizability. Additionally, ambiguity in SOZ definition and the scarcity of open-access iEEG datasets continue to hinder progress and reproducibility in the field. Machine learning offers significant potential for advancing SOZ localization. Development of more robust algorithms, integration of multimodal data, and greater model interpretability, can improve model reliability, ensure consistency, and enhance real-world applicability, thereby transforming the future of SOZ localization.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 4","pages":"677-692"},"PeriodicalIF":3.2,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229413/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144576650","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":"Efficient sparse-view medical image classification for low radiation and rapid COVID-19 diagnosis.","authors":"Seunghyun Gwak, Sooyoung Yang, Heawon Jeong, Junhu Park, Myungjoo Kang","doi":"10.1007/s13534-025-00478-4","DOIUrl":"10.1007/s13534-025-00478-4","url":null,"abstract":"<p><p>This study proposes a deep learning-based diagnostic model called the Projection-wise Masked Autoencoder (ProMAE) for rapid and accurate COVID-19 diagnosis using sparse-view CT images. ProMAE employs a column-wise masking strategy during pre-training to effectively learn critical diagnostic features from sinograms, even under extremely sparse conditions. The trained ProMAE can directly classify sparse-view sinograms without requiring CT image reconstruction. Experiments on sparse-view data with 50%, 75%, 85%, 95%, and 99% sparsity show that ProMAE achieves a diagnostic accuracy of over 95% at all sparsity levels and, in particular, outperforms ResNet, ConvNeXt, and conventional MAE models in COVID-19 diagnosis in environments with 85% or higher sparsity. This capability is especially advantageous for the development of portable and flexible imaging systems during large-scale outbreaks such as COVID-19, as it ensures accurate diagnosis while minimizing radiation exposure, making it a vital tool in resource-limited and high-demand settings.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 4","pages":"785-795"},"PeriodicalIF":3.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229398/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144576648","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":"Artificial intelligence in ECG-based diagnosis of low left ventricular ejection fraction: a systematic review and meta-analysis.","authors":"Gayathiri R R, Arya Bhardwaj, R Pradeep Kumar, Bala Chakravarthy Neelapu, Kunal Pal, J Sivaraman","doi":"10.1007/s13534-025-00479-3","DOIUrl":"https://doi.org/10.1007/s13534-025-00479-3","url":null,"abstract":"<p><p>Detecting Left Ventricular Systolic Dysfunction (LVSD) is crucial for counteracting heart failure progression. While Electrocardiograms (ECG) are widely used, their standalone diagnostic accuracy is insufficient. Integrating Artificial Intelligence (AI) with ECG analysis offers a promising approach to enhance precision. A systematic review was conducted to assess AI-enabled ECG for LVSD detection. Of 394 initial studies, 19 qualified for the systematic review, with 17 incorporated into meta-analysis. Study quality was gauged using QUADAS-2. Univariate meta-analysis, Spearman correlation, and bivariate meta-analyses were performed, along with publication bias assessment. The pooled sensitivity and specificity for AI-enabled ECG models were 86.9% and 84.4%, respectively. Studies with an ejection fraction (EF) threshold of 35% had the highest sensitivity, while those with 50% showed lower sensitivity and specificity. A weak positive Spearman correlation was found across all studies (ρ = 0.374, <i>p</i> = 0.066). A strong positive correlation for externally validated studies (ρ = 0.696, <i>p</i> = 0.008), and a weak negative correlation for test-only studies, indicated a threshold effect. Hierarchical summary receiver operating characteristic curve showed diagnostic robustness for studies with a 40% EF threshold; however, it showed a lack of real-world generalizability for test-only studies. AI-enabled ECG models show strong diagnostic potential for severe LVSD but remain limited for mild cases. External validation is essential for robustness and generalizability. Future research should enhance diagnostic accuracy for mild LVSD and address publication bias to optimize AI-based tools.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 4","pages":"661-676"},"PeriodicalIF":3.2,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585253","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":"Sensor arrangement strategy for effective bowel sound source localization.","authors":"Kenji Takawaki, Takeyuki Haraguchi, Takahiro Emoto","doi":"10.1007/s13534-025-00476-6","DOIUrl":"https://doi.org/10.1007/s13534-025-00476-6","url":null,"abstract":"<p><p>The main purpose of this study is to develop a method that can objectively evaluate the intestinal motility of patients with functional gastrointestinal disorders through non-invasive means. The research question focuses on whether the asymmetry in electronic stethoscope (ES) arrangements can enhance the accuracy of bowel sound (BS) source localization, which is crucial for detailed assessments of intestinal motility. This study introduced a new index called the angle-based asymmetry degree ([Formula: see text]), derived from the interior angles of the quadrilateral formed by the ESs, to quantitatively evaluate the asymmetry of four-ES-based arrangement patterns. We conducted simulations in an abdominal acoustic environment to compare the effects of symmetric and asymmetric ES arrangements on BS source localization accuracy. The influence of different [Formula: see text] values on localization performance was also analyzed under various signal-to-noise ratio ([Formula: see text]) conditions. The simulations revealed that BS source localization accuracy improved as the [Formula: see text] increased. Asymmetric ES arrangements significantly enhanced the localization accuracy compared to conventional symmetric arrangements, even in environments with high levels of noise. Additionally, various ES arrangement patterns corresponding to different [Formula: see text] values demonstrated improvements in localization accuracy. The study concludes that using asymmetric ES arrangements based on the newly proposed [Formula: see text] index substantially improves BS source localization accuracy. These findings suggest that asymmetry in ES placements can be a critical factor in enhancing non-invasive evaluations of intestinal motility, thereby contributing to the development of more effective BS source localization technologies. The results hold promise for practical applications in diagnosing and managing functional gastrointestinal disorders.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 4","pages":"763-772"},"PeriodicalIF":3.2,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229448/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585257","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":"Facial electromyogram-based emotion recognition for virtual reality applications using machine learning classifiers trained on posed expressions.","authors":"Jung-Hwan Kim, Ho-Seung Cha, Chang-Hwan Im","doi":"10.1007/s13534-025-00477-5","DOIUrl":"https://doi.org/10.1007/s13534-025-00477-5","url":null,"abstract":"<p><p>Recognition of human emotions holds great potential for various daily-life applications. With the increasing interest in virtual reality (VR) technologies, numerous studies have proposed new approaches to integrating emotion recognition into VR environments. However, despite recent advancements, camera-based emotion-recognition technology faces critical limitations due to the physical obstruction caused by head-mounted displays (HMDs). Facial electromyography (fEMG) offers a promising alternative for human emotion-recognition in VR environments, as electrodes can be readily embedded in the padding of commercial HMD devices. However, conventional fEMG-based emotion recognition approaches, although not yet developed for VR applications, require lengthy and tedious calibration sessions. These sessions typically involve collecting fEMG data during the presentation of audio-visual stimuli for eliciting specific emotions. We trained a machine learning classifier using fEMG data acquired while users intentionally made posed facial expressions. This approach simplifies the traditionally time-consuming calibration process, making it less burdensome for users. The proposed method was validated using 20 participants who made posed facial expressions for calibration and then watched emotion-evoking video clips for validation. The results demonstrated the effectiveness of our method in classifying high- and low-valence states, achieving a macro F1-score of 88.20%. This underscores the practicality and efficiency of the proposed method. To the best of our knowledge, this is the first study to successfully build an fEMG-based emotion-recognition model using posed facial expressions. This approach paves the way for developing user-friendly interface technologies in VR-immersive environments.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 4","pages":"773-783"},"PeriodicalIF":3.2,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585255","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":"A study for expert-informed active pulmonary nodule segmentation.","authors":"Shuangping Tan, Tong Zhang, Youfeng Deng, Zhimin Nie, Xiali Wu, Xinyue Yan, Xiaojuan Zhang, Huike Yi, Xianci Song, Jun Li","doi":"10.1007/s13534-025-00474-8","DOIUrl":"https://doi.org/10.1007/s13534-025-00474-8","url":null,"abstract":"<p><p>Accurate segmentation of pulmonary nodule based on computed tomography (CT) images is of great significance for the diagnosis and treatment of lung cancer. However, the current popular segmentation algorithms usually do not involve expert knowledge from radiologists, thereby carrying the risk of failing to produce generalizable and trustworthy models. In this study, we develop an expert-informed active pulmonary nodule segmentation method that iteratively optimize a deep segmentation model using an active learning scheme. The uncertainties from both intermediate segmentation results and correction inputs from radiologists are combined effectively. Interactive graph interfaces are developed to enable online corrections, significantly facilitating the integration of expert knowledge from radiologists. Evaluation results on the Luna16 dataset demonstrate that the proposed approach significantly promotes the segmentation performance of pulmonary nodules. The proposed method can effectively incorporate expert knowledge of multiple radiologists into deep segmentation algorithms, which not only promote the segmentation performance, but also enhance the validity, reliability, and generalizability of computer-aided diagnosis methods.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 4","pages":"735-748"},"PeriodicalIF":3.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229428/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585251","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":"Advances in cardiovascular signal analysis with future directions: a review of machine learning and deep learning models for cardiovascular disease classification based on ECG, PCG, and PPG signals.","authors":"Yunendah Nur Fuadah, Ki Moo Lim","doi":"10.1007/s13534-025-00473-9","DOIUrl":"https://doi.org/10.1007/s13534-025-00473-9","url":null,"abstract":"<p><p>This systematic review examines the transformative impact of artificial intelligence (AI), including machine learning (ML) and deep learning (DL), on cardiovascular signal analysis, focusing on electrocardiograms (ECG), phonocardiograms (PCG), and photoplethysmograms (PPG). It evaluates state-of-the-art methodologies that enhance diagnostic accuracy and predictive analytics by leveraging AI-driven systems. A wide range of public and private datasets are assessed, with attention to their strengths and limitations in supporting cardiovascular diagnostics. Key preprocessing techniques, such as noise reduction, signal normalization, and artifact mitigation, are explored for their role in improving signal quality. The review also highlights feature extraction methods, from time-domain and frequency-domain analyses to advanced morphological and spectral approaches, which contribute to robust classifier performance. Traditional ML models, such as K-Nearest Neighbor (KNN), Support Vector Machines (SVM) and Random Forests (RF), are compared with advanced DL architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and transfer learning models for detecting cardiovascular diseases. Despite these advancements, challenges such as dataset heterogeneity, preprocessing variability, and computational complexity persist, hindering clinical adoption. The review underscores the importance of large-scale, diverse datasets, multi-modal signal integration, and explainable AI to foster clinical trust and facilitate ethical deployment. By addressing these challenges, this review highlights the potential of AI to revolutionize cardiovascular healthcare through early diagnosis, wearable technology, and real-time decision support, paving the way for precision medicine and improved patient outcomes.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 4","pages":"619-660"},"PeriodicalIF":3.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229450/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585252","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}