Medical & Biological Engineering & Computing最新文献

{"title":"Towards biomimetic evolution of artificial intervertebral disc: a review.","authors":"Ashutosh Khanna, Pushpdant Jain, C P Paul","doi":"10.1007/s11517-025-03371-5","DOIUrl":"10.1007/s11517-025-03371-5","url":null,"abstract":"<p><p>In lumbar total disc replacement, artificial disc implants are utilized to cure degenerative disc disease and restore natural motion. Human intervertebral discs (IVD) are part of the spine and contribute to delivering six degrees of freedom, elastic deformation, and shock absorption and act differently under different load conditions. Despite advancements in spinal fixation systems and IVD replacement techniques, achieving long-term segmental stability while preserving physiological motion remains a significant challenge. To overcome this issue, the proposed work aims to identify the biomechanics of artificial IVD implants through rigorous analysis. The ultimate goal is to provide the information to explore the design and develop novel implants that seamlessly integrate with the spine, restoring natural spine function and providing long-term, sustainable load-bearing properties, mimicking the resilience and longevity of the natural IVD. To address all these issues, a comprehensive review of the literature was conducted, organizing findings based on body structure, associated diseases, biomechanics, and various IVD development models. The present endeavour involves a critical analysis with the aim of facilitating the input to the design and development of novel IVD implants in the future.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"2853-2869"},"PeriodicalIF":2.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144052040","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":"Analysis of anthropometric measurements in U-15 female weightlifters using Kinect camera and comparison with traditional methods.","authors":"Bülent Işik, Kenan Erdaği, Serkan Örücü, Usame Ömer Osmanoğlu, Erkan Özbay","doi":"10.1007/s11517-025-03373-3","DOIUrl":"10.1007/s11517-025-03373-3","url":null,"abstract":"<p><p>Anthropometric measurements are important in a wide range of areas, from athlete selection to performance analysis and medical exercise applications. With its portable and cost-effective structure, Microsoft Kinect offers significant advantages in measuring human movements and provides valuable data in the fields of sports science and medicine. The aim of this study is to compare certain anthropometric measurements of adolescent female weightlifters in the U-15 age category with Kinect V2 and traditional methods. Twelve athletes who won medals in international weightlifting championships were included in the study. Anthropometric evaluations of each athlete were performed using Kinect V2 and the traditional method. Differences between measurements were analyzed with Bland-Altman plots and Pearson correlation coefficients. Kinect was found to exhibit less variability and higher coherency in measurements of humerus, forearm, trochanter-tibiale laterale, tibial lengths, and shoulder width. It has been observed that the traditional method provides more coherence results in hand length measurements. Kinect was found to exhibit less variability and higher coherency in measurements of forearm, trochanter-tibiale laterale, tibial lengths, and shoulder width, indicating its potential as a reliable tool for these parameters. Notably, Kinect demonstrated high reliability for tibial length (ICC 0.918) and moderate reliability for trochanter-tibiale laterale (ICC 0.737), showing its effectiveness in these measurements. Additionally, Kinect exhibited lower variation and higher coherency in most measurements compared to manual methods, supporting its consistency and repeatability in anthropometric assessments. These results indicate that Kinect may be a suitable tool for some measurements but that traditional methods may be preferable for hand length measurements. These findings suggest that Kinect can be used effectively for certain anthropometric measurements in sports and medical science.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3003-3018"},"PeriodicalIF":2.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12436582/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081639","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":"Passive haptic interface for robot-assisted endovascular catheterization.","authors":"Yu Song, Yu Tian, Liutao Li, Qiang Gao, Zhiwei Li","doi":"10.1007/s11517-025-03374-2","DOIUrl":"10.1007/s11517-025-03374-2","url":null,"abstract":"<p><p>Master-slave vascular interventional surgical robots reduce surgeon's exposure to radiation during procedures. However, the master-slave structure keeps the surgeon away from the patient, the surgeon loses the sense of haptics while manipulating the robot, and the surgeon's sense of surgical presence is reduced. To solve this problem, we have developed a master robot with haptic feedback. The surgeon manipulates the master robot without changing the traditional surgical manipulation posture, and the magnetorheological fluid-based haptic interface generates passive haptic feedback to the surgeon. Magnetic field simulation analysis is used to optimize the parameters of the haptic interface. The haptic interface adopts a closed-loop control strategy based on the magnetic field-resistance prediction model, which uses the magnetic field information inside the device to complete the haptic force closed-loop control. The slave robot utilizes dual linear guides for precise delivery of catheters/guidewires. Experimentally verified that the developed master robot meets the surgeon's need for haptic feedback in vascular interventional procedures and has good applicability.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3067-3082"},"PeriodicalIF":2.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144278","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":"Emerging trends and clinical challenges in AI-enhanced emotion diagnosis using physiological data.","authors":"Ying-Ying Tsai, Guan-Lin Wu, Yu-Jie Chen, Yen-Feng Lin, Ju-Yu Wu, Ching-Han Hsu, Lun-De Liao","doi":"10.1007/s11517-025-03435-6","DOIUrl":"https://doi.org/10.1007/s11517-025-03435-6","url":null,"abstract":"&lt;p&gt;&lt;p&gt;This review explores the relationships between physiological parameters and emotions, as well as the potential value and applications of the use of machine learning to facilitate emotion recognition. First, the relationships between physiological parameters (such as heart rate, respiration, blood pressure, galvanic skin response, electroencephalography, and heart rate variability [HRV]) and emotions are discussed. The impacts of emotional states on these physiological parameters represent a crucial aspect of emotion research. For example, the increased heart rates and faster breathing resulting from excitement or anxiety are physiological changes that cannot be ignored. Subsequently, models used for emotion recognition are introduced. These models employ techniques such as machine learning or deep learning and are trained to detect emotional states on the basis of changes in physiological parameters. These techniques have important applications in clinical psychology, including by helping doctors assess patients' status, diagnose emotional disorders, and guide treatment. In the context of managing emotional disorders such as depression, anxiety, bipolar disorder, and borderline personality disorder, emotion recognition technologies can facilitate accurate emotional monitoring and early intervention, thereby reducing the risk of disease recurrence. These models can be used in the contexts of emotion management and health monitoring, thus helping individuals understand and cope with emotional changes more effectively and improving their quality of life. This paper identifies HRV, which reflects an individual's ability to adapt to stress, emotions, and physical conditions, as a key indicator that can be used in the contexts of emotion recognition and physiological parameter analysis. By incorporating HRV parameters into relevant models, emotional changes can be analyzed more precisely, thereby providing more effective emotion management and health monitoring tools, which can enhance individuals' quality of life. However, the use of these physiological parameters entails many challenges, including those pertaining to the collection of physiological data, privacy and security concerns, and the need for personalized adjustments as a result of the variability observed among individuals in this context. These challenges require continuous efforts on the part of technical experts and researchers to advance the development and application of emotion recognition technologies. Finally, this paper presents an in-depth investigation of the associations between physiological parameters and emotions, and it explores the potential value and challenges associated with the use of machine learning to facilitate emotion recognition. The results of these studies suggest that emotion recognition technology can be used more widely in the contexts of mental health, emotional management, and health monitoring to provide individuals with better emotional support and ","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145202078","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":"Generation of multimodal realistic computational phantoms as a test-bed for validating deep learning-based cross-modality synthesis techniques.","authors":"Francesca Camagni, Anestis Nakas, Giovanni Parrella, Alessandro Vai, Silvia Molinelli, Viviana Vitolo, Amelia Barcellini, Agnieszka Chalaszczyk, Sara Imparato, Andrea Pella, Ester Orlandi, Guido Baroni, Marco Riboldi, Chiara Paganelli","doi":"10.1007/s11517-025-03437-4","DOIUrl":"https://doi.org/10.1007/s11517-025-03437-4","url":null,"abstract":"<p><p>The validation of multimodal deep learning models for medical image translation is limited by the lack of high-quality, paired datasets. We propose a novel framework that leverages computational phantoms to generate realistic CT and MRI images, enabling reliable ground-truth datasets for robust validation of artificial intelligence (AI) methods that generate synthetic CT (sCT) from MRI, specifically for radiotherapy applications. Two CycleGANs (cycle-consistent generative adversarial networks) were trained to transfer the imaging style of real patients onto CT and MRI phantoms, producing synthetic data with realistic textures and continuous intensity distributions. These data were evaluated through paired assessments with original phantoms, unpaired comparisons with patient scans, and dosimetric analysis using patient-specific radiotherapy treatment plans. Additional external validation was performed on public CT datasets to assess the generalizability to unseen data. The resulting, paired CT/MRI phantoms were used to validate a GAN-based model for sCT generation from abdominal MRI in particle therapy, available in the literature. Results showed strong anatomical consistency with original phantoms, high histogram correlation with patient images (HistCC = 0.998 ± 0.001 for MRI, HistCC = 0.97 ± 0.04 for CT), and dosimetric accuracy comparable to real data. The novelty of this work lies in using generated phantoms as validation data for deep learning-based cross-modality synthesis techniques.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145182512","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":"Impact of fatigue levels on EEG-based personal recognition.","authors":"Xinghan Shao, C Chang, Haixian Wang","doi":"10.1007/s11517-025-03452-5","DOIUrl":"https://doi.org/10.1007/s11517-025-03452-5","url":null,"abstract":"<p><p>The uniqueness of the electroencephalogram (EEG), a distinct biometric marker inherent to each individual, yields significant advantages for user authentication and identification in brain-computer interface (BCI) systems. However, EEG features can easily change according to the user's state, which may affect the performance of biometric recognition systems based on EEG. Notably, in EEG data collection for such systems, fatigue levels can fluctuate over time-a factor that has yet to be thoroughly investigated concerning its impact on individual recognition performance. This study explores the implications of fatigue on EEG-based personal recognition systems. We derived six sub-datasets from two simulated driving datasets, each labeled with varying levels of fatigue. From each sub-dataset, we extracted six features for identity recognition within and across different fatigue levels. Single-session and cross-session studies revealed that the disparity of EEG fatigue levels between the training and testing sets increased, and system recognition accuracy experienced a decline. Specifically, recognition accuracy typically fell by over 30 <math><mo>%</mo></math> after 90 min of simulated driving. Furthermore, identity recognition results are better when the training set includes EEG in more fatigued states compared to the test set, rather than the other way around. Crucially, the method based on functional connectivity features shows the best recognition accuracy under different fatigue levels. This research emphasizes the potential benefits of considering fatigue variations in EEG-based personal recognition systems.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145151781","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":"Understanding the mismatch between in-vivo and in-silico rhinomanometry.","authors":"Marco Atzori, Gabriele Dini Ciacci, Maurizio Quadrio","doi":"10.1007/s11517-025-03450-7","DOIUrl":"https://doi.org/10.1007/s11517-025-03450-7","url":null,"abstract":"<p><p>Numerical simulations and clinical measurements of nasal resistance are in quantitative disagreement. The order of magnitude of this mismatch, that sometimes exceeds 100%, is such that known sources of uncertainty cannot explain it. The goal of the present work is to examine a source of bias introduced by the design of medical devices, which has not been considered until now as a possible explanation. We study the effect of the location of the probe on the rhinomanometer that is meant to measure the ambient pressure. Rhinomanometry is carried out on a 3D silicone model of a patient-specific anatomy; a clinical device and dedicated sensors are employed side-by-side for mutual validation. The same anatomy is also employed for numerical simulations, with approaches spanning a wide range of fidelity levels. We find that the intrinsic uncertainty of the numerical simulations is of minor importance. To the contrary, the position of the pressure tap intended to acquire the external pressure in the clinical device is crucial, and can cause a mismatch comparable to that generally observed between in-silico and in-vivo rhinomanometry data. A source of systematic bias may therefore exist in rhinomanometers, designed under the assumption that measurements of the nasal resistance are unaffected by the flow development within the instruments.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145139145","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":"Integrating CT image reconstruction, segmentation, and large language models for enhanced diagnostic insight.","authors":"Altamash Ahmad Abbasi, Ashfaq Hussain Farooqi","doi":"10.1007/s11517-025-03446-3","DOIUrl":"https://doi.org/10.1007/s11517-025-03446-3","url":null,"abstract":"<p><p>Deep learning has significantly advanced medical imaging, particularly computed tomography (CT), which is vital for diagnosing heart and cancer patients, evaluating treatments, and tracking disease progression. High-quality CT images enhance clinical decision-making, making image reconstruction a key research focus. This study develops a framework to improve CT image quality while minimizing reconstruction time. The proposed four-step medical image analysis framework includes reconstruction, preprocessing, segmentation, and image description. Initially, raw projection data undergoes reconstruction via a Radon transform to generate a sinogram, which is then used to construct a CT image of the pelvis. A convolutional neural network (CNN) ensures high-quality reconstruction. A bilateral filter reduces noise while preserving critical anatomical features. If required, a medical expert can review the image. The K-means clustering algorithm segments the preprocessed image, isolating the pelvis and removing irrelevant structures. Finally, the FuseCap model generates an automated textual description to assist radiologists. The framework's effectiveness is evaluated using peak signal-to-noise ratio (PSNR), normalized mean square error (NMSE), and structural similarity index measure (SSIM). The achieved values-PSNR 30.784, NMSE 0.032, and SSIM 0.877-demonstrate superior performance compared to existing methods. The proposed framework reconstructs high-quality CT images from raw projection data, integrating segmentation and automated descriptions to provide a decision-support tool for medical experts. By enhancing image clarity, segmenting outputs, and providing descriptive insights, this research aims to reduce the workload of frontline medical professionals and improve diagnostic efficiency.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145138954","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":"Pain assessment and determination methods with wearable sensors: a scoping review.","authors":"Beren Semiz, Özge Kartin Hancioglu, Remziye Semerci Şahin","doi":"10.1007/s11517-025-03448-1","DOIUrl":"https://doi.org/10.1007/s11517-025-03448-1","url":null,"abstract":"<p><p>There is no gold standard for objectively measuring pain; wearable devices cannot claim to measure pain itself, but may offer correlational insights through physiological signals. This scoping review synthesizes current evidence on pain-related assessment methods using wearable sensors across pediatric and adult populations. This review followed the PRISMA-ScR guidelines. A systematic literature search was conducted across PubMed, Cochrane Library, Scopus, Web of Science, CINAHL, and Ovid MEDLINE for studies published up to December 2024. A total of 24 studies met the inclusion criteria. The most used wearable devices included commercially available smartwatches, wristbands, and multisensor platforms. Physiological indicators associated with pain responses included heart rate, heart rate variability, electrocardiography, electrodermal activity, electromyography, surface electromyography, photoplethysmography, skin temperature, and electroencephalography, reflecting autonomic, muscular, and neural system activity. Wearable sensors represent a promising, non-invasive tool for capturing physiological pain-related responses, particularly in contexts where verbal self-report is not feasible. While these devices may support more responsive and continuous pain monitoring, they cannot replace self-report measures and should not be interpreted as providing objective pain measurements.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132356","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 biomedical engineer's pledge: overview and context.","authors":"Antoni Ivorra, Txetxu Ausín, Laura Becerra-Fajardo, Antonio J Del Ama, Jesús Minguillón, Aracelys García-Moreno, Jordi Aguiló, Filipe Oliveira Barroso, Bart Bijnens, Oscar Camara, Sara Capdevila, Roger Castellanos Fernandez, Rafael V Davalos, Jean-Louis Divoux, Ahmed Eladly, Dario Farina, Carla García Hombravella, Raquel González López, Cesar A Gonzalez, Jordi Grífols, Felipe Maglietti, Shahid Malik, Elad Maor, Guillermo Marshall, Berta Mateu Yus, Lluis M Mir, Juan C Moreno, Xavier Navarro, Núria Noguera, Andrés Ozaita, Gemma Piella, José L Pons, Rita Quesada, Pilar Rivera-Gil, Boris Rubinsky, Aurelio Ruiz Garcia, Albert Ruiz-Vargas, Maria Sánchez Sánchez, Andreas Schneider-Ickert, Ting Shu, Rosa Villa Sanz, Bing Zhang, Gema Revuelta","doi":"10.1007/s11517-025-03443-6","DOIUrl":"https://doi.org/10.1007/s11517-025-03443-6","url":null,"abstract":"<p><p>Although biomedical engineering (BME) is a profession with ethical responsibilities comparable to those in medicine, it has, until now, lacked a counterpart to the Hippocratic Oath. While professional societies have established codes of ethics for biomedical engineers, these documents lack the symbolic and ceremonial significance of an oath or pledge. By contrast, the recitation of the Hippocratic Oath, or its modern version, the \"Physician's Pledge,\" serves as a powerful rite of passage for medical students, fostering a strong sense of ethical duty at the start of their professional journey. However, the content of the Hippocratic Oath includes elements specific to clinical practice and is not directly applicable to biomedical engineering. To fill this gap, we have created a \"Biomedical Engineer's Pledge,\" comprising a preamble, ten promises, and a concluding statement, to inspire ethical awareness and establish a meaningful graduation tradition.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145139131","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}