Medical & Biological Engineering & Computing最新文献_第3页

Interpretable machine learning models for COPD ease of breathing estimation. COPD呼吸便利度评估的可解释机器学习模型。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-05-01 Epub Date: 2025-01-14 DOI: 10.1007/s11517-025-03285-2

Thomas T Kok, John Morales, Dirk Deschrijver, Dolores Blanco-Almazán, Willemijn Groenendaal, David Ruttens, Christophe Smeets, Vojkan Mihajlović, Femke Ongenae, Sofie Van Hoecke

{"title":"Interpretable machine learning models for COPD ease of breathing estimation.","authors":"Thomas T Kok, John Morales, Dirk Deschrijver, Dolores Blanco-Almazán, Willemijn Groenendaal, David Ruttens, Christophe Smeets, Vojkan Mihajlović, Femke Ongenae, Sofie Van Hoecke","doi":"10.1007/s11517-025-03285-2","DOIUrl":"10.1007/s11517-025-03285-2","url":null,"abstract":"Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide and greatly reduces the quality of life. Utilizing remote monitoring has been shown to improve quality of life and reduce exacerbations, but remains an ongoing area of research. We introduce a novel method for estimating changes in ease of breathing for COPD patients, using obstructed breathing data collected via wearables. Physiological signals were recorded, including respiratory airflow, acceleration, audio, and bio-impedance. By comparing patient-specific measurements, this approach enables non-intrusive remote monitoring. We analyze the influence of signal selection, window parameters, feature engineering, and classification models on predictive performance, finding that acceleration signals are most effective, complemented by audio signals. The best model achieves an F1-score of 0.83. To facilitate clinical adoption, we incorporate interpretability by designing novel saliency map methods, highlighting important aspects of the signals. We adapt local explainability techniques to time series and introduce a novel imputation method for periodic signals, improving faithfulness to the data and interpretability.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1481-1495"},"PeriodicalIF":2.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142980490","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}

引用次数: 0

Systematic review of computational techniques, dataset utilization, and feature extraction in electrocardiographic imaging. 系统回顾心电图成像中的计算技术、数据集利用和特征提取。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-05-01 Epub Date: 2025-01-09 DOI: 10.1007/s11517-024-03264-z

Dagoberto Mayorca-Torres, Alejandro J León-Salas, Diego H Peluffo-Ordoñez

引用次数: 0

Foot tissue stress in chronic ankle instability during the stance phase of cutting. 脚部组织应力在慢性踝关节不稳定的立场阶段切割。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-05-01 Epub Date: 2025-01-15 DOI: 10.1007/s11517-024-03276-9

Peimin Yu, Xuanzhen Cen, Liangliang Xiang, Alan Wang, Yaodong Gu, Justin Fernandez

{"title":"Foot tissue stress in chronic ankle instability during the stance phase of cutting.","authors":"Peimin Yu, Xuanzhen Cen, Liangliang Xiang, Alan Wang, Yaodong Gu, Justin Fernandez","doi":"10.1007/s11517-024-03276-9","DOIUrl":"10.1007/s11517-024-03276-9","url":null,"abstract":"Lower limb biomechanics of chronic ankle instability (CAI) individuals has been widely investigated, but few have evaluated the internal foot mechanics in CAI. This study evaluated bone and soft tissue stress in CAI contrasted with copers and non-injured participants during a cutting task. Integrating scanned 3D foot shapes and free-form deformation, sixty-six personalized finite element foot models were developed. Computed Achilles tendon forces and measured regional plantar pressure were applied as boundary loading conditions for simulation. It was observed that the primary group differences in foot stress occurred during midstance and heel-off phases of the cutting task. Specifically, healthy individuals had significantly higher stress in the talus and soft tissue around the talus compared to CAI participants. In contrast, CAI participants had significantly higher stress in the cuneiforms and lateral forefoot bones during mid-stance and push-off phases. CAI participants appeared to adopt a protective strategy by transferring greater force to the lateral forefoot at the heel-off phase while lowering stress around the talus, which may be associated with pain relief near the ankle. These findings suggest further attention should be placed on internal stress in CAI at the push-off phase with implications for long-term foot adaptation.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1507-1519"},"PeriodicalIF":2.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12064455/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015130","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}

引用次数: 0

Improved deep canonical correlation fusion approach for detection of early mild cognitive impairment. 改进的深度典型相关融合方法用于早期轻度认知障碍的检测。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-05-01 Epub Date: 2025-01-14 DOI: 10.1007/s11517-024-03282-x

Sreelakshmi Shaji, Rohini Palanisamy, Ramakrishnan Swaminathan

{"title":"Improved deep canonical correlation fusion approach for detection of early mild cognitive impairment.","authors":"Sreelakshmi Shaji, Rohini Palanisamy, Ramakrishnan Swaminathan","doi":"10.1007/s11517-024-03282-x","DOIUrl":"10.1007/s11517-024-03282-x","url":null,"abstract":"Detection of early mild cognitive impairment (EMCI) is clinically challenging as it involves subtle alterations in multiple brain sub-anatomic regions. Among different brain regions, the corpus callosum and lateral ventricles are primarily affected due to EMCI. In this study, an improved deep canonical correlation analysis (CCA) based framework is proposed to fuse magnetic resonance (MR) image features from lateral ventricular and corpus callosal structures for the detection of EMCI condition. For this, obtained structural MR images of healthy controls and EMCI subjects are preprocessed. Lateral ventricles and corpus callosum structures are segmented from these images and features are extracted. Extracted features from different brain structures are fused using non-linear orthogonal iteration-based deep CCA. Fused features are employed to differentiate healthy controls and EMCI condition using extreme learning machine classifier. Results indicate that fused callosal and ventricular features are able to detect EMCI. Improved deep CCA algorithm with tuned hyperparameters achieves the highest classifier performance with an F-score of 82.15%. The proposed framework is compared with state-of-the-art CCA approaches, and the results demonstrate its improved performance in EMCI detection. This highlights the potential of the proposed framework in the automated diagnosis of preclinical MCI conditions.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1451-1461"},"PeriodicalIF":2.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142980487","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}

引用次数: 0

Deep learning in 3D cardiac reconstruction: a systematic review of methodologies and dataset. 三维心脏重建中的深度学习：方法和数据集的系统回顾。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-05-01 Epub Date: 2025-01-04 DOI: 10.1007/s11517-024-03273-y

Rajendra Kumar Pandey, Yogesh Kumar Rathore

{"title":"Deep learning in 3D cardiac reconstruction: a systematic review of methodologies and dataset.","authors":"Rajendra Kumar Pandey, Yogesh Kumar Rathore","doi":"10.1007/s11517-024-03273-y","DOIUrl":"10.1007/s11517-024-03273-y","url":null,"abstract":"This study presents an advanced methodology for 3D heart reconstruction using a combination of deep learning models and computational techniques, addressing critical challenges in cardiac modeling and segmentation. A multi-dataset approach was employed, including data from the UK Biobank, MICCAI Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, and clinical datasets of congenital heart disease. Preprocessing steps involved segmentation, intensity normalization, and mesh generation, while the reconstruction was performed using a blend of statistical shape modeling (SSM), graph convolutional networks (GCNs), and progressive GANs. The statistical shape models were utilized to capture anatomical variations through principal component analysis (PCA), while GCNs refined the meshes derived from segmented slices. Synthetic data generated by progressive GANs enabled augmentation, particularly useful for congenital heart conditions. Evaluation of the reconstruction accuracy was performed using metrics such as Dice similarity coefficient (DSC), Chamfer distance, and Hausdorff distance, with the proposed framework demonstrating superior anatomical precision and functional relevance compared to traditional methods. This approach highlights the potential for automated, high-resolution 3D heart reconstruction applicable in both clinical and research settings. The results emphasize the critical role of deep learning in enhancing anatomical accuracy, particularly for rare and complex cardiac conditions. This paper is particularly important for researchers wanting to utilize deep learning in cardiac imaging and 3D heart reconstruction, bringing insights into the integration of modern computational methods.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1271-1287"},"PeriodicalIF":2.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142928351","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}

引用次数: 0

A fast-modeling framework for personalized human body models based on a single image. 基于单幅图像的个性化人体模型快速建模框架。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-05-01 Epub Date: 2024-12-30 DOI: 10.1007/s11517-024-03267-w

Qiuqi Yuan, Zhi Xiao, Xiaoming Zhu, Bin Li, Jingzhou Hu, Yunfei Niu, Shiwei Xu

引用次数: 0

Impact of backpack load during walking: an EMG and biomechanical analysis. 步行时背包负荷的影响：肌电图和生物力学分析。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-05-01 Epub Date: 2025-01-11 DOI: 10.1007/s11517-024-03280-z

Fırat Matur, Fatma Alnamroush, Bora Büyüksaraç

{"title":"Impact of backpack load during walking: an EMG and biomechanical analysis.","authors":"Fırat Matur, Fatma Alnamroush, Bora Büyüksaraç","doi":"10.1007/s11517-024-03280-z","DOIUrl":"10.1007/s11517-024-03280-z","url":null,"abstract":"This study aims to understand the impact of backpack carriage, a regular activity for many, on back muscles and joint mobility during walking so that clinicians can develop strategies or products to ensure individuals' safety and well-being. Surface electromyography (EMG) and XSENS Awinda motion capture systems were used to analyze the effects of carrying a backpack (12% of body weight) on erector spinae and multifidus muscles, as well as spinal, hip, knee, and ankle joints. Subjects walked at 4 km/h on flat and inclined surfaces. Paired t-tests compared backpack loads to baseline measurements. Carrying a backpack reduced activation levels in erector spinae and multifidus muscles and restricted spinal joint range of motion (axial and lateral bending, <math><mrow><mi>p</mi> <mo><</mo> <mn>0.05</mn></mrow> </math> ). Hip joint rotation increased ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.05</mn></mrow> </math> ). Moderate to strong correlations were observed between muscle activity and spinal joint ROM, notably with left erector spinae and L5-S1 lateral bending ( <math> <mrow><mrow><mi>r</mi> <mo>=</mo> <mn>0.723</mn></mrow> <mo>,</mo> <mrow><mi>p</mi> <mo><</mo> <mn>0.001</mn></mrow> </mrow> </math> ). Backpack carriage decreases back muscle activation and alters the joint range of motion. Asymmetric correlations show that the subjects adapt muscle activity and gait patterns asymmetrically to manage external loads.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1427-1433"},"PeriodicalIF":2.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12064632/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967347","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}

引用次数: 0

A systematic review of the blockchain application in healthcare research domain: toward a unified conceptual model. 系统回顾区块链在医疗保健研究领域的应用：走向统一的概念模型。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-05-01 Epub Date: 2025-01-10 DOI: 10.1007/s11517-024-03274-x

Seyma Cihan, Nebi Yılmaz, Adnan Ozsoy, Oya Deniz Beyan

{"title":"A systematic review of the blockchain application in healthcare research domain: toward a unified conceptual model.","authors":"Seyma Cihan, Nebi Yılmaz, Adnan Ozsoy, Oya Deniz Beyan","doi":"10.1007/s11517-024-03274-x","DOIUrl":"10.1007/s11517-024-03274-x","url":null,"abstract":"Recently, research on blockchain applications in the healthcare research domain has attracted increasing attention due to its strong potential. However, the existing literature reveals limited studies on defining use cases of blockchain in clinical research, categorizing and comparing available studies. Therefore, this study aims to explore the significant potential and use cases of blockchain in clinical research through a comprehensive systematic literature review (SLR). To thoroughly investigate all aspects of the subject, we analyzed primary studies based on research questions (RQs) and developed a unified conceptual model using step-based model creation. Studies from 2015 to 2023 were reviewed, and 34 primary studies were comprehensively analyzed by using the PICO template. In our findings, privacy emerged as the most frequently cited requirement in clinical research. The most mentioned use cases for blockchain are ensuring data immutability and security. A significant issue identified beyond the common blockchain limitations of capacity and scalability is the lack of standards for compliance with legal frameworks like GDPR and HIPAA. After all these efforts, we developed a conceptual model, which, to our best knowledge, is the first in the literature to support software developers and clinical researchers in developing and using blockchain-based research platforms efficiently.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1319-1342"},"PeriodicalIF":2.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12064621/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142957981","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}

引用次数: 0

Predicting doxorubicin-induced cardiotoxicity in breast cancer: leveraging machine learning with synthetic data. 预测乳腺癌中阿霉素引起的心脏毒性：利用机器学习与合成数据。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-05-01 Epub Date: 2025-01-20 DOI: 10.1007/s11517-025-03289-y

Daniella Castro Araújo, Ricardo Simões, Adriano de Paula Sabino, Angélica Navarro de Oliveira, Camila Maciel de Oliveira, Adriano Alonso Veloso, Karina Braga Gomes

{"title":"Predicting doxorubicin-induced cardiotoxicity in breast cancer: leveraging machine learning with synthetic data.","authors":"Daniella Castro Araújo, Ricardo Simões, Adriano de Paula Sabino, Angélica Navarro de Oliveira, Camila Maciel de Oliveira, Adriano Alonso Veloso, Karina Braga Gomes","doi":"10.1007/s11517-025-03289-y","DOIUrl":"10.1007/s11517-025-03289-y","url":null,"abstract":"Doxorubicin (DOXO) is a primary treatment for breast cancer but can cause cardiotoxicity in over 25% of patients within the first year post-chemotherapy. Recognizing at-risk patients before DOXO initiation offers pathways for alternative treatments or early protective actions. We analyzed data from 78 Brazilian breast cancer patients, with 34.6% developing cardiotoxicity within a year of their final DOXO dose. To address the limited sample size, we utilized the DAS (Data Augmentation and Smoothing) method, creating 4892 synthetic samples that exhibited high statistics fidelity to the original data. By integrating routine blood biomarkers (C-Reactive protein, total cholesterol, LDL-c, HDL-c, hematocrit, and hemoglobin) and two clinical measures (weighted smoking status and body mass index), our model achieved an AUROC of 0.85±0.10, a sensitivity of 0.89, and a specificity of 0.69, positioning it as a potential screening instrument. Notably, DAS outperformed the established methods, Adaptive Synthetic Sampling (ADASYN), Synthetic Minority Over-Sampling Technique (SMOTE), and Synthetic Data Vault (SDV), underscoring its promise for medical synthetic data generation and pioneering a cardiotoxicity prediction model specifically for DOXO.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1535-1550"},"PeriodicalIF":2.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015139","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}

引用次数: 0

Research on the analysis of morphological characteristics in pediatric femoral neck fractures utilizing 3D CT mapping. 儿童股骨颈骨折的三维CT成像形态学特征分析研究。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-05-01 Epub Date: 2025-01-15 DOI: 10.1007/s11517-024-03260-3

Niu-Niu Zhao, Xue-Lian Gu, Zhen-Zhen Dai, Chen-Chen Wu, Tian-Yi Zhang, Hai Li

{"title":"Research on the analysis of morphological characteristics in pediatric femoral neck fractures utilizing 3D CT mapping.","authors":"Niu-Niu Zhao, Xue-Lian Gu, Zhen-Zhen Dai, Chen-Chen Wu, Tian-Yi Zhang, Hai Li","doi":"10.1007/s11517-024-03260-3","DOIUrl":"10.1007/s11517-024-03260-3","url":null,"abstract":"Proximal femoral fractures in children are challenging in clinical treatment due to their unique anatomical and biomechanical characteristics. The distribution and characteristics of fracture lines directly affect the selection of treatment options and prognosis. Pediatric proximal femur fractures exhibit distinctive features, with the distribution and characteristics of the fracture line playing a crucial role in deciding optimal treatment. The study aims to investigate the morphological characteristics of pediatric femoral neck fracture (FNF) from clinical cases by fracture mapping technology and to analyze the relationship between fracture classifications and age. The CT data were collected from 46 consecutive pediatric inpatients' diagnoses of FNF from March 2009 to December 2022. The fracture imaging was reconstructed in three dimensions and performed the simulated anatomical reduction by Mimics and 3-matic. Both Delbet classification and Pauwels angle classification were documented according to the fracture line in each patient. Furthermore, all of the fracture lines in these patients were superimposed to form a fracture map and a heat map. This study included 24 boys and 22 girls (average age, 9.61 ± 3.17 years (4 to 16 years)). The fracture lines of the anterior and superior femoral neck were found to be mainly located in the middle and lower regions of the femoral neck, while fracture lines of the posterior and inferior neck were mainly concentrated in the middle region. Most children younger than 10 years had Delbet type III of fracture (69%), whereas those older than 10 years had Delbet type II of fracture (73%). Furthermore, most children had Pauwels angle type III of fracture (63%), especially in those over 10 years old (80%) (p = 0.0001). FNF in children is predominantly located in the middle and lower regions of the neck. Older children may be prone to be affected with higher fracture location of FNF or unstable type of fracture.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1497-1505"},"PeriodicalIF":2.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142984758","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}

引用次数: 0