Medical & Biological Engineering & Computing最新文献

BrainUMA: A Unified multi-atlas learning framework for brain disorders diagnosis. BrainUMA：用于脑部疾病诊断的统一多图谱学习框架。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-05-08 DOI: 10.1007/s11517-026-03581-5

Maochun Hao, Peng Cao, Guangqi Wen, Jinzhu Yang, Osmar R Zaiane

{"title":"BrainUMA: A Unified multi-atlas learning framework for brain disorders diagnosis.","authors":"Maochun Hao, Peng Cao, Guangqi Wen, Jinzhu Yang, Osmar R Zaiane","doi":"10.1007/s11517-026-03581-5","DOIUrl":"https://doi.org/10.1007/s11517-026-03581-5","url":null,"abstract":"Functional connectivity analysis of brain networks has provided valuable insights for brain disorders diagnosis. Recent studies have focused on collaborative learning with multiple brain atlases to overcome the limitations of single-atlas information. However, these approaches often overlook sufficient interaction and consistency among multiple atlases, as well as information redundancy resulting from multi-atlas fusion. We propose a unified multi-atlas learning framework (BrainUMA) with hyper-connectivity network learning for brain disorders diagnosis, which consists of two key stages: hyper-connectivity network construction, and cross-atlas HCN interactions. We employ FCN for hyper-connectivity network construction and propose a novel hyper-connectivity network construction strategy, which includes both the hypergraph structure construction and node feature learning. Meanwhile, to sufficiently model interactions across multiple atlases, we propose a feature disentanglement method that disentangles disease-related information with hyperedge-aware hypergraph convolutional networks. We introduce two loss functions: an atlas-based contrastive loss and a class-consistency loss to guide the disentanglement processes. We evaluate our model on the public Autism Brain Imaging Data Exchange (ABIDE) dataset to demonstrate the effectiveness of the proposed model and investigate the optimal combination of brain atlases. Our results shed new light on the importance of exploiting the relationship among by disentanglement for improving multi-atlas disease diagnosis. In addition, our model provides deeper insights into disease interpretability, including atlas properties and critical brain regions. Our code is publicly available at https://github.com/MortonHao/BrainUMA .","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147845254","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

Combining generative large language models and pretrained language models for predicting acute respiratory distress syndrome progression. 结合生成式大语言模型和预训练语言模型预测急性呼吸窘迫综合征进展。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-05-05 DOI: 10.1007/s11517-026-03588-y

Ming Fang, Yiheng Dai, Yutao Zha, Min Shao, Yu Wang

{"title":"Combining generative large language models and pretrained language models for predicting acute respiratory distress syndrome progression.","authors":"Ming Fang, Yiheng Dai, Yutao Zha, Min Shao, Yu Wang","doi":"10.1007/s11517-026-03588-y","DOIUrl":"https://doi.org/10.1007/s11517-026-03588-y","url":null,"abstract":"Acute Respiratory Distress Syndrome (ARDS) is a life-threatening condition in which early diagnosis and timely intervention are crucial. Recent research into machine learning for predicting ARDS progression based on objective clinical data has been hindered by the limited availability of high-quality annotated datasets. Pretrained Language Models (PLMs) have shown promising results in various Natural Language Processing (NLP) tasks, especially in unstructured electronic health records, but their application to numerical clinical data has been constrained by data type limitations. This study investigates the feasibility of combining generative Large Language Models (LLMs) with PLMs for predicting ARDS progression. The proposed method leverages LLMs to convert numerical clinical indicators into clinical narratives, followed by classification using PLMs to predict ARDS progression. The proposed method was evaluated using two prominent public ICU databases (eICU and MIMIC-IV) and a real-world dataset from the First Affiliated Hospital of Anhui Medical University. Validation results indicate that our method surpasses traditional approaches, yielding F1-scores of 0.8128, 0.9333, and 0.8462, respectively. These results show that generative LLMs and PLMs can improve early ARDS prediction and clinical decisions.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147845312","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

Musculoskeletal modeling based on deep-nsNMF for multi-DoF motion decoding. 基于深度nsnmf的多自由度运动解码肌肉骨骼建模。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-05-05 DOI: 10.1007/s11517-026-03585-1

Jianmin Li, Yue Yang, Qiyang Li, Jianchang Zhao, Jinhua Li, Lizhi Pan

{"title":"Musculoskeletal modeling based on deep-nsNMF for multi-DoF motion decoding.","authors":"Jianmin Li, Yue Yang, Qiyang Li, Jianchang Zhao, Jinhua Li, Lizhi Pan","doi":"10.1007/s11517-026-03585-1","DOIUrl":"https://doi.org/10.1007/s11517-026-03585-1","url":null,"abstract":"Surface electromyography (sEMG) signals, as physiological signals generated during muscle contraction, are widely employed in human-machine interfaces for decoding continuous motion intention. Based on the muscle synergy theory, musculoskeletal models integrated with non-negative matrix factorization provide a physiologically interpretable framework (such as M-NMF-MM) for such decoding tasks. However, the framework exhibits insufficient accuracy for simultaneous movements of multiple degrees of freedom (DoFs) due to the crosstalk between signals of multiple muscles. To address this limitation, the present study proposes an improved deep non-smooth non-negative matrix factorization musculoskeletal model (Deep-nsNMF-MM) by replacing the standard non-negative matrix factorization (NMF) in the M-NMF-MM with a deep non-smooth NMF (Deep-nsNMF) algorithm, which enhances the sparsity of muscle co-excitation and reduces signal redundancy. Compared with sparse NMF and non-smooth NMF, Deep nsNMF yields a higher variance accounted for (VAF), especially with a greater number of network layers. For four DoF synchronous motions, the Deep nsNMF MM significantly outperforms the original M-NMF-MM: the Pearson's correlation coefficient for metacarpophalangeal flexion/extension (MFLX/EXT) and wrist adduction/abduction (WADD/ABD) increases from 0.75 and 0.74 to 0.80 and 0.79, respectively, while the overall normalized root mean square error decreases by 10-15%. This work provides a novel, high precision, and physiologically interpretable decoding method for synchronous proportional control in multiple DoFs human-machine interfaces.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147845251","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

An interpretable survival benefit analytics framework for optimizing cancer treatment decision-making. 优化癌症治疗决策的可解释生存效益分析框架。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-05-02 DOI: 10.1007/s11517-026-03586-0

Shuchao Chen, Haojiang Li, Hui Mao, Tianhe Wu, Chao Luo, Zhiying Liang, Shanshan Liu, Yifei Liu, Haoyang Zhou, Lizhi Liu, Hongbo Chen

{"title":"An interpretable survival benefit analytics framework for optimizing cancer treatment decision-making.","authors":"Shuchao Chen, Haojiang Li, Hui Mao, Tianhe Wu, Chao Luo, Zhiying Liang, Shanshan Liu, Yifei Liu, Haoyang Zhou, Lizhi Liu, Hongbo Chen","doi":"10.1007/s11517-026-03586-0","DOIUrl":"https://doi.org/10.1007/s11517-026-03586-0","url":null,"abstract":"Optimal treatment decision-making is critical to the long-term survival rate of patients in clinical management of cancer. Traditional treatment decision-making models typically reach treatment decisions indirectly by estimating the various risks to the beneficial outcome of patients, which limits the interpretability of treatment effects and the performance of the models. We propose SurvS(survival supervision), a novel interpretable survival benefit analytics framework based on the real-coded genetic algorithm, to integrate individual treatment effects that directly impact long-term survival benefits. SurvS can not only construct binary (beneficial and nonbeneficial), but also construct ternary (beneficial, insensitive, and detrimental) decision-making models, allowing the identification of all three possible outcomes in a single model. The framework generates personalized model scores by integrating weighted clinical features and real-valued cutoff thresholds, optimized to reflect survival outcome differences. The robust performances of SurvS were demonstrated in decision-making tasks of two clinical cancer treatment scenarios: induction chemotherapy for nasopharyngeal carcinoma and adjuvant chemotherapy for rectal cancer. SurvS outperformed traditional methods and maintained robust performance in the independent validation cohort. By enabling survival benefit-supervised optimization and interpretable model construction, reported SurvS offers a powerful tool for personalized cancer treatment planning with the potential to improve treatment efficacy and reduce overtreatment. The code is open source and available at: https://github.com/odindis/SurvS .","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147822774","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

ADBrainNet: a deep neural network for Autism Spectrum Disorder (ASD) and Attention Deficit and Hyperactivity Disorder (ADHD) classification using resting-state fMRI images based on explainable artificial intelligence. ADBrainNet：一个深度神经网络，用于自闭症谱系障碍（ASD）和注意缺陷和多动障碍（ADHD）分类，基于可解释的人工智能，使用静息状态fMRI图像。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-05-01 Epub Date: 2026-03-05 DOI: 10.1007/s11517-026-03530-2

Xinyao Yi, Jian Huang, Babatunde Akinwunmi, Wai-Kit Ming

{"title":"ADBrainNet: a deep neural network for Autism Spectrum Disorder (ASD) and Attention Deficit and Hyperactivity Disorder (ADHD) classification using resting-state fMRI images based on explainable artificial intelligence.","authors":"Xinyao Yi, Jian Huang, Babatunde Akinwunmi, Wai-Kit Ming","doi":"10.1007/s11517-026-03530-2","DOIUrl":"10.1007/s11517-026-03530-2","url":null,"abstract":"Autism Spectrum Disorder (ASD) and Attention Deficit and Hyperactivity Disorder (ADHD) are two psychiatric disorders frequently encountered in children. ADHD is further categorized into three subtypes. The diagnostic processes for these conditions are complex and often prone to misclassification. We proposed a lightweight deep neural network, ADBrainNet, to differentiate ASD, ADHD combined, ADHD hyperactive/impulsive, ADHD inattentive and neurotypical individuals. Our methodology was benchmarked against prevalent ImageNet transfer learning methods, including AlexNet, MobileNet, ResNet18, and Xception, for training on resting-state fMRI images sourced from ABIDE and ADHD-200 datasets. ADBrainNet achieved superior performance on the independent external testing set through five-fold cross-validation, with a mean (± standard deviation) accuracy, precision, recall, and F1 score of 61.87% (± 5.59%), 65.72% (± 6.98%), 61.87% (± 5.59%), and 62.50% (± 5.78%), respectively. Furthermore, the explainable artificial intelligence algorithm LIME was employed to explore the most significant features during ADBrainNet's decision process. Our model provides an interpretable computational framework for neuroimaging-based classification between ASD and ADHD subtypes. This approach may inform future research and, upon further validation and comparison with clinician performance, could potentially aid in patient assessment, stratification, and management of psychiatric disorders.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1609-1622"},"PeriodicalIF":2.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147357338","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

Biomechanical assessment of vertebral fracture risk incorporating the effect of paraspinal muscles. 结合棘旁肌作用的椎体骨折风险的生物力学评估。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-05-01 Epub Date: 2026-03-10 DOI: 10.1007/s11517-026-03557-5

Yuhan Zhao, Rahman Ud Din, Jinping Dong, Xiaoguang Cheng, Haisheng Yang

引用次数: 0

Accurate segmentation of pulmonary arteries and veins via a human-in-the-loop framework with application in COPD. 基于人在环框架的肺动脉静脉精确分割及其在COPD中的应用。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-05-01 Epub Date: 2026-03-23 DOI: 10.1007/s11517-026-03556-6

Patrice Monkam, Xin Du, Yanan Wu, Tiande Zhang, Jiaxuan Xu, Jie Feng, Rongchang Chen, Zhenyu Liang, Shouliang Qi

引用次数: 0

Uncertainty quantification of conduction velocity in models of cardiac spread of activation. 心脏活化扩散模型中传导速度的不确定量化。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-05-01 Epub Date: 2026-03-21 DOI: 10.1007/s11517-026-03560-w

Anna Busatto, Lindsay C R Tanner, Jake A Bergquist, Gernot Plank, Karli Gillette, Akil Narayan, Rob S MacLeod

引用次数: 0

Does FNS outperform CCS in Pauwel's type III fracture fixation: a combined experimental and numerical study. 在Pauwel III型骨折固定中，FNS是否优于CCS：一项实验和数值联合研究。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-05-01 Epub Date: 2026-03-30 DOI: 10.1007/s11517-026-03564-6

Laureb Rao, Samarth Mittal, Apoorva Kabra, Vivek Trikha, Anoop Chawla, Kaushik Mukherjee

引用次数: 0

Assessment of the severity of lower extremity peripheral arterial disease based on gastrocnemius surface electromyography and mechanomyography signals. 基于腓肠肌表面肌电图和肌力图信号的下肢外周动脉疾病严重程度评估。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-05-01 Epub Date: 2026-03-31 DOI: 10.1007/s11517-026-03566-4

Wenxuan Sun, Yanbin Guo, Shuwei Lin, Guoping Wang, Youpeng Zhu, Yao Zhang, Ye Du, Zibo Feng

引用次数: 0