Computer methods and programs in biomedicine最新文献

{"title":"Fusion of multi-scale feature extraction and adaptive multi-channel graph neural network for 12-lead ECG classification","authors":"Teng Chen ,&nbsp;Yumei Ma ,&nbsp;Zhenkuan Pan ,&nbsp;Weining Wang ,&nbsp;Jinpeng Yu","doi":"10.1016/j.cmpb.2025.108725","DOIUrl":"10.1016/j.cmpb.2025.108725","url":null,"abstract":"<div><h3>Background and objective:</h3><div>The 12-lead electrocardiography (ECG) is a widely used diagnostic method in clinical practice for cardiovascular diseases. The potential correlation between interlead signals is an important reference for clinical diagnosis but is often overlooked by most deep learning methods. Although graph neural networks can capture the associations between leads through edge topology, the complex correlations inherent in 12-lead ECG may involve edge topology, node features, or their combination.</div></div><div><h3>Methods:</h3><div>In this study, we propose a multi-scale adaptive graph fusion network (MSAGFN) model, which fuses multi-scale feature extraction and adaptive multi-channel graph neural network (AMGNN) for 12-lead ECG classification. The proposed MSAGFN model first extracts multi-scale features individually from 12 leads and then utilizes these features as nodes to construct feature graphs and topology graphs. To efficiently capture the most correlated information from the feature graphs and topology graphs, AMGNN iteratively performs a series of graph operations to learn the final graph-level representations for prediction. Moreover, we incorporate consistency and disparity constraints into our model to further refine the learned features.</div></div><div><h3>Results:</h3><div>Our model was validated on the PTB-XL dataset, achieving an area under the receiver operating characteristic curve score of 0.937, mean accuracy of 0.894, and maximum F1 score of 0.815. These results surpass the corresponding metrics of state-of-the-art methods. Additionally, we conducted ablation studies to further demonstrate the effectiveness of our model.</div></div><div><h3>Conclusions:</h3><div>Our study demonstrates that, in 12-lead ECG classification, by constructing topology graphs based on physiological relationships and feature graphs based on lead feature relationships, and effectively integrating them, we can fully explore and utilize the complementary characteristics of the two graph structures. By combining these structures, we construct a comprehensive data view, significantly enhancing the feature representation and classification accuracy.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"265 ","pages":"Article 108725"},"PeriodicalIF":4.9,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Physics-Integrated Deep Learning Approach for Patient-Specific Non-Newtonian Blood Viscosity Assessment using PPG","authors":"Hyeong Jun Lee ,&nbsp;Young Woo Kim ,&nbsp;Seung Yong Shin ,&nbsp;San Lee Lee ,&nbsp;Chae Hyeon Kim ,&nbsp;Kyung Soo Chung ,&nbsp;Joon Sang Lee","doi":"10.1016/j.cmpb.2025.108740","DOIUrl":"10.1016/j.cmpb.2025.108740","url":null,"abstract":"<div><h3>Background and Objective</h3><div>The aim of this study is to extract a patient-specific viscosity equation from photoplethysmography (PPG) data. An aging society has increased the need for remote, non-invasive health monitoring systems. However, the circulatory system remains beyond the scope of wearable devices. The solution might be found in the possibility of measuring blood viscosity from wearable devices. Blood viscosity information can be used to monitor and diagnose various circulatory system diseases. Therefore, if blood viscosity can be calculated from wearable photoplethysmography, the versatility of a non-invasive health monitoring system can be broadened.</div></div><div><h3>Methods</h3><div>A hybrid 1D CNN-LSTM architecture incorporating physics-informed constraints was developed to integrate rheological principles into data-driven PPG analysis. The shear-viscosity equation derived from the viscometer was used as ground-truth data. The signal obtained from the wearable devices was processed with noise filtering and wandering elimination to gain stable blood pressure waves. The neural network was trained using k-fold cross-validation and weight factor optimization, with the loss function incorporating rheological constraints from the Carreau–Yasuda model.</div></div><div><h3>Results</h3><div>The final estimation model achieved an accuracy of 81.1 %. The accuracy in the physiological shear range (50–300 s^-1) was 84.0 %, outperforming other low and high shear regions. Mean absolute errors of 0.67 cP in the physiological range align with clinical viscometry tolerances (&lt; 1 cP), demonstrating diagnostic feasibility. Statistical analysis revealed strong linear relationships between predicted and ground truth values across all shear rates (correlation coefficients: 0.619–0.742, <em>p</em> &lt; 0.0001), with mean absolute errors decreasing from 7.84 cP at low shear rates to 0.67 cP in the physiological range. The accuracy and contribution of each parameter to the Carreau–Yasuda model were also analyzed. The results show that the contribution of each parameter varies based on the shear range, providing insight into weight factor optimization.</div></div><div><h3>Conclusion</h3><div>By non-invasively estimating blood viscosity from PPG, the diagnostic capabilities of wearable healthcare systems can be expanded to target various diseases related to the circulatory system. The demonstrated accuracy in physiologically relevant shear ranges supports the potential clinical application of this methodology.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"265 ","pages":"Article 108740"},"PeriodicalIF":4.9,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fluid-structure interaction simulations in patient-specific coronary arteries with aneurysms: Viscoelastic or shear-thinning property of blood","authors":"C. Paz ,&nbsp;E. Suárez ,&nbsp;C. Gil ,&nbsp;S.I.S. Pinto","doi":"10.1016/j.cmpb.2025.108736","DOIUrl":"10.1016/j.cmpb.2025.108736","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Coronary artery aneurysm is a progressive and often asymptomatic condition with a prevalence ranging from 0.3 % to 5 %. This condition can lead to severe and potentially fatal complications. Given the challenges of conducting experiments on real patients, numerical simulations have emerged as a valuable alternative. This study aims to enhance the accuracy of hemodynamic analysis and fluid-structure interaction assessments by incorporating the viscoelastic properties of blood, which are often overlooked, in simulations of the right coronary artery with various aneurysm morphologies.</div></div><div><h3>Methods</h3><div>This research employs numerical simulations to analyse the hemodynamics and assess the one-way coupled fluid-structure interaction within the right coronary artery. The study utilised the simplified Phan-Thien/Tanner model to represent the viscoelastic properties of blood. Different aneurysm morphologies were simulated, and the results were compared with those obtained using the widely recognised Carreau model. The analysis focused on the time-average wall-shear stress, as well as the stress and deformation experienced by the aneurysm wall.</div></div><div><h3>Results</h3><div>The hemodynamic analysis demonstrated that the simplified Phan-Thien/Tanner model produced similar flow patterns to the Carreau model but resulted in a significant reduction of approximately 50 % in the time-average wall-shear stress. This reduction aligns with previous findings. Additionally, the study revealed substantial differences in the stress and deformation of the aneurysm wall, with the simplified Phan-Thien/Tanner model proving more accuracy. The largest deformations were observed in aneurysms with incipient and fusiform shapes, particularly in the divergent section of the proximal region. In the case of saccular aneurysms, the most compromised area was identified not within the aneurysm sac itself, but in the region of the artery just upstream.</div></div><div><h3>Conclusions</h3><div>Incorporating the viscoelastic properties of blood into fluid-structure interaction simulations significantly improves the accuracy of hemodynamic and structural assessments of coronary artery aneurysms. This study underscores the importance of considering these properties when evaluating aneurysm behaviour, which could have important implications for understanding the progression and potential rupture of aneurysms, thereby guiding more effective clinical interventions.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"265 ","pages":"Article 108736"},"PeriodicalIF":4.9,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of the impacts of neuromuscular electrical stimulation based on cortico-muscular-cortical functional network","authors":"Jianpeng Tang ,&nbsp;Xugang Xi ,&nbsp;Ting Wang ,&nbsp;Lihua Li ,&nbsp;Jian Yang","doi":"10.1016/j.cmpb.2025.108735","DOIUrl":"10.1016/j.cmpb.2025.108735","url":null,"abstract":"<div><h3>Background and objective</h3><div>Neuromuscular electrical stimulation (NMES) has been extensively applied for recovery of motor functions. However, its impact on the cortical network changes related to muscle activity remains unclear, which is crucial for understanding the changes in the collaborative working patterns within the sensory-motor control system post-stroke.</div></div><div><h3>Methods</h3><div>In this research, we have integrated cortico-muscular interactions, intercortical interactions, and intramuscular interactions to propose a novel closed-loop network structure, namely the cortico-muscular-cortical functional network (CMCFN). The framework is endowed with the capability to distinguish the directionality of causal interactions and local frequency band characteristics through transfer spectral entropy (TSE). Subsequently, the CMCFN is applied to stroke patients to elucidate the potential influence of NMES on cortical physiological function changes during motor induction.</div></div><div><h3>Results</h3><div>The results indicate that short-term modulation by NMES significantly enhanced the cortico-muscular interactions of the contralateral cerebral hemisphere and the affected upper limb (p &lt; 0.001), while coexistence of facilitatory and inhibitory effects is observed in the intermuscular coupling across different electromyography (EMG) signals. Furthermore, following NMES treatment, the connectivity of the brain functional network is significantly strengthened, particularly in the γ frequency band (30–45 Hz), with marked improvements in the clustering coefficient and shortest path length (p &lt; 0.001).</div></div><div><h3>Conclusions</h3><div>As a new framework, CMCFN offers a novel perspective for studying motor cortical networks related to muscle activity.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"265 ","pages":"Article 108735"},"PeriodicalIF":4.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance analysis of 2D and 3D image features for computer-assisted speech diagnosis of dental sibilants in Polish children","authors":"Agata Sage","doi":"10.1016/j.cmpb.2025.108716","DOIUrl":"10.1016/j.cmpb.2025.108716","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Sigmatism is a speech disorder concerning sibilants, and its diagnosis affects many Polish children of preschool age. The success of therapy often depends on early and accurate diagnosis. This paper presents research findings on using 2D and 3D (time-related) visual features to analyze the place of articulation, sibilance (the character of a gap between teeth that allows the articulation of sibilant sounds), and tongue positioning in four of twelve Polish sibilants:/s/,/z/,/ʦ/, and/dz/.</div></div><div><h3>Methods:</h3><div>A dedicated data acquisition system captured the stereovision stream during the speech therapy examination (201 speakers aged 4-8). The material contains 23 words and four logatomes. This study introduces 3D texture and shape features extracted for the mouth, lips, and tongue. The third dimension is the time of articulation, and the volumes reflect the movements of speech organs. The research compares the usability of 3D mode to a 2D approach (mouth texture features; mouth, lips, and tongue shape parameters) described in previous works. The statistical analysis includes Mann-Whitney U test to indicate the significant differences between selected articulation patterns for each sibilant and pronunciation aspect (considering p<span><math><mo>&lt;</mo></math></span>0.05).</div></div><div><h3>Results:</h3><div>Overall outcomes suggest the dominance of 3D time-related statistically significant features, especially describing the shape of a tongue. Analysis considering features with at least medium effect size showed that 3D features differentiate dental and interdental articulation in case of/s/,/z/, and/ʦ/, while in case of/dz/ significant parameters were 2D. The 3D mode prevails also in terms of sibilance: analysis of sounds/z/ and/ʦ/ results in 3D features only, but for/s/ and/dz/ outcomes include both 3D and 2D parameters. Analysis of the tongue positioning during articulation in terms of at least moderate effect size suggests a presence of features only in the case of affricates:/ʦ/ (3D features) and/dz/ (2D features). All parameters with at least medium effect size describe the shape of the tongue.</div></div><div><h3>Conclusions:</h3><div>This research proves the potential of visual data in building computer-aided speech diagnosis systems using non-contact recording tools. It highlights the usability of a 3D approach introduced in this paper. Results also emphasize the importance of tongue movement analysis.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"264 ","pages":"Article 108716"},"PeriodicalIF":4.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A first explainable-AI-based workflow integrating forward-forward and backpropagation-trained networks of label-free multiphoton microscopy images to assess human biopsies of rare neuromuscular disease","authors":"Riccardo Scodellaro ,&nbsp;Jana Zschüntzsch ,&nbsp;Anna-Kathrin Hell ,&nbsp;Frauke Alves","doi":"10.1016/j.cmpb.2025.108733","DOIUrl":"10.1016/j.cmpb.2025.108733","url":null,"abstract":"<div><h3>Background and objective</h3><div>Diagnosis of rare neuromuscular diseases often relies on muscle biopsy analysis, which varies based on the evaluator's experience. Advances in deep learning show promise in improving diagnostic accuracy by identifying standardized features and phenotypic expressions in biopsy images. Explainable artificial intelligence extracts these features from the neural network's “black box,” ensuring compliance with European ethical standards for the use of clinical data in real-world applications. This study proposes a clinic-friendly workflow, based on explainable artificial intelligence. It combines forward-forward and backpropagation-trained convolutional networks to identify complementary features of Duchenne Muscular Dystrophy. Our proposal sets the forward-forward training, applied here for the first time on biomedical images, as a potential new standard for interpretable deep learning applications in clinics.</div></div><div><h3>Methods</h3><div>We analyzed a multiphoton microscopy dataset of 1600 images from 16 human muscle biopsies obtained during elective spinal surgery, combining autofluorescence, second and third harmonic generation signals. Class Activation Maps unveiled the dual decision-making process of the convolutional network, independently trained with both standard backpropagation and forward-forward algorithms. We evaluated the significance of the discovered features by using the Mann Whitney method. Entire biopsies were analyzed by providing an attention metric, computed as the weighted mean of all significant parameters.</div></div><div><h3>Results</h3><div>Backpropagation, gold standard for 35 years, and forward-forward achieved over 90 % accuracy in distinguishing healthy and diseased patients tissue. Class activation maps revealed that, when trained independently with both algorithms, the same network identifies Duchenne Muscular Dystrophy tissue by focusing on different features. Both methods identified intramuscular collagen as a key feature. Backpropagation also highlighted collagen waviness and fatty tissue. Forward-forward emphasized collagen density. We integrated these complementary insights, validated by significance analysis, into a standardized attention metric, allowing a multi-structural, quantitative assessment of tissue changes and highlighting areas for further clinical analysis.</div></div><div><h3>Conclusions</h3><div>Our workflow, using clinically routine biopsies and transparent diagnostic features, demonstrates the unique potential of forward-forward in providing novel, reliable, interpretable results from biomedical images. This paves the way for its dual use with backpropagation, shifting benchmarks by enabling the discovery of features potentially overlooked by backpropagation across biomedical datasets.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"265 ","pages":"Article 108733"},"PeriodicalIF":4.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"S-Net: A novel shallow network for enhanced detail retention in medical image segmentation","authors":"Qinghua Shang ,&nbsp;Guanglei Wang ,&nbsp;Xihao Wang ,&nbsp;Yan Li ,&nbsp;Hongrui Wang","doi":"10.1016/j.cmpb.2025.108730","DOIUrl":"10.1016/j.cmpb.2025.108730","url":null,"abstract":"<div><h3>Background and Objective</h3><div>In recent years, deep U-shaped network architectures have been widely applied to medical image segmentation tasks, achieving notable successes. However, the inherent limitation of this architecture is that multiple down-sampling lead to significant loss of input image detail information. A series of improvements in skip connections designed to enhance information transfer have not fundamentally resolved the issue. Therefore, we consider retaining information in a simpler and more effective way.</div></div><div><h3>Methods</h3><div>In this paper, we propose a novel shallow network, S-Net, which contains only two output resolution stages, allowing for the preservation of more detailed information from the input images. To address the challenge of shallow networks primarily relying on high-resolution feature maps as the main information flow, we propose a Global-Local Feature Fusion (GLFF) module at the network bottleneck layer. This module integrates the superior global contextual information extraction capabilities of Mamba with the local feature capturing abilities of multi-scale depthwise convolutions, enabling the extraction of crucial semantic features from high-resolution feature maps within a shallow network architecture, while maintaining a smaller model size.</div></div><div><h3>Results</h3><div>Extensive experiments on four different types of medical image datasets show that S-Net achieves the best segmentation performance compared to existing models, with more refined segmentation details. For example, on ultrasound datasets (BUSI), the IOU is 2.95% higher and DICE is 2.27% higher than the second-best model. Additionally, S-Net has only 1.52M parameters, making it competitive in terms of lightweight design.</div></div><div><h3>Conclusions</h3><div>Comparative and ablation experiments demonstrate the efficiency of the proposed architecture and modules. It shows that we do not need many down-sampling operations to reduce the size of feature maps significantly. This work provides new research ideas for further improving the accuracy of medical image segmentation and expands the research direction for model lightweight design. The code will be available at: <span><span>https://github.com/qinghua0715/S-Net</span><svg><path></path></svg></span></div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"265 ","pages":"Article 108730"},"PeriodicalIF":4.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing stability of heart disease prediction across imbalanced learning with interpretable Grow Network","authors":"Simon Bin Akter ,&nbsp;Sumya Akter ,&nbsp;Rakibul Hasan ,&nbsp;Md Mahadi Hasan ,&nbsp;David Eisenberg ,&nbsp;Riasat Azim ,&nbsp;Jorge Fresneda Fernandez ,&nbsp;Tanmoy Sarkar Pias","doi":"10.1016/j.cmpb.2025.108702","DOIUrl":"10.1016/j.cmpb.2025.108702","url":null,"abstract":"<div><h3>Background and objectives:</h3><div>Heart disease prediction models often face stability challenges when applied to public datasets due to significant class imbalances, unlike the more balanced benchmark datasets. These imbalances can adversely affect various stages of prediction, including feature selection, sampling, and modeling, leading to skewed performance, with one class often being favored over another.</div></div><div><h3>Methods:</h3><div>To enhance stability, this study proposes a Grow Network (GrowNet) architecture, which dynamically configures itself based on the data’s characteristics. To enhance GrowNet’s stability, this study proposes the use of TriDyn Dependence feature selection and Adaptive Refinement sampling, which ensure the selection of relevant features across imbalanced data and effectively manage class imbalance during training.</div></div><div><h3>Results:</h3><div>When evaluated on the benchmark UCI heart disease dataset, GrowNet has outperformed other models, achieving a specificity of 92%, sensitivity of 88%, precision of 90%, and F1 score of 90%. Further evaluation on three public datasets from the Behavioral Risk Factor Surveillance System (BRFSS), where heart disease cases constitute only about 6% of the data, has demonstrated GrowNet’s ability to maintain balanced performance, with an average specificity, sensitivity, and AUC-ROC of 77.67%, 81.67%, and 89.67%, respectively, while other models have exhibited instability. This represents a 22.8% improvement in handling class imbalance compared to prior studies. Additional tests on two public datasets from the National Health Interview Survey (NHIS) have confirmed GrowNet’s robustness and generalizability, with an average specificity, sensitivity, and AUC-ROC of 80.5%, 82.5%, and 90%, respectively, while other models have continued to demonstrate instability.</div></div><div><h3>Discussion:</h3><div>To enhance transparency, this study incorporates SHapley Additive exPlanations (SHAP) analysis, enabling healthcare professionals to understand the decision-making process and identify key risk factors for heart disease, such as bronchitis in midlife, renal dysfunction in the elderly, and depressive disorders in individuals aged 35-44.</div></div><div><h3>Conclusion:</h3><div>This study presents a robust, interpretable model to assist healthcare professionals in cost-effective, early heart disease detection by focusing on key risk factors, ultimately improving patient outcomes.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"265 ","pages":"Article 108702"},"PeriodicalIF":4.9,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring emotional climate recognition in peer conversations through bispectral features and affect dynamics","authors":"Ghada Alhussein ,&nbsp;Mohanad Alkhodari ,&nbsp;Ioannis Ziogas ,&nbsp;Charalampos Lamprou ,&nbsp;Ahsan H. Khandoker ,&nbsp;Leontios J. Hadjileontiadis","doi":"10.1016/j.cmpb.2025.108695","DOIUrl":"10.1016/j.cmpb.2025.108695","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Emotion recognition in conversations using artificial intelligence (AI) has gained significant attention due to its potential to provide insights into human social behavior. This study extends AI-based emotion recognition to the recognition of emotional climate (EC), which reflects the joint emotional atmosphere dynamically created and perceived by peers during conversations. The objective is to propose and evaluate a novel approach, MLBispec, for EC recognition using speech signals.</div></div><div><h3>Methods:</h3><div>The MLBispec approach involves time-windowed bispectral analysis of conversational speech signals to extract features related to nonlinear harmonic interactions. These features are combined with peers’ affect dynamics, derived from emotion labeling for the same time windows, to form an extended feature set. The combined feature set is then fed into machine learning (ML) classifiers. MLBispec was evaluated on the IEMOCAP, K-EmoCon, and SEWA open-access datasets, which provide 2D emotion annotations (arousal and valence) divided into low/high classes. Additionally, cross-lingual experiments were conducted to test the framework’s generalization across languages.</div></div><div><h3>Results:</h3><div>Experimental results demonstrated that MLBispec outperformed previous deep learning-based state-of-the-art approaches in speech emotion recognition, achieving accuracies of 82.6% for arousal and 75.4% for valence. The framework’s incorporation of both qualitative and quantitative EC measurements enhanced its ability to characterize the dynamic speech representations of conversational affective structures. Cross-lingual experiments further validated the robustness of MLBispec.</div></div><div><h3>Conclusions:</h3><div>The findings highlight the effectiveness of MLBispec in objectively recognizing peers’ EC during conversations, setting a new standard for practical emotionally-aware applications. These include point-of-care healthcare, human–computer interfaces (HCI), and large-language models (LLMs). By enabling dynamic and reliable EC recognition, MLBispec paves the way for advancements in emotionally intelligent systems.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"265 ","pages":"Article 108695"},"PeriodicalIF":4.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143695950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SP-XTIN: A single projection grating-based X-ray tri-contrast imaging network","authors":"Linhai Xu ,&nbsp;Changsheng Zhang ,&nbsp;Yu Liu ,&nbsp;Gang Zhao ,&nbsp;Shengping Yuan ,&nbsp;Wei Guan ,&nbsp;Jian Fu","doi":"10.1016/j.cmpb.2025.108718","DOIUrl":"10.1016/j.cmpb.2025.108718","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Grating-based X-ray imaging (GBXI) enables the acquisition of tri-contrast signals—absorption, phase, and dark- field—making it highly promising for applications in clinical diagnostics. However, traditional GBXI requires phase stepping of gratings, leading to high radiation doses. In this study, a single projection grating-based X-ray tri-contrast imaging network (SP-XTIN) is proposed.</div></div><div><h3>Methods:</h3><div>A Pix2pixHD-based architecture is adopted, and a multi-task learning strategy is employed to transform the generator into a multi-output model that can simultaneously generate tri-contrast images. Additionally, an edge loss term is integrated into the loss function to enhance edge preservation in the tri-contrast images.</div></div><div><h3>Results:</h3><div>The proposed SP-XTIN is validated on two experimental datasets: one acquired with synchrotron radiation (SR) and another using a laboratory X-ray tube source. For the SR dataset, the feature similarity index measure (FSIM) values for absorption, phase, and dark-field signals achieved were 0.9871, 0.9863, and 0.9786, respectively. Using the laboratory X-ray tube source dataset, the FSIM values were 0.9883, 0.9670, and 0.9631.</div></div><div><h3>Conclusion:</h3><div>The proposed SP-XTIN is effective in advancing GBXI technology. These results highlight its effectiveness and are expected to contribute to the further development of this field.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"264 ","pages":"Article 108718"},"PeriodicalIF":4.9,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}