Computer methods and programs in biomedicine最新文献

{"title":"A computationally efficient FEM platform for comprehensive simulations of photoacoustic imaging","authors":"Reza Rahpeima,&nbsp;Chieh-Hsun Wen,&nbsp;Pai-Chi Li","doi":"10.1016/j.cmpb.2025.108620","DOIUrl":"10.1016/j.cmpb.2025.108620","url":null,"abstract":"<div><h3>Background and Objective</h3><div>This study introduces a comprehensive finite element method (FEM) platform to overcome limitations in photoacoustic imaging (PAI) simulations, addressing challenges associated with the simplified numerical methods and rudimentary geometries of existing simulators. The objective is to develop a physics-based numerical simulation method that comprehensively models the entire PAI process, encompassing the various physics processes involved from the initial laser irradiation to the final image reconstruction stage, and producing results that closely replicate real-world scenarios.</div></div><div><h3>Methods</h3><div>The proposed comprehensive simulation platform models the physics of ray optics, bioheat transfer, solid mechanics, elastic waves, and pressure acoustics, encompassing all the various physical processes involved in PAI. This platform employs time-explicit numerical methods, making it computationally efficient and attractive for preclinical analyses. The method was validated by comparing the results of FEM simulations with those from k-wave simulations and experimental tests. The simulations focus on an anatomically realistic breast phantom to demonstrate the induced effects of laser irradiation.</div></div><div><h3>Results</h3><div>The FEM simulation results revealed that laser irradiation caused a slight temperature increase of approximately 0.6 °C in the tumor area. This temperature increase led to the generation of a maximum pressure stress of 853,000 N m^–2 due to thermoelastic expansion, resulting in the production of acoustic waves with a maximum acoustic pressure of 446 kPa after 2 μs of propagation. These acoustic waves propagate, and are detected by a transducer for subsequent image reconstruction. The reported findings highlight the platform's high precision in simulating PAI, including all of its intermediate steps.</div></div><div><h3>Conclusions</h3><div>The developed FEM platform is versatile across diverse scenarios, making it a powerful tool for various applications such as PAI simulations of different body parts, evaluation of various beamforming methods, and consideration of different transducer types. The applications of the platform include temperature monitoring during hyperthermia therapy. This simulation method also has significant potential for training machine-learning and deep-learning models.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108620"},"PeriodicalIF":4.9,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143094957","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":"TSPE: Reconstruction of multi-morphological tumors of NIR-II fluorescence molecular tomography based on positional encoding","authors":"Keyi Han ,&nbsp;Chunzhao Li ,&nbsp;Anqi Xiao ,&nbsp;Yaqi Tian ,&nbsp;Jie Tian ,&nbsp;Zhenhua Hu","doi":"10.1016/j.cmpb.2024.108554","DOIUrl":"10.1016/j.cmpb.2024.108554","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Fluorescence molecular tomography (FMT) is a noninvasive and highly sensitive imaging modality, which can display 3D visualization of tumors by reconstructing fluorescence probes’ distribution. However, existing methods mostly ignore positional information, which includes spatial structure information crucial for the reconstruction of light sources. This limits the reconstruction accuracy of light sources with multiple morphologies. Therefore, to our best knowledge, we for the first time integrated positional encoding into the FMT task, enabling the incorporation of high-frequency spatial structure information.</div></div><div><h3>Methods</h3><div>We proposed a three-stage network embedded with a positional encoding module (TSPE) to perform high reconstruction accuracy of tumors with multiple morphologies. Additionally, our study focused on NIR-II which had less severe scattering problems and higher imaging accuracy than NIR-I.</div></div><div><h3>Results</h3><div>The simulation experiments demonstrated that TSPE achieved high reconstruction accuracy in NIR-II FMT, with the barycenter error (BCE) for single-tumor reconstruction reaching 0.18 mm, representing a 14 % reduction compared to other methods. TSPE more accurately distinguished adjacent multi-morphological tumors with a minimal edge-to-edge distance (EED) of 0.3 mm. In vivo experiments also showed that TSPE could achieve more accurate reconstruction of tumors compared with other methods.</div></div><div><h3>Conclusions</h3><div>The proposed method can achieve the best reconstruction performance. It has potential to promote the development of NIR-II FMT and its preclinical application.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108554"},"PeriodicalIF":4.9,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143074260","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 multi-layer EEG fusion decoding method with channel selection for multi-brain motor imagery","authors":"Li Zhu,&nbsp;Yankai Xin,&nbsp;Yu Yang,&nbsp;Wanzeng Kong","doi":"10.1016/j.cmpb.2025.108595","DOIUrl":"10.1016/j.cmpb.2025.108595","url":null,"abstract":"<div><div>Traditional motor imagery-based single-brain computer interfaces(BCIs) face inherent limitations, such as unstable signals and low recognition accuracy. In contrast, multi-brain BCIs offer a promising solution by leveraging group electroencephalography (EEG) data. This paper presents a novel multi-layer EEG fusion method with channel selection for motor imagery-based multi-brain BCIs. We utilize mutual information convergent cross-mapping (MCCM) to identify channels that the represent causal relationships between brains; this strategy is combined with multiple linear discriminant analysis (MLDA) for decoding intentions via both data-layer and decision-layer strategies. Our experimental results demonstrate that the proposed method improves the accuracy of multi-brain motor imagery decoding by approximately 10% over that of the traditional methods, with a further 3%–5% accuracy increase due to the effective channel selection mechanism.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"262 ","pages":"Article 108595"},"PeriodicalIF":4.9,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395212","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":"Towards practical and privacy-preserving CNN inference service for cloud-based medical imaging analysis: A homomorphic encryption-based approach","authors":"Yanan Bai ,&nbsp;Hongbo Zhao ,&nbsp;Xiaoyu Shi ,&nbsp;Lin Chen","doi":"10.1016/j.cmpb.2025.108599","DOIUrl":"10.1016/j.cmpb.2025.108599","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Cloud-based Deep Learning as a Service (DLaaS) has transformed biomedicine by enabling healthcare systems to harness the power of deep learning for biomedical data analysis. However, privacy concerns emerge when sensitive user data must be transmitted to untrusted cloud servers. Existing privacy-preserving solutions are hindered by significant latency issues, stemming from the computational complexity of inner product operations in convolutional layers and the high communication costs of evaluating nonlinear activation functions. These limitations make current solutions impractical for real-world applications.</div></div><div><h3>Methods:</h3><div>In this paper, we address the challenges in mobile cloud-based medical imaging analysis, where users aim to classify private body-related radiological images using a Convolutional Neural Network (CNN) model hosted on a cloud server while ensuring data privacy for both parties. We propose PPCNN, a practical and privacy-preserving framework for CNN Inference. It introduces a novel mixed protocol that combines a low-expansion homomorphic encryption scheme with the noise-based masking method. Our framework is designed based on three key ideas: (1) optimizing computation costs by shifting unnecessary and expensive homomorphic multiplication operations to the offline phase, (2) introducing a coefficient-aware packing method to enable efficient homomorphic operations during the linear layer of the CNN, and (3) employing data masking techniques for nonlinear operations of the CNN to reduce communication costs.</div></div><div><h3>Results:</h3><div>We implemented PPCNN and evaluated its performance on three real-world radiological image datasets. Experimental results show that PPCNN outperforms state-of-the-art methods in mobile cloud scenarios, achieving superior response times and lower usage costs.</div></div><div><h3>Conclusions:</h3><div>This study introduces an efficient and privacy-preserving framework for cloud-based medical imaging analysis, marking a significant step towards practical, secure, and trustworthy AI-driven healthcare solutions.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108599"},"PeriodicalIF":4.9,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143058362","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":"Multiscale feature enhanced gating network for atrial fibrillation detection","authors":"Xidong Wu,&nbsp;Mingke Yan,&nbsp;Renqiao Wang,&nbsp;Liping Xie","doi":"10.1016/j.cmpb.2025.108606","DOIUrl":"10.1016/j.cmpb.2025.108606","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Atrial fibrillation (AF) is a significant cause of life-threatening heart disease due to its potential to lead to stroke and heart failure. Although deep learning-assisted diagnosis of AF based on ECG holds significance in clinical settings, it remains unsatisfactory due to insufficient consideration of noise and redundant features. In this work, we propose a novel multiscale feature-enhanced gating network (MFEG Net) for AF diagnosis.</div></div><div><h3>Method</h3><div>The network integrates multiscale convolution, adaptive feature enhancement (FE), and dynamic temporal processing. The multiscale convolution helps capture global and local information. The FE module consists of a soft-threshold residual shrinkage component, a dilated convolution module, and a Squeeze-and-Excitation (SE) module, eliminating redundant features and emphasizing effective features. The design allows the network to focus on the most relevant AF features, thereby enhancing its robustness and accuracy in the presence of noise and irrelevant information. The dynamic temporal module helps the network learn and recognize the time dependence associated with AF. The novel design endows the model with excellent robustness to cope with random noise in real-world environments.</div></div><div><h3>Result</h3><div>Compared with the state-of-the-art methods, our model exhibits excellent classification performance with an accuracy of 0.930, an F1 score of 0.883, and remarkable resilience to noise interference on the PhysioNet Challenge 2017 dataset. Moreover, the model was trained on the CinC2017 database and validated on the CPSC2018 database and AFDB database, achieving accuracies of 0.908 and 0.938, respectively.</div></div><div><h3>Conclusion</h3><div>The excellent classification performance of MFEG Net, coupled with its robustness in processing noisy electrocardiogram signals, makes it a powerful method for automatic atrial fibrillation detection. This method has made significant progress over state-of-the-art methods and may alleviate the burden of manual diagnosis for clinical doctors.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108606"},"PeriodicalIF":4.9,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143028018","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":"VAE-Surv: A novel approach for genetic-based clustering and prognosis prediction in myelodysplastic syndromes","authors":"Cesare Rollo ,&nbsp;Corrado Pancotti ,&nbsp;Flavio Sartori ,&nbsp;Isabella Caranzano ,&nbsp;Saverio D’Amico ,&nbsp;Luciana Carota ,&nbsp;Francesco Casadei ,&nbsp;Giovanni Birolo ,&nbsp;Luca Lanino ,&nbsp;Elisabetta Sauta ,&nbsp;Gianluca Asti ,&nbsp;Alessandro Buizza ,&nbsp;Mattia Delleani ,&nbsp;Elena Zazzetti ,&nbsp;Marilena Bicchieri ,&nbsp;Giulia Maggioni ,&nbsp;Pierre Fenaux ,&nbsp;Uwe Platzbecker ,&nbsp;Maria Diez-Campelo ,&nbsp;Torsten Haferlach ,&nbsp;Tiziana Sanavia","doi":"10.1016/j.cmpb.2025.108605","DOIUrl":"10.1016/j.cmpb.2025.108605","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>Several computational pipelines for biomedical data have been proposed to stratify patients and to predict their prognosis through survival analysis. However, these analyses are usually performed independently, without integrating the information derived from each of them. Clustering of survival data is an underexplored problem, and current approaches are limited for biomedical applications, whose data are usually heterogeneous and multimodal, with poor scalability for high-dimensionality.</div></div><div><h3>Methods</h3><div>We introduce VAE-Surv, a multimodal computational framework for patients’ stratification and prognosis prediction. VAE-Surv integrates a Variational Autoencoder (VAE), which reduces the high-dimensional space characterizing the molecular data, with a deep survival model, which combines the embedded information with the clinical features. The VAE embedding step prioritizes local coherence within the feature space to detect potential nonlinear relationships among the molecular markers. The latent representation is then exploited to perform K-means clustering. To test the clinical robustness of the algorithm, VAE-Surv was applied to the Genomed4all cohort of Myelodysplastic Syndromes (MDS), comparing the identified subtypes with the World Health Organization (WHO) classification. The survival outcome was compared with the state-of-the-art Cox model and its penalized versions. Finally, to assess the generalizability of the results, the method was also validated on an external MDS cohort.</div></div><div><h3>Results</h3><div>Tested on 2,043 patients in the GenomMed4All cohort, VAE-Surv achieved a median C-index of 0.78, outperforming classical approaches. In addition, the latent space enhanced the clustering performance compared to a traditional approach that applies the clustering directly to the input data. Compared to the WHO 2016 MDS subtypes, the analysis of the identified clusters showed that the proposed framework can capture existing clinical categorizations while also suggesting novel, data-driven patient groups. Even tested in an external MDS cohort of 2,384 patients, VAE-Surv achieved a good prediction performance (median C-index=0.74), preserving the interpretability of the main clinical and genetic features.</div></div><div><h3>Conclusions</h3><div>VAE-Surv enables automatic identification of patients’ clusters, while outperforming the traditional CoxPH model in survival prediction tasks at the same time. Applied to MDS use case, the obtained genetic-based clusters exhibit a clear survival stratification, and the application of the clinical information allowed high performance in prognosis prediction.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108605"},"PeriodicalIF":4.9,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143058374","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":"Elevating performance and interpretability of in silico classifiers for drug proarrhythmia risk evaluations using multi-biomarker approach with ranking algorithm","authors":"Ali Ikhsanul Qauli ,&nbsp;Nurul Qashri Mahardika T ,&nbsp;Ulfa Latifa Hanum ,&nbsp;Frederique Jos Vanheusden ,&nbsp;Ki Moo Lim","doi":"10.1016/j.cmpb.2025.108609","DOIUrl":"10.1016/j.cmpb.2025.108609","url":null,"abstract":"<div><h3>Background and objective</h3><div>Using electrophysiological simulations and machine learning to predict drug proarrhythmia risk has gained popularity due to its effectiveness. The leading <em>in silico</em> drug assessment system mainly uses a single biomarker (qNet) to predict proarrhythmia risk, offering good performance and straightforward interpretation. Other advanced classifiers incorporating additional physiological biomarkers provide better predictive capabilities but are less intuitive. Thus, a method that accommodates multiple biomarkers while maintaining interpretability is needed.</div></div><div><h3>Methods</h3><div>We enhance the current best ordinal logistic regression (OLR) model by adding more physiological biomarkers to overcome its limitations. We also introduce a general torsade metric score (TMS) for multi-biomarker approaches to facilitate easier interpretation. Additionally, a novel ranking algorithm based on a simple multi-criteria decision analysis method is employed to evaluate various classifiers against standard proarrhythmia risk criteria efficiently.</div></div><div><h3>Results</h3><div>Our proposed method demonstrates that using multiple well-known biomarkers yields better performance than using qNet alone. Some accepted multi-biomarker OLR models do not incorporate qNet yet outperform those that do. Moreover, some ill-performing biomarkers when utilized individually can show improved performance in combination with other biomarkers.</div></div><div><h3>Conclusion</h3><div>The proposed approach offers an effective way of utilizing multiple biomarkers, including well-known ones, providing practical alternatives for proarrhythmia risk assessment. The interpretability of the accepted models is straightforward, thanks to the TMS thresholds for multi-biomarker OLR models that allow direct evaluation of the classification prediction of individual drugs.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108609"},"PeriodicalIF":4.9,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143028016","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":"Multi-region nomogram for predicting central lymph node metastasis in papillary thyroid carcinoma using multimodal imaging: A multicenter study","authors":"Shidi Miao ,&nbsp;Qifan Xuan ,&nbsp;Wenjuan Huang ,&nbsp;Yuyang Jiang ,&nbsp;Mengzhuo Sun ,&nbsp;Hongzhuo Qi ,&nbsp;Ao Li ,&nbsp;Zengyao Liu ,&nbsp;Jing Li ,&nbsp;Xuemei Ding ,&nbsp;Ruitao Wang","doi":"10.1016/j.cmpb.2025.108608","DOIUrl":"10.1016/j.cmpb.2025.108608","url":null,"abstract":"<div><h3>Background and objective</h3><div>Central lymph node metastasis (CLNM) is associated with high recurrence rate and low survival in patients with papillary thyroid carcinoma (PTC). However, there is no satisfactory model to predict CLNM in PTC. This study aimed to integrate PTC deep learning feature based on ultrasound (US) images, fat radiomics features based on computed tomography (CT) images and clinical characteristics to construct a multimodal and multi-region nomogram (MMRN) for predicting the CLNM in PTC.</div></div><div><h3>Methods</h3><div>We enrolled 661 patients diagnosed with PTC by thyroidectomy from two independent centers. Patients were divided into the primary cohort, internal test cohort (ITC), and external test cohort (ETC), and collected their US images and CT images. Resnet50 was employed to predict the CLNM status of PTC based on US images. Using radiomics feature extraction methods to extract fat radiomics features from CT images. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) regression. The predictive performance of the MMRN was evaluated using five-fold cross-validation. We comprehensively evaluated the DLRCN and compared it with five radiologists.</div></div><div><h3>Results</h3><div>In the ITC and ETC, the area under the curves (AUCs) of MMRN were 0.829 (95 % CI: 0.822, 0.835) and 0.818 (95 % CI: 0.808, 0.828). The calibration curve revealed good predictive accuracy between the actual probability and predicted probability (<em>P</em> &gt; 0.05). Decision curve analysis showed that the MMRN was clinically useful. Under equal specificity or sensitivity, the performance of MMRN increased by 6.5 % or 2.9 % compared to radiologist assessments. The incorporation of fat radiomics features led to significant net reclassification improvement (NRI) and integrated discrimination improvement (IDI) (NRI=0.174, <em>P</em> &lt; 0.05, IDI=0.035, <em>P</em> &lt; 0.05).</div></div><div><h3>Conclusion</h3><div>The MMRN demonstrated good performance in predicting the CLNM status of PTC, which was comparable to radiologist assessments. The fat radiomics features exhibited supplementary value for predicting CLNM in PTC.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108608"},"PeriodicalIF":4.9,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001424","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":"Severity grading of hypertensive retinopathy using hybrid deep learning architecture","authors":"Supriya Suman ,&nbsp;Anil Kumar Tiwari ,&nbsp;Shreya Sachan ,&nbsp;Kuldeep Singh ,&nbsp;Seema Meena ,&nbsp;Sakshi Kumar","doi":"10.1016/j.cmpb.2025.108585","DOIUrl":"10.1016/j.cmpb.2025.108585","url":null,"abstract":"<div><h3>Background and Objectives:</h3><div>Hypertensive Retinopathy (HR) is a retinal manifestation resulting from persistently elevated blood pressure. Severity grading of HR is essential for patient risk stratification, effective management, progression monitoring, timely intervention, and minimizing the risk of vision impairment. Computer-aided diagnosis and artificial intelligence (AI) systems play vital roles in the diagnosis and grading of HR. Over the years, very limited research has been conducted for the grading of HR. Nevertheless, there are no publicly available datasets for HR grading. Moreover, one of the key challenges observed is high-class imbalance.</div></div><div><h3>Methods:</h3><div>To address these issues, in this paper, we develop “HRSG: Expert-Annotated Hypertensive Retinopathy Severity Grading” dataset, classifying HR severity into four distinct classes: normal, mild, moderate, and severe. Further, to enhance the grading performance on limited datasets, this paper introduces a novel hybrid architecture that combines the strengths of pretrained ResNet-50 via transfer learning, and a modified Vision Transformer (ViT) architecture enhanced with a combination of global self-attention and locality self-attention mechanisms. The locality self-attention addresses the common issue of a lack of inductive bias in ViT architecture. This architecture effectively captures both local and global contextual information, resulting in a robust and resilient classification model. To overcome class imbalance, Decouple Representation and Classifier (DRC) - based training approach is proposed. This method improves the model’s ability to learn effective features while preserving the original dataset’s distribution, leading to better diagnostic accuracy.</div></div><div><h3>Results:</h3><div>Performance evaluation results show the competence of the proposed method in accurately grading the severity of HR. The proposed method achieved an average accuracy of 0.9688, sensitivity of 0.9435, specificity of 0.9766, F1-score of 0.9442, and precision of 0.9474. The comparative results indicate that the proposed method outperforms existing HR methods, state-of-the-art CNN models, and baseline pretrained ViT models. Additionally, we compared our method with a CNNViT model, which combines a shallow CNN architecture with 3 convolution blocks consisting of a convolution layer, a batch normalization layer, a max pooling layer, and lightweight ViT architecture, due to limited datasets. In comparison with the CNNViT, the proposed method achieved superior performance, demonstrating its effectiveness.</div></div><div><h3>Conclusion:</h3><div>The experimental results demonstrate the efficacy of the proposed method in accurately grading HR severity.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108585"},"PeriodicalIF":4.9,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143037476","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":"Analysis of the hemodynamic impact of coronary plaque morphology in mild coronary artery stenosis","authors":"Luyuan Chen ,&nbsp;Haoyao Cao ,&nbsp;Yiming Li ,&nbsp;Mao Chen ,&nbsp;Tinghui Zheng","doi":"10.1016/j.cmpb.2025.108602","DOIUrl":"10.1016/j.cmpb.2025.108602","url":null,"abstract":"<div><h3>Objectives</h3><div>As is well known, plaque morphology plays an important role in the hemodynamics of stenotic coronary arteries, thus their clinic outcomes. However, so far, there has been no research on how the cross-sectional shape of a stenotic lumen affects its hemodynamics. Therefore, this study aims to explore the impact of plaque cross-sectional shape on coronary hemodynamics under mild or moderate stenosis conditions (diameter stenosis degree ≤50 %).</div></div><div><h3>Methods</h3><div>A three-dimensional model of the coronary tree was established using CT images of a subject without coronary stenosis. Based on real CT images of patients, six types of plaque cross-sectional morphologies were created at the same location in one main left coronary artery model, controlling for 50 % and 25 % diameter stenosis, respectively. Computational fluid dynamics (CFD) simulations were performed on the six stenosed coronary models and one normal control model under the same boundary conditions. The differences in hemodynamic results among the models were compared.</div></div><div><h3>Results</h3><div>(1) Type III plaque caused the largest disturbance in the flow field. (2) In type IV plaque, the area with an oscillatory shear index (OSI) &gt;0.1 accounted for 11.18 %. (3) Type V plaque exhibited the most prominent vortex flow lines. (4) Hemodynamic parameters within type VI plaques were most similar to those of normal coronary arteries. (5) Area stenosis better reflects the severity of coronary stenosis.</div></div><div><h3>Conclusion</h3><div>Different cross-sectional morphologies can lead to abnormalities in different hemodynamic parameters, leading to different clinical outcomes. Especially, type III plaques are most likely to cause vascular wall damage, while type V plaques warrant caution due to the risk of complications such as thrombosis. Considering plaque cross-sectional morphology can provide doctors with more information and theoretical support for diagnosis.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108602"},"PeriodicalIF":4.9,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143022594","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}