Computer methods and programs in biomedicine最新文献

{"title":"Optimizing beat-wise input for arrhythmia detection using 1-D convolutional neural networks: A real-world ECG study","authors":"Sunghan Lee ,&nbsp;Guangyao Zheng ,&nbsp;Jeonghwan Koh ,&nbsp;Haoran Li ,&nbsp;Zicheng Xu ,&nbsp;Sung Pil Cho ,&nbsp;Sung Il Im ,&nbsp;Vladimir Braverman ,&nbsp;In cheol Jeong","doi":"10.1016/j.cmpb.2025.108898","DOIUrl":"10.1016/j.cmpb.2025.108898","url":null,"abstract":"<div><h3>Backgrounds and objectives:</h3><div>Cardiac arrhythmias, characterized by irregular heartbeats, are difficult to diagnose in real-world scenarios. Machine learning has advanced arrhythmia detection; however, the optimal number of heartbeats for precise classification remains understudied. This study addresses this using machine learning while assessing the performance of arrhythmia detection across inter-patient and intra-patient conditions. Furthermore, the performance–resource trade-offs are evaluated for practical deployment in mobile health (mHealth) applications.</div></div><div><h3>Methods:</h3><div>Beat-wise segmentation and resampling techniques were utilized for preprocessing electrocardiography (ECG) signals to ensure consistent input lengths. A 1-D convolutional neural network was used to classify the eight multi-labeled arrhythmias. The dataset comprised real-world ECG recordings from the HiCardi wireless device alongside data from the MIT-BIH Arrhythmia database. Model performance was assessed through fivefold cross-validation under both inter-patient and intra-patient conditions.</div></div><div><h3>Results:</h3><div>The proposed model demonstrated peak accuracy at four beats under inter-patient conditions, with minimal improvements beyond this point. This configuration achieved a balance between performance (94.82% accuracy) and resource consumption (training time: 72.27 s per epoch; prediction time: 155 <span><math><mi>μ</mi></math></span>s per segment). Real-world simulations validated the feasibility of real-time arrhythmia detection for approximately 5000 patients.</div></div><div><h3>Conclusion:</h3><div>Utilizing four heartbeats as the input size for arrhythmia classification results in a trade-off between accuracy and computational efficiency. This discovery has significant implications for real-time wearable ECG devices, where both performance and resource constraints are crucial considerations. This insight is expected to serve as a valuable reference for enhancing the design and implementation of arrhythmia detection systems for scalable and efficient mHealth applications.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"269 ","pages":"Article 108898"},"PeriodicalIF":4.9,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338982","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":"Machine learning strategies for multi-label pre-diagnosis of diseases with superficial data","authors":"Dengqun Gou ,&nbsp;Xu Luo ,&nbsp;Zhichen Liu","doi":"10.1016/j.cmpb.2025.108911","DOIUrl":"10.1016/j.cmpb.2025.108911","url":null,"abstract":"<div><h3>Background and objective</h3><div>General practice (GP) pre-diagnosis, a key task in disease triage, directs patients to suitable departments despite limited data and multi-label classification challenges. To address this issue, a framework with dimensionality reduction machine learning strategies was provided.</div></div><div><h3>Methods</h3><div>Disease information was organized into hierarchical tiers, focusing primarily on overarching disease classifications (I-level) and their subcategories (II-level). Two machine learning strategies were introduced and embedded into a framework. One was the classifier chain strategy, and the other one was ensemble learning-DNN (Deep Neural Networks) strategy. In classifier chains, the base candidate algorithms included XGBoost, RF (Random Forest), LR (Logistic Regression), and SVM (Support Vector Machine). In GP pre-diagnosis, the I-level and II-level disease information was progressively inferred. The efficacy of the methodologies was demonstrated through 3124 retrospective electronic medical records of patients complaining of abdominal pain. The performance metrics included AUPRC, AUROC, F1, accuracy, sensitivity, specificity, and hamming loss. The performance of different machine learning approaches was compared using the Friedman test, followed by the Nemenyi post-hoc test.</div></div><div><h3>Results</h3><div>The statistical results indicated that the Classifier chain-RF approach was optimal. For overarching disease categorizations, performance was excellent with nearly all metrics exceeding 0.90. For disease subcategories, performance slightly declined but remained highly effective, with most metrics surpassing 0.80.</div></div><div><h3>Conclusions</h3><div>The proposed framework exhibited its efficacy by performing well across various metrics and successfully accomplishing the established objectives, contributing insights to computer-aided diagnosis in the specific area of GP pre-diagnosis. Classifier chain-RF is recommended as an embedding approach.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"269 ","pages":"Article 108911"},"PeriodicalIF":4.9,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322393","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":"Weakly-supervised segmentation using sparse single point annotations for lumen and wall of carotid arteries in 3D MRI","authors":"Jonghun Kim ,&nbsp;Inye Na ,&nbsp;Junmo Kwon ,&nbsp;Woo-Keun Seo ,&nbsp;Hyunjin Park","doi":"10.1016/j.cmpb.2025.108881","DOIUrl":"10.1016/j.cmpb.2025.108881","url":null,"abstract":"<div><h3>Background and objective:</h3><div>Segmentation of the carotid artery is a crucial step in planning therapy for atherosclerosis. Manual annotation is a time-consuming and labor-intensive process, and there is a need to reduce this effort.</div></div><div><h3>Methods:</h3><div>We propose a weakly supervised segmentation method using only a few annotated axial slices, each with a single-point annotation from 3D magnetic resonance imaging for the lumen and wall of the carotid artery. The proposed method contains three loss functions designed to (1) locate the center point of the vessel, (2) constrain the range of the vessel radius using prior information implemented with spatial maps, and (3) encourage similar segmentation results in adjacent slices. Both the lumen (inner structure) and wall (outer structure) can be segmented by adjusting the range of plausible radii.</div></div><div><h3>Results:</h3><div>Experimental evaluations on the COSMOS2022 dataset show that our method achieved similar performance results (<span><math><msub><mrow><mtext>DSC</mtext></mrow><mrow><mi>l</mi><mi>u</mi></mrow></msub></math></span> 0.821 lumen, <span><math><msub><mrow><mtext>DSC</mtext></mrow><mrow><mi>w</mi><mi>a</mi></mrow></msub></math></span> 0.841 wall) to those of fully supervised methods with dense annotations (<span><math><msub><mrow><mtext>DSC</mtext></mrow><mrow><mi>l</mi><mi>u</mi></mrow></msub></math></span> 0.814-0.857, <span><math><msub><mrow><mtext>DSC</mtext></mrow><mrow><mi>w</mi><mi>a</mi></mrow></msub></math></span> 0.832-0.875). Similar trends were observed on an independent Harvard dataset.</div></div><div><h3>Conclusion:</h3><div>Our proposed method demonstrated effective segmentation of crucial arteries, internal carotid artery, external carotid artery, and common carotid artery in atherosclerosis. We anticipate that this efficient approach utilizing single-point annotation will contribute to the effective management of carotid atherosclerosis. Our code is available at <span><span>https://github.com/jongdory/CASCA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"269 ","pages":"Article 108881"},"PeriodicalIF":4.9,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307337","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":"Automated quantitative analysis of peri-articular bone microarchitecture in HR-pQCT knee images","authors":"Nathan J. Neeteson ,&nbsp;Sasha M. Hasick ,&nbsp;Roberto Souza ,&nbsp;Steven K. Boyd","doi":"10.1016/j.cmpb.2025.108882","DOIUrl":"10.1016/j.cmpb.2025.108882","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Applying HR-pQCT to image the knee necessitates the development and validation of novel image analysis workflows. Here, we present and validate the first automated workflow for &lt;em&gt;in vivo&lt;/em&gt; quantitative assessment of peri-articular bone density and microarchitecture in the knee. Segmentation models were first trained with radius and tibia images (N=2,598) then fine-tuned with knee images (N=131). Atlas-based registration was used to create medial and lateral contact surface masks, which were combined with bone segmentations to generate peri-articular regions of interest masks. The accuracy and precision of the workflow was assessed with an external validation dataset (N=128) and a triple-repeat measures dataset (N=29), respectively. Predicted and reference morphological parameters had linear coefficients of determination between 0.86 and 0.99, with moderate bias present in predictions of subchondral bone plate density and thickness. The average short-term precision RMS%CV estimates across all compartments and all morphological parameters ranged from 1.0 % to 2.9 %.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Background and Objective:&lt;/h3&gt;&lt;div&gt;There is growing interest in applying HR-pQCT to image the knee, particularly in the study of osteoarthritis. This necessitates the development and validation of novel image analysis workflows tailored to knee HR-pQCT images. In this work, we present and validate the first fully automated workflow for &lt;em&gt;in vivo&lt;/em&gt; quantitative assessment of peri-articular bone density and microarchitecture in the human knee.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods:&lt;/h3&gt;&lt;div&gt;Bone segmentation models were trained by transfer learning with a large dataset of radius and tibia images (N=2,598) and fine-tuned on a knee image dataset (N=131). Tibia and femur atlases were created and atlas-based registration was used to identify medial and lateral contact surfaces. Morphological operations combined bone segmentations and atlas-generated contact surface masks to generate peri-articular regions of interest masks, in which standard morphological analysis was applied. The accuracy and precision of estimated morphological parameters was assessed with an external validation dataset containing femurs and tibiae (N=128) and a triple-repeat measures dataset containing only tibiae (N=29), respectively.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results:&lt;/h3&gt;&lt;div&gt;On the external validation dataset, predicted and reference morphological parameters showed excellent correspondence (0.86 &lt;span&gt;&lt;math&gt;&lt;mo&gt;≤&lt;/mo&gt;&lt;/math&gt;&lt;/span&gt; R&lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt; &lt;span&gt;&lt;math&gt;&lt;mo&gt;≤&lt;/mo&gt;&lt;/math&gt;&lt;/span&gt; 0.99), with moderate bias present in predictions of subchondral bone plate density (−80 mg HA/cm&lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt;) and thickness (＋0.15 mm). With intra-participant rigid registration, the average short-term precision RMS%CV estimates across all compartments were 2.2 % and 2.8 % for subchondral bone ","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"269 ","pages":"Article 108882"},"PeriodicalIF":4.9,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297376","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":"Topological representation based on wavelet transform as a novel imaging biomarker for tumor diagnosis in ultrasound images: A comprehensive study","authors":"Qu Wei ,&nbsp;Si Zeng ,&nbsp;Zekun Jiang ,&nbsp;Fashu Xu ,&nbsp;Shuhua Shi ,&nbsp;Wen Wen ,&nbsp;Ziyuan Qin ,&nbsp;Zhenye Lou ,&nbsp;Kang Li","doi":"10.1016/j.cmpb.2025.108859","DOIUrl":"10.1016/j.cmpb.2025.108859","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Topological data analysis (TDA) and topological representation are emerging research directions in medical image analysis, aimed at mining the spatial topological features of diseases such as tumors, exploring biological complex patterns, and constructing predictive tools for clinical diagnosis and treatment. Ultrasound examinations are widely used in the preliminary diagnosis of various tumor diseases due to their convenience, real-time imaging, and non-radiation. However, since ultrasound examinations rely heavily on the subjective experience of doctors and often require pathological gold standards for tumor typing determination, there is an urgent need to develop novel quantitative analysis methods to build the correlation between ultrasound quantitative features and histological information, thereby optimizing clinical decision-making paths. The extensive evaluation and validation studies of TDA in ultrasound imaging are still scarcely reported.</div></div><div><h3>Methods:</h3><div>We proposed a novel ultrasound topological representation method, termed WT-TDA, which is based on wavelet transformation to enhance topological feature representation and combines SHAP-based feature selection to optimize modeling effects. We evaluated the algorithm on three ultrasound image datasets to verify the potential of topological analysis in clinical diagnosis.</div></div><div><h3>Results:</h3><div>The WT-TDA demonstrated ideal tumor diagnosis performance across breast, thyroid, and kidney ultrasound datasets, achieving the test accuracy of 0.932, 0.805, and 0.888 and test AUCs of 0.915, 0.805, and 0.889, respectively. Additionally, WT-TDA enabled the extraction of a set of ultrasound topological features that are beneficial for clinical analysis, and SHAP analysis enhanced the interpretability of the topological models.</div></div><div><h3>Conclusion:</h3><div>The study verifies the persistent homology of ultrasound images and demonstrates the potential application of WT-TDA in the benign and malignant diagnosis of ultrasound tumors, which can help optimize ultrasound diagnosis and provide decision support for ultrasound doctors.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"269 ","pages":"Article 108859"},"PeriodicalIF":4.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279862","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":"Artery fragment guided approach for enhancing cerebral aneurysm detection in TOF-MRA imaging","authors":"Chen Geng ,&nbsp;Yucheng Lu ,&nbsp;Peiyang Xue ,&nbsp;Bin Dai ,&nbsp;Yifang Bao ,&nbsp;Dunhui Bai ,&nbsp;Yuxin Li ,&nbsp;Yakang Dai","doi":"10.1016/j.cmpb.2025.108906","DOIUrl":"10.1016/j.cmpb.2025.108906","url":null,"abstract":"<div><h3>Background</h3><div>Cerebral aneurysms are a type of cerebrovascular disease that poses a severe threat to life and health. Early screening using Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) can effectively reduce the risk of rupture. Despite the importance of early detection, manual image screening remains a laborious and inefficient process. The current thrust of research in computer-aided detection (CAD) methods is to refine neural networks to improve diagnostic accuracy. In our preliminary work, we discovered that utilizing arterial contour as external knowledge guidance can substantially enhance the detection capabilities of existing networks, thus providing a new perspective for optimizing aneurysm detection techniques.</div></div><div><h3>Methods</h3><div>In this paper, we introduce an innovative approach to building a cerebral aneurysm detection model that employs artery fragment as external guidance data. We propose a hypothesis regarding the optimal distribution pattern of knowledge-guided data based on the brain artery volume of interest (VOI), and based on this, we have developed an end-to-end fully automatic and data-adaptive artery fragment generation method tailored for both training and testing data. Utilizing a multicenter dataset, we tested the performance enhancement capabilities of this method for two commonly used vascular networks, SE-3D UNet and VNet. Furthermore, we conducted a comparative analysis with other guidance methods using the best-performing model to elucidate the mechanisms behind the improved guidance efficacy of our approach.</div></div><div><h3>Results</h3><div>This study amassed a total of 500 cases of 3.0T TOF-MRA data from 13 devices across 6 hospitals, with 400 cases designated as the training set and 100 cases as the test set, while data from one device was exclusively used for testing. The proposed method showed significant improvements for both SE-3D UNet and VNet. Specifically, SE 3D UNET saw a 13.89 % increase in sensitivity while maintaining a false positives per case (FPs/case) of 0.63. For VNet, the FPs/case was reduced by 20 %, with a slight improvement in sensitivity. Compared to other guidance methods, our approach achieved optimal levels in various metrics and exhibited stronger robustness on unfamiliar datasets.</div></div><div><h3>Conclusions</h3><div>This study presents an artery fragment-guided approach that enhances the detection of cerebral aneurysms in TOF-MRA imaging. It not only outperforms our previous work but also excels when compared to alternative guidance methods. This approach offers a compelling knowledge-guided strategy for cerebral aneurysm detection.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"269 ","pages":"Article 108906"},"PeriodicalIF":4.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297377","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 novel fine-tuning and evaluation methodology for large language models on IoT raw data summaries (LLM-RawDMeth): A joint perspective in diabetes care","authors":"Juan F. Gaitán-Guerrero ,&nbsp;Carmen Martínez-Cruz ,&nbsp;Macarena Espinilla ,&nbsp;David Díaz-Jiménez ,&nbsp;Jose L. López","doi":"10.1016/j.cmpb.2025.108878","DOIUrl":"10.1016/j.cmpb.2025.108878","url":null,"abstract":"<div><h3>Background and objective:</h3><div>Diabetes is a global health concern, affecting millions of adults worldwide and exhibiting a growing prevalence. Managing the disease highly relies on continuous glucose monitoring, yet the dense and complex nature of electronic devices data streams poses significant challenges for efficient interpretation. Large Language Models are being widely applied across different domains for their ability to generate human-like text, but still fall short in producing accurate and meaningful text from raw data. To address this limitation, this study proposes a fine-tuning methodology tailored specifically to glucose data, but scalable to other expert-guided domains, enabling the models to generate concise, relevant and safe summaries, bridging the gap between raw data and efficient medical attention.</div></div><div><h3>Methods:</h3><div>This study introduces a novel continuous glucose monitoring framework that involves fine-tuned GPT models using structured datasets generated through an expert-guided data modeling based on Fuzzy Logic and prompt engineering for task contextualization. A new evaluation methodology is defined to assess the performance of the Large Language Models across different critical domains where expert knowledge is fundamental to characterize temporally dependent data and ensure valuable insights.</div></div><div><h3>Results:</h3><div>Fine-tuned GPT-4o achieved the highest performance, with an average score of 96% across all metrics. GPT-4o-mini followed with 76% score, while GPT-3.5 scored 72%. The use of fuzzy knowledge-based prompts proved more effective in scenarios with full data availability, or in scenarios with a simplified data availability when the models are not fine-tuned; domain-guided prompts improved output relevance and stability in fine-tuned models with less data availability.</div></div><div><h3>Conclusions:</h3><div>These results indicate the capability of our methods to align Large Language Models with the task of generating human-like text from raw data, highlighting their potential to manage diabetes by complex glucose patterns interpretation, alleviating the burden on healthcare systems.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"269 ","pages":"Article 108878"},"PeriodicalIF":4.9,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271031","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":"Modeling the coupling between stent degradation and vascular remodeling considering the effects of mechanical stimuli and geometric interaction","authors":"Hanbing Zhang ,&nbsp;Shiliang Chen ,&nbsp;Tianming Du ,&nbsp;Yanping Zhang ,&nbsp;Lifang Wu ,&nbsp;Aike Qiao","doi":"10.1016/j.cmpb.2025.108900","DOIUrl":"10.1016/j.cmpb.2025.108900","url":null,"abstract":"<div><h3>Background and objectives</h3><div>The degradation of stents and vascular remodeling are processes involving mechanical and geometric interactions. However, in previous studies, these two processes were treated as independent. This study aims to develop a finite element coupling model based on the constitutive and stress-growth relationships to investigate the impact of mechanical stimuli and geometric interactions on the coupled process.</div></div><div><h3>Methods</h3><div>A stent degradation model that incorporates multiple corrosion factors and a vascular remodeling model that considers artery stress stimuli were first established. Then, these two models were coupled on spatio-temporal scales, and the mechanical and geometric interactions between them were carefully configured by setting material properties and corrosion properties for the individual element as well as marking the element status. Based on this coupling model, we simulated stent degradation and vascular remodeling under different mechanical and geometric interaction conditions.</div></div><div><h3>Results</h3><div>Compared to constant initial stress stimuli following stent deployment, dynamic stress stimuli during the coupling process prolonged stent fracture time by 4 % due to reduced stress corrosion and altered the neointima volume trend from a continuous linear increase to a gradual convergence by mitigating artery damage. Furthermore, the dynamic changes in geometric interaction during coupling extended stent fracture time by 24 % through the neointima's coverage of the stent.</div></div><div><h3>Conclusions</h3><div>These findings highlight the significant influence of dynamic mechanical stimuli and geometric interactions on the coupling outcomes. Therefore, it is crucial to incorporate these factors into the coupling model. Ultimately, this model may provide a biomechanical foundation for understanding the supporting performance, degradation rate, and in-stent restenosis of biodegradable vascular stents in clinical settings.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"269 ","pages":"Article 108900"},"PeriodicalIF":4.9,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271030","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":"echoGAN: Extending the field of view in Transthoracic Echocardiography through conditional GAN-based outpainting","authors":"Matej Gazda ,&nbsp;Jakub Gazda ,&nbsp;Samuel Kadoury ,&nbsp;Robert Kanasz ,&nbsp;Peter Drotar","doi":"10.1016/j.cmpb.2025.108869","DOIUrl":"10.1016/j.cmpb.2025.108869","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Transthoracic Echocardiography (TTE) is a fundamental, non-invasive diagnostic tool in cardiovascular medicine, enabling detailed visualization of cardiac structures that is crucial for diagnosing various heart conditions. Despite its widespread use, TTE ultrasound imaging faces inherent limitations, notably a trade-off between field of view (FoV) and resolution.</div></div><div><h3>Methods:</h3><div>This paper introduces a novel conditional Generative Adversarial Network (cGAN), incorporating a domain-aware augmentation technique that simulates the typical cone-shaped FoV in ultrasound. This approach is specifically designed to enable effective outpainting of occluded areas, setting the foundation for our cGAN architecture, termed echoGAN.</div></div><div><h3>Results:</h3><div>The results, obtained on two different datasets, confirm that echoGAN demonstrates the capability to generate realistic anatomical structures through outpainting, effectively broadening the viewable area in medical imaging.</div></div><div><h3>Conclusions:</h3><div>This advancement has the potential to enhance both automatic and manual ultrasound navigation, offering a more comprehensive view that could significantly reduce the learning curve associated with ultrasound imaging.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"269 ","pages":"Article 108869"},"PeriodicalIF":4.9,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144240506","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":"Virtual cardiac histology: Towards a radiodensitometric characterization of left ventricular cardiac muscle in healthy and pathological conditions","authors":"Riccardo Forni ,&nbsp;Andrea Colacino ,&nbsp;Bruna Punzo ,&nbsp;Carlo Cavaliere ,&nbsp;Monica Franzese ,&nbsp;Aevar Orn Ulfarsson ,&nbsp;Cristiana Corsi ,&nbsp;Paolo Gargiulo","doi":"10.1016/j.cmpb.2025.108876","DOIUrl":"10.1016/j.cmpb.2025.108876","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Cardiovascular imaging plays a crucial role in disease understanding and case severity. Despite good results in morphological assessment due to an elevated spatial resolution, functional evaluation about cardiac tissue status is still lacking. The aim of the work was to perform a virtual cardiac histology, meaning to characterize cardiac tissue of the left ventricle with Computed Tomography images and use densitometric distribution to detect the presence of cardiac diseases such as acute myocardial infarction and hypertrophic cardiomyopathy.</div></div><div><h3>Methods:</h3><div>The study retrospectively analyzed volumetric data from sixty subjects, equally distributed among classes, developing a pipeline of image processing for the semi-automatic extraction of 3D virtual samples from different levels and segments. From each sample’s densitometric profile, a set of statistical descriptor were extracted.</div></div><div><h3>Results:</h3><div>The densitometric characterization detected heterogeneity in the left ventricular tissue, differentiating the more conductive myocytes of the septum with the more contractive myocytes of the other segments. In addition, a gradient of radiodensity was found as moving from the valvular plane (basal) to the apex of the heart. The intraventricular septum was also found as an eloquent structure in pathological changes due to myocardial infarction since a geometrical modification and shift of the profile was observed (Amplitude = 0.02, Muscle HU = 57). The hypertrophic cardiomyopathy caused significative changes in the contractile segments intensity (Muscle 5-7 HU increase) and shape of the profile (Amplitude = 0.21 inferior wall) reporting the absence of physiological fat and connective tissue in those segments (fat volume = 0.2 %).</div></div><div><h3>Conclusion:</h3><div>This study introduces a novel methodology leveraging CT densitometric properties to characterize left ventricular myocardium and distinguish healthy from pathological tissue. Significant patterns associated with hypertrophic cardiomyopathy and acute myocardial infarction highlight the potential of this approach for cardiac risk stratification.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"269 ","pages":"Article 108876"},"PeriodicalIF":4.9,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297337","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}