Computer methods and programs in biomedicine最新文献

{"title":"STGAT: Graph attention networks for deconvolving spatial transcriptomics data","authors":"","doi":"10.1016/j.cmpb.2024.108431","DOIUrl":"10.1016/j.cmpb.2024.108431","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Spatially resolved gene expression profiles are crucial for understanding tissue structure and function. However, the lack of single-cell resolution in these profiles demands their integration with single-cell RNA sequencing data for accurate dataset deconvolution. We propose STGAT, an innovative deconvolution method that leverages graph attention networks to enhance spatial transcriptomic (ST) data analysis.</div></div><div><h3>Methods:</h3><div>STGAT generates pseudo-ST data that more comprehensively represents the cell-type composition within real-ST data by using three different sampling probabilities. A comprehensive combined graph is then constructed to capture the complex relationships both across pseudo- and real-ST data and within each dataset. Moreover, integrating a graph attention network further enables STGAT to dynamically assign the weights to the connections between spots, significantly enhancing the accuracy of cell-type composition predictions.</div></div><div><h3>Results:</h3><div>Extensive comparative experiments on simulated and real-world datasets, demonstrate the superior performance of STGAT for cell-type deconvolution. The method outperforms six established methods and is robust across various biological contexts.</div></div><div><h3>Conclusion:</h3><div>STGAT exhibits more precise results in cell-type composition inference that are more consistent with the known knowledge, suggesting its potential utility in improving the resolution and accuracy of spatial transcriptomics data analysis.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142496562","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":"Automating COVID-19 epidemiological situation reports based on multiple data sources, the Netherlands, 2020 to 2023","authors":"","doi":"10.1016/j.cmpb.2024.108436","DOIUrl":"10.1016/j.cmpb.2024.108436","url":null,"abstract":"<div><h3>Background</h3><div>During the COVID-19 pandemic, the National Institute for Public Health and the Environment in the Netherlands developed a pipeline of scripts to automate and streamline the production of epidemiological situation reports (epi‑sitrep). The pipeline was developed for the Automation of Data Import, Summarization, and Communication (hereafter called the A-DISC pipeline).</div></div><div><h3>Objective</h3><div>This paper describes the A-DISC pipeline and provides a customizable scripts template that may be useful for other countries wanting to automate their infectious disease surveillance processes.</div></div><div><h3>Methods</h3><div>The A-DISC pipeline was developed using the open-source statistical software R. It is organized in four modules: <em>Prepare, Process data, Produce report</em>, and <em>Communicate.</em> The <em>Prepare</em> scripts set the working environment (e.g., load packages). The (data-specific) <em>Process data</em> scripts import, validate, verify, transform, save, analyze, and summarize data as tables and figures and store these data summaries. The <em>Produce report</em> scripts gather summaries from multiple data sources and integrate them into a RMarkdown document – the epi‑sitrep. The <em>Communicate</em> scripts send e-mails to stakeholders with the epi‑sitrep.</div></div><div><h3>Results</h3><div>As of March 2023, up to ten data sources were automatically summarized into tables and figures by A-DISC. These data summaries were featured in routine extensive COVID-19 epi‑sitreps, shared as open data, plotted on RIVM's website, sent to stakeholders and submitted to European Centre for Disease Prevention and Control via the European Surveillance System -TESSy [<span><span>38</span></span>].</div></div><div><h3>Discussion</h3><div>In the face of an unprecedented high number of cases being reported during the COVID-19 pandemic, the A-DISC pipeline was essential to produce frequent and comprehensive epi‑sitreps. A-DISC's modular and intuitive structure allowed for the integration of data sources of varying complexities, encouraged collaboration among people with various R-scripting capabilities, and improved data lineage. The A-DISC pipeline remains under active development and is currently being used in modified form for the automatization and professionalization of various other disease surveillance processes at the RIVM, with high acceptance from the participant epidemiologists.</div></div><div><h3>Conclusion</h3><div>The A-DISC pipeline is an open-source, robust, and customizable tool for automating epi‑sitreps based on multiple data sources.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142343062","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":"Physics-Informed Graph Neural Networks to solve 1-D equations of blood flow","authors":"","doi":"10.1016/j.cmpb.2024.108427","DOIUrl":"10.1016/j.cmpb.2024.108427","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Computational models of hemodynamics can contribute to optimizing surgical plans, and improve our understanding of cardiovascular diseases. Recently, machine learning methods have become essential to reduce the computational cost of these models. In this study, we propose a method that integrates 1-D blood flow equations with Physics-Informed Graph Neural Networks (PIGNNs) to estimate the propagation of blood flow velocity and lumen area pulse waves along arteries.</div></div><div><h3>Methods:</h3><div>Our methodology involves the creation of a graph based on arterial topology, where each 1-D line represents edges and nodes in the blood flow analysis. The innovation lies in decoding the mathematical data connecting the nodes, where each node has velocity and lumen area pulse waveform outputs. The training protocol for PIGNNs involves measurement data, specifically velocity waves measured from inlet and outlet vessels and diastolic lumen area measurements from each vessel. To optimize the learning process, our approach incorporates fundamental physical principles directly into the loss function. This comprehensive training strategy not only harnesses the power of machine learning but also ensures that PIGNNs respect fundamental laws governing fluid dynamics.</div></div><div><h3>Results:</h3><div>The accuracy was validated <em>in silico</em> with different arterial networks, where PIGNNs achieved a coefficient of determination (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>) consistently above 0.99, comparable to numerical methods like the discontinuous Galerkin scheme. Moreover, with <em>in vivo</em> data, the prediction reached <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> values greater than 0.80, demonstrating the method’s effectiveness in predicting flow and lumen dynamics using minimal data.</div></div><div><h3>Conclusions:</h3><div>This study showcased the ability to calculate lumen area and blood flow rate in blood vessels within a given topology by seamlessly integrating 1-D blood flow with PIGNNs, using only blood flow velocity measurements. Moreover, this study is the first to compare the PIGNNs method with other classic Physics-Informed Neural Network (PINNs) approaches for blood flow simulation. Our findings highlight the potential to use this cost-effective and proficient tool to estimate real-time arterial pulse waves.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319229","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":"Lazy Resampling: Fast and information preserving preprocessing for deep learning","authors":"","doi":"10.1016/j.cmpb.2024.108422","DOIUrl":"10.1016/j.cmpb.2024.108422","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Preprocessing of data is a vital step for almost all deep learning workflows. In computer vision, manipulation of data intensity and spatial properties can improve network stability and can provide an important source of generalisation for deep neural networks. Models are frequently trained with preprocessing pipelines composed of many stages, but these pipelines come with a drawback; each stage that resamples the data costs time, degrades image quality, and adds bias to the output. Long pipelines can also be complex to design, especially in medical imaging, where cropping data early can cause significant artifacts.</div></div><div><h3>Methods:</h3><div>We present Lazy Resampling, a software that rephrases spatial preprocessing operations as a graphics pipeline. Rather than each transform individually modifying the data, the transforms generate transform descriptions that are composited together into a single resample operation wherever possible. This reduces pipeline execution time and, most importantly, limits signal degradation. It enables simpler pipeline design as crops and other operations become non-destructive. Lazy Resampling is designed in such a way that it provides the maximum benefit to users without requiring them to understand the underlying concepts or change the way that they build pipelines.</div></div><div><h3>Results:</h3><div>We evaluate Lazy Resampling by comparing traditional pipelines and the corresponding lazy resampling pipeline for the following tasks on Medical Segmentation Decathlon datasets. We demonstrate lower information loss in lazy pipelines vs. traditional pipelines. We demonstrate that Lazy Resampling can avoid catastrophic loss of semantic segmentation label accuracy occurring in traditional pipelines when passing labels through a pipeline and then back through the inverted pipeline. Finally, we demonstrate statistically significant improvements when training UNets for semantic segmentation.</div></div><div><h3>Conclusion:</h3><div>Lazy Resampling reduces the loss of information that occurs when running processing pipelines that traditionally have multiple resampling steps and enables researchers to build simpler pipelines by making operations such as rotation and cropping effectively non-destructive. It makes it possible to invert labels back through a pipeline without catastrophic loss of accuracy.</div><div>A reference implementation for Lazy Resampling can be found at <span><span>https://github.com/KCL-BMEIS/LazyResampling</span><svg><path></path></svg></span>. Lazy Resampling is being implemented as a core feature in MONAI, an open source python-based deep learning library for medical imaging, with a roadmap for a full integration.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421046","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":"A robust myoelectric pattern recognition framework based on individual motor unit activities against electrode array shifts","authors":"","doi":"10.1016/j.cmpb.2024.108434","DOIUrl":"10.1016/j.cmpb.2024.108434","url":null,"abstract":"<div><h3>Background and objective</h3><div>Electrode shift is always one of the critical factors to compromise the performance of myoelectric pattern recognition (MPR) based on surface electromyogram (SEMG). However, current studies focused on the global features of SEMG signals to mitigate this issue but it is just an oversimplified description of the human movements without incorporating microscopic neural drive information. The objective of this work is to develop a novel method for calibrating the electrode array shifts toward achieving robust MPR, leveraging individual motor unit (MU) activities obtained through advanced SEMG decomposition.</div></div><div><h3>Methods</h3><div>All of the MUs from decomposition of SEMG data recorded at the original electrode array position were first initialized to train a neural network for pattern recognition. A part of decomposed MUs could be tracked and paired with MUs obtained at the original position based on spatial distribution of their MUAP waveforms, so as to determine the shift vector (describing both the orientation and distance of the shift) implicated consistently by these multiple MU pairs. Given the known shift vector, the features of the after-shift decomposed MUs were corrected accordingly and then fed into the network to finalize the MPR task. The performance of the proposed method was evaluated with data recorded by a 16 × 8 electrode array placed over the finger extensor muscles of 8 subjects performing 10 finger movement patterns.</div></div><div><h3>Results</h3><div>The proposed method achieved a shift detection accuracy of 100 % and a pattern recognition accuracy approximating to 100 %, significantly outperforming the conventional methods with lower shift detection accuracies and lower pattern recognition accuracies (<em>p</em> &lt; 0.05).</div></div><div><h3>Conclusions</h3><div>Our method demonstrated the feasibility of using decomposed MUAP waveforms’ spatial distributions to calibrate electrode shift. This study provides a new tool to enhance the robustness of myoelectric control systems via microscopic neural drive information at an individual MU level.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142326264","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":"Decoding motor imagery loaded on steady-state somatosensory evoked potential based on complex task-related component analysis","authors":"","doi":"10.1016/j.cmpb.2024.108425","DOIUrl":"10.1016/j.cmpb.2024.108425","url":null,"abstract":"<div><h3>Background and objective</h3><div>Motor Imagery (MI) recognition is one of the most critical decoding problems in brain- computer interface field. Combined with the steady-state somatosensory evoked potential (MI-SSSEP), this new paradigm can achieve higher recognition accuracy than the traditional MI paradigm. Typical algorithms do not fully consider the characteristics of MI-SSSEP signals. Developing an algorithm that fully captures the paradigm's characteristics to reduce false triggering rate is the new step in improving performance.</div></div><div><h3>Methods</h3><div>The idea to use complex signal task-related component analysis (cTRCA) algorithm for spatial filtering processing has been proposed in this paper according to the features of SSSEP signal. In this research, it's proved from the analysis of simulation signals that task-related component analysis (TRCA) as typical method is affected when the response between stimuli has reduced correlation and the proposed algorithm can effectively overcome this problem. The experimental data under the MI-SSSEP paradigm have been used to identify right-handed target tasks and three unique interference tasks are used to test the false triggering rate. cTRCA demonstrates superior performance as confirmed by the Wilcoxon signed-rank test.</div></div><div><h3>Results</h3><div>The recognition algorithm of cTRCA combined with mutual information-based best individual feature (MIBIF) and minimum distance to mean (MDM) can obtain AUC value up to 0.89, which is much higher than traditional algorithm common spatial pattern (CSP) combined with support vector machine (SVM) (the average AUC value is 0.77, <em>p</em> &lt; 0.05). Compared to CSP+SVM, this algorithm model reduced the false triggering rate from 38.69 % to 20.74 % (<em>p</em> &lt; 0.001).</div></div><div><h3>Conclusions</h3><div>The research prove that TRCA is influenced by MI-SSSEP signals. The results further prove that the motor imagery task in the new paradigm MI-SSSEP causes the phase change in evoked potential. and the cTRCA algorithm based on such phase change is more suitable for this hybrid paradigm and more conducive to decoding the motor imagery task and reducing false triggering rate.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315333","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":"Does GLP-1 cause post-bariatric hypoglycemia: ‘Computer says no’","authors":"","doi":"10.1016/j.cmpb.2024.108424","DOIUrl":"10.1016/j.cmpb.2024.108424","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Patients who underwent Roux-en-Y Gastric Bypass surgery for treatment of obesity or diabetes can suffer from post-bariatric hypoglycemia (PBH). It has been assumed that PBH is caused by increased levels of the hormone GLP-1. In this research, we elucidate the role of GLP-1 in PBH with a physiology-based mathematical model.</div></div><div><h3>Methods:</h3><div>The Eindhoven Diabetes Simulator (EDES) model, simulating postprandial glucose homeostasis, was adapted to include the effect of GLP-1 on insulin secretion. Parameter sensitivity analysis was used to identify parameters that could cause PBH. Virtual patient models were created by defining sets of models parameters based on 63 participants from the HypoBaria study cohort, before and one year after bariatric surgery.</div></div><div><h3>Results:</h3><div>Simulations with the virtual patient models showed that glycemic excursions can be correctly simulated for the study population, despite heterogeneity in the glucose, insulin and GLP-1 data. Sensitivity analysis showed that GLP-1 stimulated insulin secretion alone was not able to cause PBH. Instead, analyses showed the increased transit speed of the ingested food resulted in quick and increased glucose absorption in the gut after surgery, which in turn induced postprandial glycemic dips. Furthermore, according to the model post-bariatric increased rate of glucose absorption in combination with different levels of insulin sensitivity can result in PBH.</div></div><div><h3>Conclusions:</h3><div>Our model findings implicate that if initial rapid improvement in insulin sensitivity after gastric bypass surgery is followed by a more gradual decrease in insulin sensitivity, this may result in the emergence of PBH after prolonged time (months to years after surgery).</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319227","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":"NecroGlobalGCN: Integrating micronecrosis information in HCC prognosis prediction via graph convolutional neural networks","authors":"","doi":"10.1016/j.cmpb.2024.108435","DOIUrl":"10.1016/j.cmpb.2024.108435","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Hepatocellular carcinoma (HCC) ranks fourth in cancer mortality, underscoring the importance of accurate prognostic predictions to improve postoperative survival rates in patients. Although micronecrosis has been shown to have high prognostic value in HCC, its application in clinical prognosis prediction requires specialized knowledge and complex calculations, which poses challenges for clinicians. It would be of interest to develop a model to help clinicians make full use of micronecrosis to assess patient survival.</div></div><div><h3>Methods</h3><div>To address these challenges, we propose a HCC prognosis prediction model that integrates pathological micronecrosis information through Graph Convolutional Neural Networks (GCN). This approach enables GCN to utilize micronecrosis, which has been shown to be highly correlated with prognosis, thereby significantly enhancing prognostic stratification quality. We developed our model using 3622 slides from 752 patients with primary HCC from the FAH-ZJUMS dataset and conducted internal and external validations on the FAH-ZJUMS and TCGA-LIHC datasets, respectively.</div></div><div><h3>Results</h3><div>Our method outperformed the baseline by 8.18% in internal validation and 9.02% in external validations. Overall, this paper presents a deep learning research paradigm that integrates HCC micronecrosis, enhancing both the accuracy and interpretability of prognostic predictions, with potential applicability to other pathological prognostic markers.</div></div><div><h3>Conclusions</h3><div>This study proposes a composite GCN prognostic model that integrates information on HCC micronecrosis, collecting large dataset of HCC histopathological images. This approach could assist clinicians in analyzing HCC patient survival and precisely locating and visualizing necrotic tissues that affect prognosis. Following the research paradigm outlined in this paper, other prognostic biomarker integration models with GCN could be developed, significantly enhancing the predictive performance and interpretability of prognostic model.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142364650","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":"Combining clinical and molecular data for personalized treatment in acute myeloid leukemia: A machine learning approach","authors":"","doi":"10.1016/j.cmpb.2024.108432","DOIUrl":"10.1016/j.cmpb.2024.108432","url":null,"abstract":"<div><h3>Background and Objective</h3><div>The standard of care in <em>Acute Myeloid Leukemia</em> patients has remained essentially unchanged for nearly 40 years. Due to the complicated mutational patterns within and between individual patients and a lack of targeted agents for most mutational events, implementing individualized treatment for AML has proven difficult. We reanalysed the <em>BeatAML dataset</em> employing <em>Machine Learning algorithms</em>. The BeatAML project entails patients extensively characterized at the molecular and clinical levels and linked to drug sensitivity outputs. Our approach capitalizes on the molecular and clinical data provided by the <em>BeatAML dataset</em> to predict the <em>ex vivo</em> drug sensitivity for the 122 drugs evaluated by the project.</div></div><div><h3>Methods</h3><div>We utilized ElasticNet, which produces fully interpretable models, in combination with a two-step training protocol that allowed us to narrow down computations. We automated the genes’ filtering step by employing two metrics, and we evaluated all possible data combinations to identify the best training configuration settings per drug.</div></div><div><h3>Results</h3><div>We report a Pearson correlation across all drugs of 0.36 when clinical and RNA sequencing data were combined, with the best-performing models reaching a Pearson correlation of 0.67. When we trained using the datasets in isolation, we noted that RNA Sequencing data (Pearson: 0.36) attained three times the predictive power of whole exome sequencing data (Pearson: 0.11), with clinical data falling somewhere in between (Pearson 0.26). Lastly, we present a paradigm of clinical significance. We used our models’ prediction as a <em>drug sensitivity score</em> to rank an individual's expected response to treatment. We identified 78 patients out of 89 (88 %) that the proposed drug was more potent than the administered one based on their <em>ex vivo</em> drug sensitivity data.</div></div><div><h3>Conclusions</h3><div>In conclusion, our reanalysis of the BeatAML dataset using Machine Learning algorithms demonstrates the potential for individualized treatment prediction in Acute Myeloid Leukemia patients, addressing the longstanding challenge of treatment personalization in this disease. By leveraging molecular and clinical data, our approach yields promising correlations between predicted drug sensitivity and actual responses, highlighting a significant step forward in improving therapeutic outcomes for AML patients.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169260724004255/pdfft?md5=2452e4d62b109d4cc19e47265be2ee8e&pid=1-s2.0-S0169260724004255-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310716","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":"Development, calibration and validation of impact-specific cervical spine models: A novel approach using hybrid multibody and finite-element methods","authors":"","doi":"10.1016/j.cmpb.2024.108430","DOIUrl":"10.1016/j.cmpb.2024.108430","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Spinal cord injuries can have a severe impact on athletes’ or patients’ lives. High axial impact scenarios like tackling and scrummaging can cause hyperflexion and buckling of the cervical spine, which is often connected with bilateral facet dislocation. Typically, finite-element (FE) or musculoskeletal models are applied to investigate these scenarios, however, they have the drawbacks of high computational cost and lack of soft tissue information, respectively. Moreover, material properties of the involved tissues are commonly tested in quasi-static conditions, which do not accurately capture the mechanical behavior during impact scenarios. Thus, the aim of this study was to develop, calibrate and validate an approach for the creation of impact-specific hybrid, rigid body - finite-element spine models for high-dynamic axial impact scenarios.</div></div><div><h3>Methods:</h3><div>Five porcine cervical spine models were used to replicate in-vitro experiments to calibrate stiffness and damping parameters of the intervertebral joints by matching the kinematics of the in-vitro with the in-silico experiments. Afterwards, a five-fold cross-validation was conducted. Additionally, the von Mises stress of the lumped FE-discs was investigated during impact.</div></div><div><h3>Results:</h3><div>The results of the calibration and validation of our hybrid approach agree well with the in-vitro experiments. The stress maps of the lumped FE-discs showed that the highest stress of the most superior lumped disc was located anterior while the remaining lumped discs had their maximum in the posterior portion.</div></div><div><h3>Conclusion:</h3><div>Our hybrid method demonstrated the importance of impact-specific modeling. Overall, our hybrid modeling approach enhances the possibilities of identifying spine injury mechanisms by facilitating dynamic, impact-specific computational models.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169260724004231/pdfft?md5=ce1e473dd9a8e164ca33393fec592b86&pid=1-s2.0-S0169260724004231-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310832","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}