Computer methods and programs in biomedicine最新文献

{"title":"Unraveling the complex interplay between abnormal hemorheology and shape asymmetry in flow through stenotic arteries","authors":"","doi":"10.1016/j.cmpb.2024.108437","DOIUrl":"10.1016/j.cmpb.2024.108437","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Stenosis or narrowing of arteries due to the buildup of plaque is a common occurrence in atherosclerosis and coronary artery disease (CAD), limiting blood flow to the heart and posing substantial cardiovascular risk. While the role of geometric irregularities in arterial stenosis is well-documented, the complex interplay between the abnormal hemorheology and asymmetric shape in flow characteristics remains unexplored.</div></div><div><h3>Methods:</h3><div>This study investigates the influence of varying hematocrit (Hct) levels, often caused by conditions such as diabetes and anemia, on flow patterns in an idealized eccentric stenotic artery using computational fluid dynamics simulations. We consider three physiological levels of Hct, 25%, 45%, and 65%, representing anemia, healthy, and diabetic conditions, respectively. The numerical simulations are performed for different combinations of shape eccentricity and blood rheological parameters, and hemodynamic indicators such as wall shear stress (WSS), oscillatory shear index (OSI), are relative residence time (RRT) are calculated to assess the arterial health.</div></div><div><h3>Results:</h3><div>Our results reveal the significant influence of <span><math><mrow><mi>H</mi><mi>c</mi><mi>t</mi></mrow></math></span> level on stenosis progression. CAD patients with anemia are exposed to lower WSS and higher OSI, which may increase the propensity for plaque progression and rupture. However, for CAD patients with high <span><math><mrow><mi>H</mi><mi>c</mi><mi>t</mi></mrow></math></span> level — as is often the case in diabetes — the WSS at the minimal lumen area increases rapidly, which may also lead to plaque rupture and cause adverse events such as heart attacks. These disturbances promote endothelial dysfunction, inflammation, and thrombus formation, thereby intensifying cardiovascular risk.</div></div><div><h3>Conclusions:</h3><div>Our findings underscore the significance of incorporating hemorheological parameters, such as <span><math><mrow><mi>H</mi><mi>c</mi><mi>t</mi></mrow></math></span>, into computational models for accurate assessment of flow dynamics. We envision that insights gained from this study will inform the development of tailored treatment strategies and interventions in CAD patients with common comorbidities such as diabetes and anemia, thus mitigating the adverse effects of abnormal hemorheology and reducing the ever-growing burden of cardiovascular diseases.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142364651","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":"Paying attention to uncertainty: A stochastic multimodal transformers for post-traumatic stress disorder detection using video","authors":"","doi":"10.1016/j.cmpb.2024.108439","DOIUrl":"10.1016/j.cmpb.2024.108439","url":null,"abstract":"<div><h3>Background and Objectives:</h3><div>Post-traumatic stress disorder is a debilitating psychological condition that can manifest following exposure to traumatic events. It affects individuals from diverse backgrounds and is associated with various symptoms, including intrusive thoughts, nightmares, hyperarousal, and avoidance behaviors.</div></div><div><h3>Methods:</h3><div>To address this challenge this study proposes a decision support system powered by a novel multimodal deep learning approach, based on a stochastic Transformer and video data. This Transformer has the ability to take advantage of its stochastic activation function and layers that allow it to learn sparse representations of the inputs. The method leverages a combination of low-level features extracted using three modalities, including Mel-frequency cepstral coefficients extracted from audio recordings, Facial Action Units captured from facial expressions, and textual data obtained from the audio transcription. By considering these modalities, our proposed model captures a comprehensive range of information related to post-traumatic stress disorder symptoms, including vocal cues, facial expressions, and linguistic content.</div></div><div><h3>Results:</h3><div>The deep learning model was trained and evaluated on the eDAIC dataset, which consists of clinical interviews with individuals with and without post-traumatic disorder. The model achieved state-of-the-art results, demonstrating its effectiveness in accurately detecting PTSD, showing an impressive Root Mean Square Error of 1.98, and a Concordance Correlation Coefficient of 0.722, signifying the model’s superior performance compared to existing approaches.</div></div><div><h3>Conclusion:</h3><div>This work introduces a new method for post-traumatic stress disorder detection from videos by utilizing a multimodal stochastic Transformer model. The model makes use of a variety of modalities, such as text, audio, and visual data, to gather comprehensive and varied information in order to make the detection.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142326265","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":"GLCV-NET: An automatic diagnosis system for advanced liver fibrosis using global–local cross view in B-mode ultrasound images","authors":"","doi":"10.1016/j.cmpb.2024.108440","DOIUrl":"10.1016/j.cmpb.2024.108440","url":null,"abstract":"<div><h3>Background and objective</h3><div>: Advanced liver fibrosis is a critical stage in the evaluation of chronic liver disease (CLD), holding clinical significance in the development of treatment strategies and estimating the disease progression.</div></div><div><h3>Methods:</h3><div>This paper proposes an innovative Global–Local Cross-View Network (GLCV-Net) for the automatic diagnosis of advanced liver fibrosis from ultrasound (US) B-mode images. The proposed method consists of three main components: 1. A Segmentation-enhanced Global Hybrid Feature Extractor for segmenting the liver parenchyma and extracting global features; 2. A Heatmap-weighted Local Feature Extractor for selecting candidate regions and automatically identifying suspicious areas to construct local features; 3. A Scale-adaptive Fusion Module to balance the contributions of global and local scales in evaluating advanced liver fibrosis.</div></div><div><h3>Results:</h3><div>The predictive performance of the model was validated on an internal dataset of 1003 chronic liver disease (CLD) patients and an external dataset of 46 CLD patients, both subjected to liver fibrosis staging through pathological assessment. On the internal dataset, GLCV-Net achieved 86.9% accuracy, 85.0% recall, 85.4% precision, and 85.2% F1-score. Further validation on the external dataset confirmed its robustness, with scores of 86.1% in accuracy, 83.1% in recall, 80.8% in precision, and 81.9% in F1-score.</div></div><div><h3>Conclusion:</h3><div>These results underscore the GLCV-Net’s potential as a promising approach for non-invasively and accurately diagnosing advanced liver fibrosis in CLD patients, breaking through the limitations of traditional methods by integrating global and local information of liver fibrosis, significantly enhancing diagnostic accuracy.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142388709","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":"Forecasting glucose values for patients with type 1 diabetes using heart rate data","authors":"","doi":"10.1016/j.cmpb.2024.108438","DOIUrl":"10.1016/j.cmpb.2024.108438","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background:&lt;/h3&gt;&lt;div&gt;Type 1 Diabetes Mellitus (T1DM) is a chronic metabolic disease affecting millions of people worldwide. T1DM requires patients to continuously monitor their blood glucose levels. Due to pancreatic dysfunctions, patients use insulin injections to correct glucose values by synthetic insulin. Continuous Glucose Monitoring (CGM) is a system which includes an algorithm allowing to measure (and in some cases to predict) glucose levels at a frequent sampling time. This enable implementing advanced devices, including automated insulin pump delivery. Nevertheless, CGM still presents some limitations, including (i) the delay (time lag) in detecting change in glucose levels compared to the traditional blood glucose measurement, and (ii) the lack of a sufficient and acceptable time to accurately predict glucose values.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods:&lt;/h3&gt;&lt;div&gt;We propose a framework based on a Gated Recurrent Unit (GRU) model to forecast both short- and long-term glucose values using heart rate (HR) and interstitial glucose (IG) values. The framework acquires HR and IG data and predicts glucose values with higher precision compared to state-of-the-art models. For training and testing the proposed framework, we used the OhioT1DM Dataset, which includes physiological data such as HR and IG values collected over an 8-week observation period. Additionally, we validated our framework using two other glucose datasets to ensure its generalizability across different HR and IG sampling frequencies. The proposed framework can be used to optimize the CGM system by incorporating patient HR measurements, thereby improving the prediction of short- and long-term glucose levels and reducing risks associated with conditions like hypoglycemia.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results:&lt;/h3&gt;&lt;div&gt;Experimental tests were conducted using HR and IG data from the OhioT1DM Dataset, as well as from two additional T1DM patient datasets. We analyzed 6 patients from Ohio dataset while we validated the algorithm on 23 patients coming from two different university hospitals (6 from the University of Catanzaro medical hospital and 17 gathered from a validated study at IRCCS San Matteo Hospital in Pavia) for a total number of 29 patients. Our framework demonstrates an improvement in forecasting IG values in terms of RMSE and MAE for different choice of prediction horizons (PH). In the case of a PH of 5, 10, 20, 30, and 60 min, we reach an RMSE of 5.0, 9.38, 15.27, 20.48, and 34.16 respectively. The framework is freely available as an open-source, with an example dataset on a GitHub repository (see &lt;span&gt;&lt;span&gt;https://github.com/rafgia/attention_to_glycemia&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion:&lt;/h3&gt;&lt;div&gt;Our framework offers a promising solution for improving glucose level prediction and management in T1DM patients. By leveraging a GRU model and incorporating HR and IG values, we achieve more precise glucose level forecasting compared to state-of-t","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322389","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":"Heterogeneous biomechanical/mathematical modeling of spatial prediction of glioblastoma progression using magnetic resonance imaging-based finite element method","authors":"","doi":"10.1016/j.cmpb.2024.108441","DOIUrl":"10.1016/j.cmpb.2024.108441","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Brain tumors are one of the most common diseases and causes of death in humans. Since the growth of brain tumors has irreparable risks for the patient, predicting the growth of the tumor and knowing its effect on the brain tissue will increase the efficiency of treatment strategies.</div></div><div><h3>Methods</h3><div>This study examines brain tumor growth using mathematical modeling based on the Reaction-Diffusion equation and the biomechanical model based on continuum mechanics principles. With the help of the image threshold technique of magnetic resonance images, a heterogeneous and close-to-reality environment of the brain has been modeled and experimental data validated the results to achieve maximum accuracy in predicting growth.</div></div><div><h3>Results</h3><div>The obtained results have been compared with the reported conventional models to evaluate the presented model. In addition to incorporating the chemotherapy effects in governing equations, the real-time finite element analysis of the stress tensors of the surrounding tissue of tumor cells and considering its role in changing the shape and growth of the tumor has added to the importance and accuracy of the current model.</div></div><div><h3>Conclusions</h3><div>The comparison of the obtained results with conventional models shows that the heterogeneous model has higher reliability due to the consideration of the appropriate properties for the different regions of the brain. The presented model can contribute to personalized medicine, aid in understanding the dynamics of tumor growth, optimize treatment regimens, and develop adaptive therapy strategies.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356854","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":"Parameter quantification for oxygen transport in the human brain","authors":"","doi":"10.1016/j.cmpb.2024.108433","DOIUrl":"10.1016/j.cmpb.2024.108433","url":null,"abstract":"<div><h3>Background and objective:</h3><div>Oxygen is carried to the brain by blood flow through generations of vessels across a wide range of length scales. This multi-scale nature of blood flow and oxygen transport poses challenges on investigating the mechanisms underlying both healthy and pathological states through imaging techniques alone. Recently, multi-scale models describing whole brain perfusion and oxygen transport have been developed. Such models rely on effective parameters that represent the microscopic properties. While parameters of the perfusion models have been characterised, those for oxygen transport are still lacking. In this study, we set to quantify the parameters associated with oxygen transport and their uncertainties.</div></div><div><h3>Methods:</h3><div>Effective parameter values of a continuum-based porous multi-scale, multi-compartment oxygen transport model are systematically estimated. In particular, geometric parameters that capture the microvascular topologies are obtained through statistically accurate capillary networks. Maximum consumption rates of oxygen are optimised to uniquely define the oxygen distribution over depth. Simulations are then carried out within a one-dimensional tissue column and a three-dimensional patient-specific brain mesh using the finite element method.</div></div><div><h3>Results:</h3><div>Effective values of the geometric parameters, vessel volume fraction and surface area to volume ratio, are found to be 1.42% and 627 [mm<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>/mm<span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span>], respectively. These values compare well with those acquired from human and monkey vascular samples. Simulation results of the one-dimensional tissue column show qualitative agreement with experimental measurements of tissue oxygen partial pressure in rats. Differences between the oxygenation level in the tissue column and the brain mesh are observed, which highlights the importance of anatomical accuracy. Finally, one-at-a-time sensitivity analysis reveals that the oxygen model is not sensitive to most of its parameters; however, perturbations in oxygen solubilities and plasma to whole blood oxygen concentration ratio have a considerable impact on the tissue oxygenation.</div></div><div><h3>Conclusions:</h3><div>The findings of this study demonstrate the validity of using a porous continuum approach to model organ-scale oxygen transport and draw attention to the significance of anatomy and parameters associated with inter-compartment diffusion.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142371212","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":"Feature selection integrating Shapley values and mutual information in reinforcement learning: An application in the prediction of post-operative outcomes in patients with end-stage renal disease","authors":"","doi":"10.1016/j.cmpb.2024.108416","DOIUrl":"10.1016/j.cmpb.2024.108416","url":null,"abstract":"<div><h3>Background:</h3><div>In predicting post-operative outcomes for patients with end-stage renal disease, our study faced challenges related to class imbalance and a high-dimensional feature space. Therefore, with a focus on overcoming class imbalance and improving interpretability, we propose a novel feature selection approach using multi-agent reinforcement learning.</div></div><div><h3>Methods:</h3><div>We proposed a multi-agent feature selection model based on a comprehensive reward function that combines classification model performance, Shapley additive explanations values, and the mutual information. The definition of rewards in reinforcement learning is crucial for model convergence and performance improvement. Initially, we set a deterministic reward based on the mutual information between variables and the target class, selecting variables that are highly dependent on the class, thus accelerating convergence. We then prioritized variables that influence the minority class on a sample basis and introduced a dynamic reward distribution strategy using Shapley additive explanations values to improve interpretability and solve the class imbalance problem.</div></div><div><h3>Results:</h3><div>Involving the integration of electronic medical records, anesthesia records, operating room vital signs, and pre-operative anesthesia evaluations, our approach effectively mitigated class imbalance and demonstrated superior performance in ablation analysis. Our model achieved a 16% increase in the minority class F1 score and an 8.2% increase in the overall F1 score compared to the baseline model without feature selection.</div></div><div><h3>Conclusion:</h3><div>This study contributes important research findings that show that the multi-agent-based feature selection method can be a promising approach for solving the class imbalance problem.</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":"142343063","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":"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}