Computer methods and programs in biomedicine最新文献

{"title":"A consistent decision support system for interpreting of magnetocardiographic data as a tool to improve the acceptance of magnetocardiography in clinical practice","authors":"Illya Chaikovsky,&nbsp;Igor Nedayvoda,&nbsp;Mykhailo Primin","doi":"10.1016/j.cmpb.2024.108489","DOIUrl":"10.1016/j.cmpb.2024.108489","url":null,"abstract":"<div><h3>Background</h3><div>Magnetocardiography undoubtedly has exceptionally high sensitivity to electrophysiological changes in the myocardium. This is an absolutely non-invasivemethod with no contraindications. However, several barriers exist to the widespread adoption of this technique into clinical routine. One of the most important is the lack of a clear and consistent medical algorithm for interpreting magnetocardiographic data, leading to a clinically significant decision.</div></div><div><h3>Areas covered</h3><div>The article outlines the main clinical questions clinicians pose using the magnetocardiography method. Methods for assessing the degree of abnormality of the results of a magnetocardiographic study and differential diagnosis based on the analysis of CDV maps are described in detail. Both methods for visual evaluation of sets of these maps and automatic decision rules based on linear discriminant analysis and pattern recognition are characterized. Also, techniques are described for localizing the pathological changes in the myocardium. As an example of using the developed system for interpreting magnetocardiographic data, the results of two multicenter studies in which this system of interpretation of MCG studies was used are presented.</div></div><div><h3>Сonclusion</h3><div>The magnetocardiographic examination is reliable for diagnosing chronic coronary heart disease, including in difficult-to-diagnose cases. A consistent system for interpreting of magnetocardiographic data allows medical practitioners to easily master the MCG technology and obtain the correct examination result.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"258 ","pages":"Article 108489"},"PeriodicalIF":4.9,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616197","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":"Prediction of intracranial electric field strength and analysis of treatment protocols in tumor electric field therapy targeting gliomas of the brain","authors":"Jun Wen ,&nbsp;Lingzhi Xiong ,&nbsp;Shulu Wang ,&nbsp;Xiaoguang Qiu ,&nbsp;Jianqiao Cui ,&nbsp;Fan Peng ,&nbsp;Xiang Liu ,&nbsp;Jian Lu ,&nbsp;Haikuo Bian ,&nbsp;Dikang Chen ,&nbsp;Jiusheng Chang ,&nbsp;Zhengxi Yao ,&nbsp;Sheng Fan ,&nbsp;Dan Zhou ,&nbsp;Ze Li ,&nbsp;Jialin Liu ,&nbsp;Hongyu Liu ,&nbsp;Xu Chen ,&nbsp;Ling Chen","doi":"10.1016/j.cmpb.2024.108490","DOIUrl":"10.1016/j.cmpb.2024.108490","url":null,"abstract":"<div><h3>Background and objective</h3><div>Tumor Electric Field Therapy (TEFT) is a new treatment for glioblastoma cells with significant effect and few side effects. However, it is difficult to directly measure the intracranial electric field generated by TEFT, and the inability to control the electric field intensity distribution in the tumor target area also limits the clinical therapeutic effect of TEFT. It is a safe and effective way to construct an efficient and accurate prediction model of intracranial electric field intensity of TEFT by numerical simulation.</div></div><div><h3>Methods</h3><div>Different from the traditional methods, in this study, the brain tissue was segmented based on the MRI data of patients with retained spatial location information, and the spatial position of the brain tissue was given the corresponding electrical parameters after segmentation. Then, a single geometric model of the head profile with the transducer array is constructed, which is assembled with an electrical parameter matrix containing tissue position information. After applying boundary conditions on the transducer, the intracranial electric field intensity could be solved in the frequency domain. The effects of transducer array mode, load voltage and voltage frequency on the intracranial electric field strength were further analyzed. Finally, planning system software was developed for optimizing TEFT treatment regimens for patients.</div></div><div><h3>Results</h3><div>Experimental validation and comparison with existing results demonstrate the proposed method has a more efficient and pervasive modeling approach with higher computational accuracy while preserving the details of MRI brain tissue structure completely. In the optimization analysis of treatment protocols, it was found that increasing the load voltage could effectively increase the electric field intensity in the target area, while the effect of voltage frequency on the electric field intensity was very limited.</div></div><div><h3>Conclusions</h3><div>The results showed that adjusting the transducer array mode was the key method for making targeted treatment plans. The proposed method is capable prediction of intracranial electric field strength with high accuracy and provide guidance for the design of the TEFT therapy process. This study provides a valuable reference for the application of TEFT in clinical practice.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"258 ","pages":"Article 108490"},"PeriodicalIF":4.9,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616155","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 computationally efficient anisotropic electrophysiological multiscale uterus model: From cell to organ and myometrium to abdominal surface","authors":"Yongxiu Yang ,&nbsp;Chris Bradley ,&nbsp;Guangfei Li ,&nbsp;Rogelio Monfort-Ortiz ,&nbsp;Felix Nieto-del-Amor ,&nbsp;Dongmei Hao ,&nbsp;Yiyao Ye-Lin","doi":"10.1016/j.cmpb.2024.108487","DOIUrl":"10.1016/j.cmpb.2024.108487","url":null,"abstract":"<div><h3>Background and objective</h3><div>Preterm labor is a global problem affecting the health of newborns. Despite numerous studies reporting electrophysiological changes throughout pregnancy, the underlying mechanism that triggers labor remains unclear. Electrophysiological modeling can provide additional information to better understand the physiological transition from pregnancy to labor. Previous uterine electrophysiological models do not consider either the tissue thickness or fiber structure, which have both been shown to significantly impact propagation patterns.</div></div><div><h3>Methods</h3><div>This paper presents a parallel computational model of the uterus using the bioengineering modeling environment OpenCMISS. This model is a multiscale anisotropic model that spans different levels from cell to organ. At the cellular level, the model utilizes a mathematical representation of uterine myocytes based on multiple ion channels. In the 3D uterine model, fiber structures are added, ranging from horizontal rings in the inner layer to vertically downward fibers in the outer layer, to more accurately depict the electrophysiological activities of the uterus. Additionally, we have developed a multilayer volume conduction model based on the boundary element method to describe the propagation of electrical signals from the myometrium to the abdominal surface.</div></div><div><h3>Results</h3><div>Our model can not only reproduce faithfully both local non-propagated and global propagated electrical activity, but also simulate the fast wave low and fast wave high components of the electrohysterogram (EHG) on the abdominal surface. The model results support the hypothesis that the fast wave high of the EHG signal is related to uterine excitability and fast wave low is related to signal propagation. The amplitude of the simulated signal on the abdominal surface falls in the ranges of real EHG data, which is inversely proportional to the abdominal subcutaneous fat thickness, and the signal waveform highly depends on electrode position and the relative distance to the pacemaker. In addition, the propagation velocity is highly dependent on the uterus geometry and falls in the real-world data range</div></div><div><h3>Conclusions</h3><div>Our models facilitate a better understanding of the electrophysiological changes of the uterus during pregnancy and labor, and allow for an investigation of drug effects and/or structural or anatomical abnormalities.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108487"},"PeriodicalIF":4.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142590344","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":"Gamified devices for stroke rehabilitation: A systematic review","authors":"Juan J. Sánchez-Gil ,&nbsp;Aurora Sáez-Manzano ,&nbsp;Rafael López-Luque ,&nbsp;Juan-José Ochoa-Sepúlveda ,&nbsp;Eduardo Cañete-Carmona","doi":"10.1016/j.cmpb.2024.108476","DOIUrl":"10.1016/j.cmpb.2024.108476","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Rehabilitation after stroke is essential to minimize permanent disability. Gamification, the integration of game elements into non-game environments, has emerged as a promising strategy for increasing motivation and rehabilitation effectiveness. This article systematically reviews the gamified devices used in stroke rehabilitation and evaluates their impact on emotional, social, and personal effects on patients, providing a comprehensive view of gamified rehabilitation.</div></div><div><h3>Methods:</h3><div>A comprehensive search using the PRISMA 2020 guidelines was conducted using the IEEE Xplore, PubMed, Springer Link, APA PsycInfo, and ScienceDirect databases. Empirical studies published between January 2019 and December 2023 that quantified the effects of gamification in terms of usability, motivation, engagement, and other qualitative patient responses were selected.</div></div><div><h3>Results:</h3><div>In total, 169 studies involving 6404 patients were included. Gamified devices are categorized into four types: robotic/motorized, non-motorized, virtual reality, and neuromuscular electrical stimulation. The results showed that gamified devices not only improved motor and cognitive function but also had a significant positive impact on patients’ emotional, social and personal levels. Most studies have reported high levels of patient satisfaction and motivation, highlighting the effectiveness of gamification in stroke rehabilitation.</div></div><div><h3>Conclusions:</h3><div>Gamification in stroke rehabilitation offers significant benefits beyond motor and cognitive recovery by improving patients’ emotional and social well-being. This systematic review provides a comprehensive overview of the most effective gamified technologies and highlights the need for future multidisciplinary research to optimize the design and implementation of gamified solutions in stroke rehabilitation.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"258 ","pages":"Article 108476"},"PeriodicalIF":4.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616229","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":"Physiological model-based machine learning for classifying patients with binge-eating disorder (BED) from the Oral Glucose Tolerance Test (OGTT) curve","authors":"Anna Procopio ,&nbsp;Marianna Rania ,&nbsp;Paolo Zaffino ,&nbsp;Nicola Cortese ,&nbsp;Federica Giofrè ,&nbsp;Franco Arturi ,&nbsp;Cristina Segura-Garcia ,&nbsp;Carlo Cosentino","doi":"10.1016/j.cmpb.2024.108477","DOIUrl":"10.1016/j.cmpb.2024.108477","url":null,"abstract":"<div><h3>Background and objective:</h3><div>Binge eating disorder (BED) is the most frequent eating disorder, often confused with obesity, with which it shares several characteristics. Early identification could enable targeted therapeutic interventions. In this study, we propose a hybrid pipeline that, starting from plasma glucose data acquired during the Oral Glucose Tolerance Test (OGTT), allows us to classify the two types of patients through computational modeling and artificial intelligence.</div></div><div><h3>Methods:</h3><div>The proposed hybrid pipeline integrates a classical mechanistic model of delayed differential equations (DDE) that describes glucose–insulin dynamics with machine learning (ML) methods. Ad hoc techniques, including structural identifiability analysis, have been employed for refining and evaluating the mathematical model. Additionally, a dedicated pipeline for identifying and optimizing model parameters has been applied to obtain reliable estimates. Robust feature extraction and classifier selection processes were developed to ensure the optimal choice of the best-performing classifier.</div></div><div><h3>Results:</h3><div>By leveraging parameters estimated from the mechanistic model alongside easily obtainable patient information (such as glucose levels at 30 and 120 min post-OGTT, glycated hemoglobin (Hb1Ac), body mass index (BMI), and waist circumference), our approach facilitates accurate classification of patients, enabling tailored therapeutic interventions.</div></div><div><h3>Conclusion:</h3><div>Initial findings, focusing on correctly categorizing patients with BED based on metabolic data, demonstrate promising outcomes. These results suggest significant potential for refinement, including exploration of alternative mechanistic models and machine learning algorithms, to enhance classification accuracy and therapeutic strategies.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"258 ","pages":"Article 108477"},"PeriodicalIF":4.9,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142603429","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":"Optimization of grinding parameters in robotic-assisted preparation of cracked teeth based on fracture mechanics: FEA and experiment","authors":"Jianpeng Sun ,&nbsp;Jingang Jiang ,&nbsp;Biao Ma ,&nbsp;Yongde Zhang ,&nbsp;Jie Pan ,&nbsp;Di Qiao","doi":"10.1016/j.cmpb.2024.108485","DOIUrl":"10.1016/j.cmpb.2024.108485","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>If left untreated, cracked teeth can lead to tooth loss, of which the incidence is 70%. Dental preparation is an effective treatment, but it is difficult to meet the clinical requirements when traditionally prepared by dentists. Grinding-based tooth preparation robot (TPR) shows promise for clinical applications to assist dentists. However, current TPR has problems with chipping and crack extension when preparing real teeth.</div></div><div><h3>Methods</h3><div>We propose a grinding parameter optimization strategy to solve this problem, specifically including preparation depth and direction. Among them, surface morphology observation technology and thermal-mechanical coupling simulation technology are used. Through theoretical modeling, computer simulation techniques and surface morphology experimental studies, different motion parameters are compared and analyzed to derive the optimal preparation parameters.</div></div><div><h3>Results</h3><div>One of our contributions is to control the preparation depth based on the different material removal methods, and the brittle removal methods and grinding heat during the preparation process were reduced. Another contribution is to derive the stress intensity factor (SIF) at the crack tip for different preparation directions based on multi-grit and thermal-mechanical coupling finite element model for different preparation stages. The preparation direction was directed and crack extension was minimized. Finally, the experimental system of the TPR was constructed. Based on the proposed morphology and preparation direction optimization method, the material removal method during the preparation process can be controlled in plastic removal. Crack extension was also reduced based on different stages of optimized preparation directions. Based on the guided strategy, the TPR can provide safe assisted dentists.</div></div><div><h3>Conclusions</h3><div>In this work, the preparation parameters of the cracked preparation robot were optimized to enable it to perform the preparation of hard and brittle cracked teeth. The surface morphology met the clinical requirements. Intraoral preparation will be considered in the future to advance the robot toward clinical dental applications.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"258 ","pages":"Article 108485"},"PeriodicalIF":4.9,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616240","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":"DEDUCE: Multi-head attention decoupled contrastive learning to discover cancer subtypes based on multi-omics data","authors":"Liangrui Pan ,&nbsp;Xiang Wang ,&nbsp;Qingchun Liang ,&nbsp;Jiandong Shang ,&nbsp;Wenjuan Liu ,&nbsp;Liwen Xu ,&nbsp;Shaoliang Peng","doi":"10.1016/j.cmpb.2024.108478","DOIUrl":"10.1016/j.cmpb.2024.108478","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Given the high heterogeneity and clinical diversity of cancer, substantial variations exist in multi-omics data and clinical features across different cancer subtypes.</div></div><div><h3>Methods:</h3><div>We propose a model, named DEDUCE, based on a symmetric multi-head attention encoders (SMAE), for unsupervised contrastive learning to analyze multi-omics cancer data, with the aim of identifying and characterizing cancer subtypes. This model adopts a unsupervised SMAE that can deeply extract contextual features and long-range dependencies from multi-omics data, thereby mitigating the impact of noise. Importantly, DEDUCE introduces a subtype decoupled contrastive learning method based on a multi-head attention mechanism to simultaneously learn features from multi-omics data and perform clustering for identifying cancer subtypes. Subtypes are clustered by calculating the similarity between samples in both the feature space and sample space of multi-omics data. The fundamental concept involves decoupling various attributes of multi-omics data features and learning them as contrasting terms. A contrastive loss function is constructed to quantify the disparity between positive and negative examples, and the model minimizes this difference, thereby promoting the acquisition of enhanced feature representation.</div></div><div><h3>Results:</h3><div>The DEDUCE model undergoes extensive experiments on simulated multi-omics datasets, single-cell multi-omics datasets, and cancer multi-omics datasets, outperforming 10 deep learning models. The DEDUCE model outperforms state-of-the-art methods, and ablation experiments demonstrate the effectiveness of each module in the DEDUCE model. Finally, we applied the DEDUCE model to identify six cancer subtypes of AML.</div></div><div><h3>Conclusion:</h3><div>In this paper, we proposed DEDUCE model learns features from multi-omics data through SMAE, and the subtype decoupled contrastive learning consistently optimizes the model for clustering and identifying cancer subtypes. The DEDUCE model demonstrates a significant capability in discovering new cancer subtypes. We applied the DEDUCE model to identify six subtypes of AML. Through the analysis of GO function enrichment, subtype-specific biological functions, and GSEA of AML using the DEDUCE model, the interpretability of the DEDUCE model in identifying cancer subtypes is further enhanced.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108478"},"PeriodicalIF":4.9,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587373","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":"Comparative analysis of Zero Pressure Geometry and prestress methods in cardiovascular Fluid-Structure Interaction","authors":"André Mourato ,&nbsp;Rodrigo Valente ,&nbsp;José Xavier ,&nbsp;Moisés Brito ,&nbsp;Stéphane Avril ,&nbsp;António C. Tomás ,&nbsp;José Fragata","doi":"10.1016/j.cmpb.2024.108475","DOIUrl":"10.1016/j.cmpb.2024.108475","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Modelling patient-specific aortic biomechanics with advanced computational techniques, such as Fluid–Structure Interaction (FSI), can be crucial to provide effective decision-making indices to enhance current clinical practices. To effectively simulate Ascending Thoracic Aortic Aneurysms (ATAA), the stress-free configuration must be defined. The Zero Pressure Geometry (ZPG) and the Prestress Tensor (PT) are two of the main approaches to tackle this issue. However, their impact on the numerical results is yet to be analysed. Computed Tomography Angiography (CTA) and Magnetic Resonance Imaging (MRI) data were used to develop patient-specific 2-way FSI frameworks.</div></div><div><h3>Methods:</h3><div>Three models were developed considering different tissue prestressing approaches to account for the reference configuration and their numerical results were compared. The selected approaches were: (i) ZPG, (ii) PT and (iii) a combination of the PT approach with a regional mapping of material properties (PTCAL).</div></div><div><h3>Results:</h3><div>The pressure fields estimated by all models were equivalent. The estimation of Wall Shear Stress (WSS) based metrics revealed good correspondence between all models except the Relative Residence Time (RRT). Regarding ATAA wall mechanics, the proposed extension to the PT approach presented a closer agreement with the ZPG model than its counterpart. Additionally, the PT and PTCAL approaches required around 60% fewer iterations to achieve cycle-to-cycle convergence than the ZPG algorithm.</div></div><div><h3>Conclusion:</h3><div>Using a regional mapping of material properties in combination with the PT method presented a better correspondence with the ZPG approach. The outcomes of this study can pave the way for advancing the accuracy and convergence of ATAA numerical models using the PT methodology.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108475"},"PeriodicalIF":4.9,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142581381","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":"Thrombogenic Risk Assessment of Transcatheter Prosthetic Heart Valves Using a Fluid-Structure Interaction Approach","authors":"Kyle Baylous ,&nbsp;Brandon Kovarovic ,&nbsp;Rodrigo R. Paz ,&nbsp;Salwa Anam ,&nbsp;Ryan Helbock ,&nbsp;Marc Horner ,&nbsp;Marvin Slepian ,&nbsp;Danny Bluestein","doi":"10.1016/j.cmpb.2024.108469","DOIUrl":"10.1016/j.cmpb.2024.108469","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Prosthetic heart valve interventions such as TAVR have surged over the past decade, but the associated complication of long-term, life-threatening thrombotic events continues to undermine patient outcomes. Thus, improving thrombogenic risk analysis of TAVR devices is crucial. In vitro studies for thrombogenicity are typically difficult to perform. However, revised ISO testing standards include computational testing for thrombogenic risk assessment of cardiovascular implants. We present a fluid-structure interaction (FSI) approach for assessing thrombogenic risk of transcatheter aortic valves.</div></div><div><h3>Methods</h3><div>An FSI framework was implemented via the incompressible computational fluid dynamics multi-physics solver of the ANSYS LS-DYNA software. The numerical modeling approach for flow analysis was validated by comparing the derived flow rate of the 29 mm CoreValve device from benchtop testing and orifice areas of commercial TAVR valves in the literature to in silico results. Thrombogenic risk was analyzed by computing stress accumulation (SA) on virtual platelets seeded in the flow fields via ANSYS EnSight. The integrated FSI-thrombogenicity methodology was subsequently employed to examine hemodynamics and thrombogenic risk of TAVR devices with two approaches: 1) engineering optimization and 2) clinical assessment.</div></div><div><h3>Results</h3><div>Simulated effective orifice areas for commercial valves were in reported ranges. In silico cardiac output and flow rate during the positive pressure differential period matched experimental results by approximately 93 %. The approach was used to analyze the effect of various TAVR leaflet designs on hemodynamics, where platelets experienced instantaneous stresses reaching around 10 Pa. Post-TAVR deployment hemodynamics in patient-specific bicuspid aortic valve anatomies revealed varying degrees of thrombogenic risk with the highest median SA around 70 dyn·s/cm² - nearly double the activation threshold - despite those being clinically classified as “mild” paravalvular leaks.</div></div><div><h3>Conclusions</h3><div>Our methodology can be used to improve the thromboresistance of prosthetic valves from the initial design stage to the clinic. It allows for unparalleled optimization of devices, uncovering key TAVR leaflet design parameters that can be used to mitigate thrombogenic risk, in addition to patient-specific modeling to evaluate device performance. This work demonstrates the utility of advanced in silico analysis of TAVR devices that can be utilized for thrombogenic risk assessment of other blood recirculating devices.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108469"},"PeriodicalIF":4.9,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142496563","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":"The CrowdGleason dataset: Learning the Gleason grade from crowds and experts","authors":"Miguel López-Pérez ,&nbsp;Alba Morquecho ,&nbsp;Arne Schmidt ,&nbsp;Fernando Pérez-Bueno ,&nbsp;Aurelio Martín-Castro ,&nbsp;Javier Mateos ,&nbsp;Rafael Molina","doi":"10.1016/j.cmpb.2024.108472","DOIUrl":"10.1016/j.cmpb.2024.108472","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background:&lt;/h3&gt;&lt;div&gt;Currently, prostate cancer (PCa) diagnosis relies on the human analysis of prostate biopsy Whole Slide Images (WSIs) using the Gleason score. Since this process is error-prone and time-consuming, recent advances in machine learning have promoted the use of automated systems to assist pathologists. Unfortunately, labeled datasets for training and validation are scarce due to the need for expert pathologists to provide ground-truth labels.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods:&lt;/h3&gt;&lt;div&gt;This work introduces a new prostate histopathological dataset named CrowdGleason, which consists of 19,077 patches from 1045 WSIs with various Gleason grades. The dataset was annotated using a crowdsourcing protocol involving seven pathologists-in-training to distribute the labeling effort. To provide a baseline analysis, two crowdsourcing methods based on Gaussian Processes (GPs) were evaluated for Gleason grade prediction: SVGPCR, which learns a model from the CrowdGleason dataset, and SVGPMIX, which combines data from the public dataset SICAPv2 and the CrowdGleason dataset. The performance of these methods was compared with other crowdsourcing and expert label-based methods through comprehensive experiments.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results:&lt;/h3&gt;&lt;div&gt;The results demonstrate that our GP-based crowdsourcing approach outperforms other methods for aggregating crowdsourced labels (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;κ&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;7048&lt;/mn&gt;&lt;mo&gt;±&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;0207&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) for SVGPCR vs.(&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;κ&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;6576&lt;/mn&gt;&lt;mo&gt;±&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;0086&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) for SVGP with majority voting). SVGPCR trained with crowdsourced labels performs better than GP trained with expert labels from SICAPv2 (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;κ&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;6583&lt;/mn&gt;&lt;mo&gt;±&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;0220&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) and outperforms most individual pathologists-in-training (mean &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;κ&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;5432&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;). Additionally, SVGPMIX trained with a combination of SICAPv2 and CrowdGleason achieves the highest performance on both datasets (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;κ&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;7814&lt;/mn&gt;&lt;mo&gt;±&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;0083&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; and &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;κ&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;7276&lt;/mn&gt;&lt;mo&gt;±&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;0260&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion:&lt;/h3&gt;&lt;div&gt;The experiments show that the CrowdGleason dataset can be successfully used for training and validating supervised and crowdsourcing methods. Furthermore, the crowdsourcing methods trained on this dataset obtain competitive results against those using expert labels. Interestingly, the combination of expert and non-expert labels opens the door to a future of massive labeling by incorporating both expert and non-expert pathologist an","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108472"},"PeriodicalIF":4.9,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142564128","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}