Computer methods and programs in biomedicine最新文献

Conv-RGNN: An efficient Convolutional Residual Graph Neural Network for ECG classification Conv-RGNN：用于心电图分类的高效卷积残差图神经网络

IF 4.9 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine

Pub Date : 2024-09-03 DOI: 10.1016/j.cmpb.2024.108406

Background and objective:

Electrocardiogram (ECG) analysis is crucial in diagnosing cardiovascular diseases (CVDs). It is important to consider both temporal and spatial features in ECG analysis to improve automated CVDs diagnosis. Significant progress has been made in automated CVDs diagnosis based on ECG with the continuous development of deep learning. Current most researches often treat 12-lead ECG signals as synchronous sequences in Euclidean space, focusing primarily on extracting temporal features while overlooking the spatial relationships among the 12-lead. However, the spatial distribution of 12-lead ECG electrodes can be more naturally represented using non-Euclidean data structures, which makes the relationships among leads more consistent with their intrinsic characteristics.

Methods:

This study proposes an innovative method, Convolutional Residual Graph Neural Network (Conv-RGNN), for ECG classification. The first step is to segment the 12-lead ECG into twelve single-lead ECG, which are then mapped to nodes in a graph that captures the relationships between the different leads through spatial connections, resulting in the 12-lead ECG graph. The graph is then used as input for Conv-RGNN. A convolutional neural network with a position attention mechanism is used to extract temporal sequence information and selectively integrate contextual information to enhance semantic features at different positions. The spatial features of the 12-lead ECG graph are extracted using the residual graph neural network.

Results:

The experimental results indicate that Conv-RGNN is highly competitive in two multi-label datasets and one single-label dataset, demonstrating exceptional parameter efficiency, inference speed, model performance, and robustness.

Conclusion:

The Conv-RGNN proposed in this paper offer a promising and feasible approach for intelligent diagnosis in resource-constrained environments.

背景和目的：心电图（ECG）分析是诊断心血管疾病（CVDs）的关键。在心电图分析中同时考虑时间和空间特征对于改善心血管疾病的自动诊断非常重要。随着深度学习的不断发展，基于心电图的心血管疾病自动诊断取得了重大进展。目前大多数研究通常将 12 导联心电图信号视为欧几里得空间中的同步序列，主要侧重于提取时间特征，而忽略了 12 导联之间的空间关系。然而，使用非欧几里得数据结构可以更自然地表示 12 导联心电图电极的空间分布，从而使导联之间的关系更符合其内在特征：本研究提出了一种用于心电图分类的创新方法--卷积残差图神经网络（Conv-RGNN）。第一步是将 12 导联心电图分割成 12 个单导联心电图，然后将这些单导联心电图映射到图中的节点，通过空间连接捕捉不同导联之间的关系，从而形成 12 导联心电图图。然后将该图作为 Conv-RGNN 的输入。具有位置关注机制的卷积神经网络用于提取时间序列信息，并选择性地整合上下文信息，以增强不同位置的语义特征。使用残差图神经网络提取 12 导联心电图图的空间特征：实验结果表明，Conv-RGNN 在两个多标签数据集和一个单标签数据集中具有很强的竞争力，在参数效率、推理速度、模型性能和鲁棒性方面都表现出了卓越的性能：本文提出的 Conv-RGNN 为资源受限环境下的智能诊断提供了一种前景广阔的可行方法。

{"title":"Conv-RGNN: An efficient Convolutional Residual Graph Neural Network for ECG classification","authors":"","doi":"10.1016/j.cmpb.2024.108406","DOIUrl":"10.1016/j.cmpb.2024.108406","url":null,"abstract":"<div><h3>Background and objective:</h3><p>Electrocardiogram (ECG) analysis is crucial in diagnosing cardiovascular diseases (CVDs). It is important to consider both temporal and spatial features in ECG analysis to improve automated CVDs diagnosis. Significant progress has been made in automated CVDs diagnosis based on ECG with the continuous development of deep learning. Current most researches often treat 12-lead ECG signals as synchronous sequences in Euclidean space, focusing primarily on extracting temporal features while overlooking the spatial relationships among the 12-lead. However, the spatial distribution of 12-lead ECG electrodes can be more naturally represented using non-Euclidean data structures, which makes the relationships among leads more consistent with their intrinsic characteristics.</p></div><div><h3>Methods:</h3><p>This study proposes an innovative method, Convolutional Residual Graph Neural Network (Conv-RGNN), for ECG classification. The first step is to segment the 12-lead ECG into twelve single-lead ECG, which are then mapped to nodes in a graph that captures the relationships between the different leads through spatial connections, resulting in the 12-lead ECG graph. The graph is then used as input for Conv-RGNN. A convolutional neural network with a position attention mechanism is used to extract temporal sequence information and selectively integrate contextual information to enhance semantic features at different positions. The spatial features of the 12-lead ECG graph are extracted using the residual graph neural network.</p></div><div><h3>Results:</h3><p>The experimental results indicate that Conv-RGNN is highly competitive in two multi-label datasets and one single-label dataset, demonstrating exceptional parameter efficiency, inference speed, model performance, and robustness.</p></div><div><h3>Conclusion:</h3><p>The Conv-RGNN proposed in this paper offer a promising and feasible approach for intelligent diagnosis in resource-constrained environments.</p></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142145347","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}

引用次数: 0

SleepGCN: A transition rule learning model based on Graph Convolutional Network for sleep staging SleepGCN：基于图卷积网络的睡眠分期过渡规则学习模型。

IF 4.9 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine

Pub Date : 2024-09-02 DOI: 10.1016/j.cmpb.2024.108405

Background and Objective:

Automatic sleep staging is essential for assessing and diagnosing sleep disorders, serving millions of people who suffer from them. Numerous sleep staging models have been proposed recently, but most of them have not fully explored the sleep transition rules that are essential for sleep experts to identify sleep stages. Therefore, one objective of this paper is to develop an automatic sleep staging model to capture the transition rules between sleep stages.

Methods:

In this paper, we propose a novel sleep staging model named SleepGCN. It utilizes the deep features of electroencephalogram (EEG) and electrooculogram (EOG) signals extracted by the sleep representation learning (SRL) module, in conjunction with the transition rules learned by the sleep transition rule learning (STRL) module to identify sleep stages. Specifically, the SRL module utilizes the residual network (ResNet) and Long Short Term Memory (LSTM) structure to capture the deep time-invariant features and temporal information of each sleep stage from the two-channel EEG-EOG, and then applies a feature enhancement block to obtain the refined features. The STRL module employs a Graph Convolutional Network (GCN) and a transition rule matrix to capture transition rules between sleep stages based on the sequence labels of the input signals.

Results:

We evaluate SleepGCN on five public datasets: SleepEDF-20, SleepEDF-78, SHHS, DOD-H and DOD-O. Overall, SleepGCN achieves an accuracy of 89.70%, 87.70%, 86.16%, 82.07%, and 81.20%, alongside a macro-average F1-score of 85.20%, 82.70%, 77.69%, 72.44%, and 72.93% across these datasets, respectively.

Conclusions:

The results achieved by our proposed model are much better than those of all other compared models. The ablation study validates the contributions of the SRL and STRL modules proposed in SleepGCN to the sleep staging tasks. Additionally, it shows that the sleep staging model using two-channel EEG-EOG outperforms the model using single-channel EEG or EOG. Overall, SleepGCN is an effective solution for sleep staging using two-channel EEG-EOG.

背景和目的：自动睡眠分期对于评估和诊断睡眠障碍至关重要，可为数百万睡眠障碍患者提供服务。最近提出了许多睡眠分期模型，但其中大多数都没有充分探讨睡眠过渡规则，而睡眠过渡规则对于睡眠专家识别睡眠阶段至关重要。因此，本文的目标之一就是开发一种自动睡眠分期模型，以捕捉睡眠阶段之间的过渡规则：本文提出了一种名为 SleepGCN 的新型睡眠分期模型。它利用睡眠表征学习（SRL）模块提取的脑电图（EEG）和脑电图（EOG）信号的深度特征，结合睡眠转换规则学习（STRL）模块学习到的转换规则来识别睡眠阶段。具体来说，SRL 模块利用残差网络（ResNet）和长短期记忆（LSTM）结构，从双通道脑电图-眼电图中捕捉每个睡眠阶段的深层时变特征和时间信息，然后应用特征增强模块获得细化特征。STRL模块采用图卷积网络（GCN）和过渡规则矩阵，根据输入信号的序列标签捕捉睡眠阶段之间的过渡规则：我们在五个公共数据集上对 SleepGCN 进行了评估：结果：我们在五个公开数据集上对 SleepGCN 进行了评估：SleepEDF-20、SleepEDF-78、SHHS、DOD-H 和 DOD-O。总体而言，SleepGCN 在这些数据集上的准确率分别为 89.70%、87.70%、86.16%、82.07% 和 81.20%，宏观平均 F1 分数分别为 85.20%、82.70%、77.69%、72.44% 和 72.93%：我们提出的模型所取得的结果远远优于所有其他比较模型。消融研究验证了 SleepGCN 中提出的 SRL 和 STRL 模块对睡眠分期任务的贡献。此外，研究还表明，使用双通道 EEG-EOG 的睡眠分期模型优于使用单通道 EEG 或 EOG 的模型。总之，SleepGCN 是使用双通道 EEG-EOG 进行睡眠分期的有效解决方案。

{"title":"SleepGCN: A transition rule learning model based on Graph Convolutional Network for sleep staging","authors":"","doi":"10.1016/j.cmpb.2024.108405","DOIUrl":"10.1016/j.cmpb.2024.108405","url":null,"abstract":"<div><h3>Background and Objective:</h3><p>Automatic sleep staging is essential for assessing and diagnosing sleep disorders, serving millions of people who suffer from them. Numerous sleep staging models have been proposed recently, but most of them have not fully explored the sleep transition rules that are essential for sleep experts to identify sleep stages. Therefore, one objective of this paper is to develop an automatic sleep staging model to capture the transition rules between sleep stages.</p></div><div><h3>Methods:</h3><p>In this paper, we propose a novel sleep staging model named SleepGCN. It utilizes the deep features of electroencephalogram (EEG) and electrooculogram (EOG) signals extracted by the sleep representation learning (SRL) module, in conjunction with the transition rules learned by the sleep transition rule learning (STRL) module to identify sleep stages. Specifically, the SRL module utilizes the residual network (ResNet) and Long Short Term Memory (LSTM) structure to capture the deep time-invariant features and temporal information of each sleep stage from the two-channel EEG-EOG, and then applies a feature enhancement block to obtain the refined features. The STRL module employs a Graph Convolutional Network (GCN) and a transition rule matrix to capture transition rules between sleep stages based on the sequence labels of the input signals.</p></div><div><h3>Results:</h3><p>We evaluate SleepGCN on five public datasets: SleepEDF-20, SleepEDF-78, SHHS, DOD-H and DOD-O. Overall, SleepGCN achieves an accuracy of 89.70%, 87.70%, 86.16%, 82.07%, and 81.20%, alongside a macro-average F1-score of 85.20%, 82.70%, 77.69%, 72.44%, and 72.93% across these datasets, respectively.</p></div><div><h3>Conclusions:</h3><p>The results achieved by our proposed model are much better than those of all other compared models. The ablation study validates the contributions of the SRL and STRL modules proposed in SleepGCN to the sleep staging tasks. Additionally, it shows that the sleep staging model using two-channel EEG-EOG outperforms the model using single-channel EEG or EOG. Overall, SleepGCN is an effective solution for sleep staging using two-channel EEG-EOG.</p></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142145349","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}

引用次数: 0

Barriers encountered with clinical data warehouses: Recommendations from a focus group 临床数据仓库遇到的障碍：焦点小组的建议。

IF 4.9 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine

Pub Date : 2024-09-01 DOI: 10.1016/j.cmpb.2024.108404

Background and Objective

The increasing implementation and use of electronic health records over the last few decades has made a significant volume of clinical data being available. Over the past 20 years, hospitals have also adopted and implemented data warehouse technology to facilitate the reuse of administrative and clinical data for research. However, the implementation of clinical data warehouses encounters a set of barriers: ethical, legislative, technical, human and organizational. This paper proposes an overview of difficulties and barriers encountered during a clinical data warehouse (CDW) development and implementation project.

Methods

We conducted a focus group at the 2023 Medical Informatics Europe Conference and invited professionals involved in the implementation of CDW. These experts described their CDW and the difficulties and barriers they encountered at each phase: (i) launching of the data warehouse project, (ii) implementing the data warehouse and (iii) using a data warehouse in routine operations. They were also asked to propose solutions they were able to implement to address the barriers previously reported.

Results

After synthesis and consensus, a total of 26 barriers were identified, 10 pertained to tasks, 5 to tools and technologies, 4 to persons, 4 to organization, and 3 to the external environment. To address these challenges, a set of 15 practical recommendations was offered, covering essential aspects such as governance, stakeholder engagement, interdisciplinary collaboration, and external expertise utilization.

Conclusions

These recommendations serve as a valuable resource for healthcare institutions seeking to establish and optimize CDWs, offering a roadmap for leveraging clinical data for research, quality enhancement, and improved patient care.

背景和目的：过去几十年来，电子健康记录的实施和使用日益增多，使得大量临床数据可供使用。在过去 20 年中，医院也采用并实施了数据仓库技术，以促进行政和临床数据在研究中的再利用。然而，临床数据仓库的实施遇到了一系列障碍：伦理、法律、技术、人力和组织方面的障碍。本文概述了临床数据仓库（CDW）开发和实施项目过程中遇到的困难和障碍：方法：我们在 2023 年欧洲医学信息学大会上进行了一次焦点小组讨论，邀请了参与临床数据仓库实施的专业人士。这些专家介绍了他们的临床数据仓库，以及他们在以下各个阶段遇到的困难和障碍：(i) 启动数据仓库项目，(ii) 实施数据仓库，(iii) 在日常操作中使用数据仓库。此外，还要求他们提出能够实施的解决方案，以解决之前报告的障碍：结果：经过综合和协商一致，共确定了 26 个障碍，其中 10 个与任务有关，5 个与工具和技术有关，4 个与人员有关，4 个与组织有关，3 个与外部环境有关。为了应对这些挑战，我们提出了一套 15 项实用建议，涵盖了治理、利益相关者参与、跨学科合作和外部专家利用等重要方面：这些建议是医疗机构建立和优化临床数据中心的宝贵资源，为利用临床数据开展研究、提高质量和改善患者护理提供了路线图。

{"title":"Barriers encountered with clinical data warehouses: Recommendations from a focus group","authors":"","doi":"10.1016/j.cmpb.2024.108404","DOIUrl":"10.1016/j.cmpb.2024.108404","url":null,"abstract":"<div><h3>Background and Objective</h3><p>The increasing implementation and use of electronic health records over the last few decades has made a significant volume of clinical data being available. Over the past 20 years, hospitals have also adopted and implemented data warehouse technology to facilitate the reuse of administrative and clinical data for research. However, the implementation of clinical data warehouses encounters a set of barriers: ethical, legislative, technical, human and organizational. This paper proposes an overview of difficulties and barriers encountered during a clinical data warehouse (CDW) development and implementation project.</p></div><div><h3>Methods</h3><p>We conducted a focus group at the 2023 Medical Informatics Europe Conference and invited professionals involved in the implementation of CDW. These experts described their CDW and the difficulties and barriers they encountered at each phase: (i) launching of the data warehouse project, (ii) implementing the data warehouse and (iii) using a data warehouse in routine operations. They were also asked to propose solutions they were able to implement to address the barriers previously reported.</p></div><div><h3>Results</h3><p>After synthesis and consensus, a total of 26 barriers were identified, 10 pertained to tasks, 5 to tools and technologies, 4 to persons, 4 to organization, and 3 to the external environment. To address these challenges, a set of 15 practical recommendations was offered, covering essential aspects such as governance, stakeholder engagement, interdisciplinary collaboration, and external expertise utilization.</p></div><div><h3>Conclusions</h3><p>These recommendations serve as a valuable resource for healthcare institutions seeking to establish and optimize CDWs, offering a roadmap for leveraging clinical data for research, quality enhancement, and improved patient care.</p></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142145346","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}

引用次数: 0

Prediction of mortality events of patients with acute heart failure in intensive care unit based on deep neural network 基于深度神经网络的重症监护室急性心力衰竭患者死亡事件预测

IF 4.9 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine

Pub Date : 2024-08-30 DOI: 10.1016/j.cmpb.2024.108403

Background

Acute heart failure (AHF) in the intensive care unit (ICU) is characterized by its criticality, rapid progression, complex and changeable condition, and its pathophysiological process involves the interaction of multiple organs and systems. This makes it difficult to predict in-hospital mortality events comprehensively and accurately. Traditional analysis methods based on statistics and machine learning suffer from insufficient model performance, poor accuracy caused by prior dependence, and difficulty in adequately considering the complex relationships between multiple risk factors. Therefore, the application of deep neural network (DNN) techniques to the specific scenario, predicting mortality events of patients with AHF under intensive care, has become a research frontier.

Methods

This research utilized the MIMIC-IV critical care database as the primary data source and employed the synthetic minority over-sampling technique (SMOTE) to balance the dataset. Deep neural network models—backpropagation neural network (BPNN) and recurrent neural network (RNN), which are based on electronic medical record data mining, were employed to investigate the in-hospital death event judgment task of patients with AHF under intensive care. Additionally, multiple single machine learning models and ensemble learning models were constructed for comparative experiments. Moreover, we achieved various optimal performance combinations by modifying the classification threshold of deep neural network models to address the diverse real-world requirements in the ICU. Finally, we conducted an interpretable deep model using SHapley Additive exPlanations (SHAP) to uncover the most influential medical record features for each patient from the aspects of global and local interpretation.

Results

In terms of model performance in this scenario, deep neural network models outperform both single machine learning models and ensemble learning models, achieving the highest Accuracy, Precision, Recall, F1 value, and Area under the ROC curve, which can reach 0.949, 0.925, 0.983, 0.953, and 0.987 respectively. SHAP value analysis revealed that the ICU scores (APSIII, OASIS, SOFA) are significantly correlated with the occurrence of in-hospital fatal events.

Conclusions

Our study underscores that DNN-based mortality event classifier offers a novel intelligent approach for forecasting and assessing the prognosis of AHF patients in the ICU. Additionally, the ICU scores stand out as the most predictive features, which implies that in the decision-making process of the models, ICU scores can provide the most crucial information, making the greatest positive or negative contribution to influence the incidence of in-hospital mortality among patients with acute heart failure.

背景重症监护病房（ICU）中的急性心力衰竭（AHF）具有病情危重、进展迅速、复杂多变的特点，其病理生理过程涉及多个器官和系统的相互作用。因此很难全面准确地预测院内死亡事件。传统的基于统计学和机器学习的分析方法存在模型性能不足、先验依赖性导致准确性差、难以充分考虑多种危险因素之间的复杂关系等问题。因此，将深度神经网络（DNN）技术应用于特定场景，预测重症监护下 AHF 患者的死亡事件已成为研究前沿。采用基于电子病历数据挖掘的深度神经网络模型--背向传播神经网络（BPNN）和递归神经网络（RNN），研究重症监护下AHF患者的院内死亡事件判断任务。此外，我们还构建了多个单一机器学习模型和集合学习模型进行对比实验。此外，我们还通过修改深度神经网络模型的分类阈值实现了各种最优性能组合，以满足重症监护室的不同实际需求。最后，我们利用 SHapley Additive exPlanations（SHAP）建立了一个可解释的深度模型，从全局和局部解释的角度挖掘出对每位患者最有影响力的病历特征。结果在该场景下，深度神经网络模型的性能优于单一机器学习模型和集合学习模型，获得了最高的准确率、精确率、召回率、F1 值和 ROC 曲线下面积，分别可达 0.949、0.925、0.983、0.953 和 0.987。SHAP值分析表明，ICU评分（APSIII、OASIS、SOFA）与院内死亡事件的发生显著相关。此外，ICU 评分是最具预测性的特征，这意味着在模型的决策过程中，ICU 评分能提供最关键的信息，对影响急性心力衰竭患者的院内死亡率做出最大的积极或消极贡献。

{"title":"Prediction of mortality events of patients with acute heart failure in intensive care unit based on deep neural network","authors":"","doi":"10.1016/j.cmpb.2024.108403","DOIUrl":"10.1016/j.cmpb.2024.108403","url":null,"abstract":"<div><h3>Background</h3><p>Acute heart failure (AHF) in the intensive care unit (ICU) is characterized by its criticality, rapid progression, complex and changeable condition, and its pathophysiological process involves the interaction of multiple organs and systems. This makes it difficult to predict in-hospital mortality events comprehensively and accurately. Traditional analysis methods based on statistics and machine learning suffer from insufficient model performance, poor accuracy caused by prior dependence, and difficulty in adequately considering the complex relationships between multiple risk factors. Therefore, the application of deep neural network (DNN) techniques to the specific scenario, predicting mortality events of patients with AHF under intensive care, has become a research frontier.</p></div><div><h3>Methods</h3><p>This research utilized the MIMIC-IV critical care database as the primary data source and employed the synthetic minority over-sampling technique (SMOTE) to balance the dataset. Deep neural network models—backpropagation neural network (BPNN) and recurrent neural network (RNN), which are based on electronic medical record data mining, were employed to investigate the in-hospital death event judgment task of patients with AHF under intensive care. Additionally, multiple single machine learning models and ensemble learning models were constructed for comparative experiments. Moreover, we achieved various optimal performance combinations by modifying the classification threshold of deep neural network models to address the diverse real-world requirements in the ICU. Finally, we conducted an interpretable deep model using SHapley Additive exPlanations (SHAP) to uncover the most influential medical record features for each patient from the aspects of global and local interpretation.</p></div><div><h3>Results</h3><p>In terms of model performance in this scenario, deep neural network models outperform both single machine learning models and ensemble learning models, achieving the highest Accuracy, Precision, Recall, F1 value, and Area under the ROC curve, which can reach 0.949, 0.925, 0.983, 0.953, and 0.987 respectively. SHAP value analysis revealed that the ICU scores (APSIII, OASIS, SOFA) are significantly correlated with the occurrence of in-hospital fatal events.</p></div><div><h3>Conclusions</h3><p>Our study underscores that DNN-based mortality event classifier offers a novel intelligent approach for forecasting and assessing the prognosis of AHF patients in the ICU. Additionally, the ICU scores stand out as the most predictive features, which implies that in the decision-making process of the models, ICU scores can provide the most crucial information, making the greatest positive or negative contribution to influence the incidence of in-hospital mortality among patients with acute heart failure.</p></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169260724003961/pdfft?md5=603e2fe58aafff457c4aa49d7025c8c7&pid=1-s2.0-S0169260724003961-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137113","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}

引用次数: 0

Applications of genome-scale metabolic models to the study of human diseases: A systematic review 基因组尺度代谢模型在人类疾病研究中的应用：系统综述

IF 4.9 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine

Pub Date : 2024-08-29 DOI: 10.1016/j.cmpb.2024.108397

Background and Objectives:

Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases.

Methods:

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined.

Results:

The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models.

Conclusions:

The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases.

背景与目的：基因组尺度代谢网络（GEM）是系统生物学广泛领域中一种宝贵的建模和计算工具。它能够整合约束条件和高通量生物数据，有助于研究不同细胞类型和条件下错综复杂的代谢问题和过程。在过去的十年中，GEMs 在人类疾病研究中的应用越来越多，种类也越来越丰富，同时在重建、整合和分析大量生物体方面也做出了巨大努力。本文对科学文献进行了系统回顾，探讨了基于约束的建模在人类疾病研究中的应用的几个重要问题。希望本文能为那些对应用建模和计算工具研究代谢相关人类疾病感兴趣的研究人员提供有用的参考。方法：本系统综述是根据系统综述和元分析首选报告项目（PRISMA）指南进行的。查询了 Elsevier Scopus®、美国国家医学图书馆 PubMed® 和 Clarivate Web of Science™ 数据库，共获得 566 篇科学文章。结果：综述论文全面介绍了基于基因组尺度代谢模型的最新建模和计算方法，可用于研究多种人类疾病。众多研究根据所研究疾病涉及的临床研究领域进行了分类。结论：利用基于 GEM 的方法研究人类疾病的科学论文数量表明，人们对这类方法的兴趣与日俱增；希望本综述能为有意应用计算建模方法研究人类疾病病因病理学的科学家提供有用的参考；我们还希望这项工作能促进新型应用和方法的开发，以发现代谢相关疾病的临床相关见解。

{"title":"Applications of genome-scale metabolic models to the study of human diseases: A systematic review","authors":"","doi":"10.1016/j.cmpb.2024.108397","DOIUrl":"10.1016/j.cmpb.2024.108397","url":null,"abstract":"<div><h3>Background and Objectives:</h3><p>Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases.</p></div><div><h3>Methods:</h3><p>This systematic review was conducted according to the <em>Preferred Reporting Items for Systematic Reviews and Meta-Analyses</em> (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined.</p></div><div><h3>Results:</h3><p>The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models.</p></div><div><h3>Conclusions:</h3><p>The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases.</p></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169260724003900/pdfft?md5=84ba91d2da746a463bbd24ee7f1ef111&pid=1-s2.0-S0169260724003900-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129475","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}

引用次数: 0

Longitudinal registration of thoracic CT images with radiation-induced lung diseases: A divide-and-conquer approach based on component structure wise registration using coherent point drift 胸部 CT 图像与辐射诱发肺部疾病的纵向配准：基于分量结构的分而治之法，利用相干点漂移进行明智配准

IF 4.9 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine

Pub Date : 2024-08-28 DOI: 10.1016/j.cmpb.2024.108401

Background and Objective

Registration of pulmonary computed tomography (CT) images with radiation-induced lung diseases (RILD) was essential to investigate the voxel-wise relationship between the formation of RILD and the radiation dose received by different tissues. Although various approaches had been developed for the registration of lung CTs, their performances remained clinically unsatisfactory for registration of lung CT images with RILD. The main difficulties arose from the longitudinal change in lung parenchyma, including RILD and volumetric change of lung cancers, after radiation therapy, leading to inaccurate registration and artifacts caused by erroneous matching of the RILD tissues.

Methods

To overcome the influence of the parenchymal changes, a divide-and-conquer approach rooted in the coherent point drift (CPD) paradigm was proposed. The proposed method was based on two kernel ideas. One was the idea of component structure wise registration. Specifically, the proposed method relaxed the intrinsic assumption of equal isotropic covariances in CPD by decomposing a lung and its surrounding tissues into component structures and independently registering the component structures pairwise by CPD. The other was the idea of defining a vascular subtree centered at a matched branch point as a component structure. This idea could not only provide a sufficient number of matched feature points within a parenchyma, but avoid being corrupted by the false feature points resided in the RILD tissues due to globally and indiscriminately sampling using mathematical operators. The overall deformation model was built by using the Thin Plate Spline based on all matched points.

Results

This study recruited 30 pairs of lung CT images with RILD, 15 of which were used for internal validation (leave-one-out cross-validation) and the other 15 for external validation. The experimental results showed that the proposed algorithm achieved a mean and a mean of maximum 1 % of average surface distances <2 and 8 mm, respectively, and a mean and a maximum target registration error <2 mm and 5 mm on both internal and external validation datasets. The paired two-sample t-tests corroborated that the proposed algorithm outperformed a recent method, the Stavropoulou's method, on the external validation dataset (p < 0.05).

Conclusions

The proposed algorithm effectively reduced the influence of parenchymal changes, resulting in a reasonably accurate and artifact-free registration.

背景和目的对肺部计算机断层扫描（CT）图像与辐射诱发的肺部疾病（RILD）进行配准，对于研究 RILD 的形成与不同组织所受辐射剂量之间的体素关系至关重要。尽管已开发出多种肺部 CT 图像配准方法，但在临床上，这些方法在配准有 RILD 的肺部 CT 图像时的表现仍不尽如人意。主要的困难来自于放疗后肺实质的纵向变化，包括 RILD 和肺癌的体积变化，从而导致 RILD 组织匹配错误造成的配准不准确和伪影。该方法基于两个核心思想。其一是成分结构明智配准的思想。具体来说，该方法通过将肺及其周围组织分解为组件结构，并通过 CPD 对组件结构进行独立配对，放宽了 CPD 中各向同性协方差相等的固有假设。另一种方法是将以匹配分支点为中心的血管子树定义为一个成分结构。这一想法不仅能在实质组织内提供足够数量的匹配特征点，还能避免因使用数学运算符进行全局无差别采样而被 RILD 组织中的虚假特征点所干扰。结果本研究共收集了 30 对带有 RILD 的肺部 CT 图像，其中 15 对用于内部验证（leave-one-out cross-validation），另外 15 对用于外部验证。实验结果表明，所提出的算法在内部和外部验证数据集上的平均表面距离分别为 2 毫米和 8 毫米，平均和最大目标配准误差分别为 2 毫米和 5 毫米。成对双样本 t 检验证实，在外部验证数据集上，所提出的算法优于最近的一种方法，即 Stavropoulou 方法（p < 0.05）。

{"title":"Longitudinal registration of thoracic CT images with radiation-induced lung diseases: A divide-and-conquer approach based on component structure wise registration using coherent point drift","authors":"","doi":"10.1016/j.cmpb.2024.108401","DOIUrl":"10.1016/j.cmpb.2024.108401","url":null,"abstract":"<div><h3>Background and Objective</h3><p>Registration of pulmonary computed tomography (CT) images with radiation-induced lung diseases (RILD) was essential to investigate the voxel-wise relationship between the formation of RILD and the radiation dose received by different tissues. Although various approaches had been developed for the registration of lung CTs, their performances remained clinically unsatisfactory for registration of lung CT images with RILD. The main difficulties arose from the longitudinal change in lung parenchyma, including RILD and volumetric change of lung cancers, after radiation therapy, leading to inaccurate registration and artifacts caused by erroneous matching of the RILD tissues.</p></div><div><h3>Methods</h3><p>To overcome the influence of the parenchymal changes, a divide-and-conquer approach rooted in the coherent point drift (CPD) paradigm was proposed. The proposed method was based on two kernel ideas. One was the idea of component structure wise registration. Specifically, the proposed method relaxed the intrinsic assumption of equal isotropic covariances in CPD by decomposing a lung and its surrounding tissues into component structures and independently registering the component structures pairwise by CPD. The other was the idea of defining a vascular subtree centered at a matched branch point as a component structure. This idea could not only provide a sufficient number of matched feature points within a parenchyma, but avoid being corrupted by the false feature points resided in the RILD tissues due to globally and indiscriminately sampling using mathematical operators. The overall deformation model was built by using the Thin Plate Spline based on all matched points.</p></div><div><h3>Results</h3><p>This study recruited 30 pairs of lung CT images with RILD, 15 of which were used for internal validation (leave-one-out cross-validation) and the other 15 for external validation. The experimental results showed that the proposed algorithm achieved a mean and a mean of maximum 1 % of average surface distances <2 and 8 mm, respectively, and a mean and a maximum target registration error <2 mm and 5 mm on both internal and external validation datasets. The paired two-sample <em>t</em>-tests corroborated that the proposed algorithm outperformed a recent method, the Stavropoulou's method, on the external validation dataset (<em>p</em> < 0.05).</p></div><div><h3>Conclusions</h3><p>The proposed algorithm effectively reduced the influence of parenchymal changes, resulting in a reasonably accurate and artifact-free registration.</p></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129577","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}

引用次数: 0

Myo-regressor Deep Informed Neural NetwOrk (Myo-DINO) for fast MR parameters mapping in neuromuscular disorders 用于快速绘制神经肌肉疾病磁共振参数图的肌调节器深度信息神经网络（Myo-DINO）

IF 4.9 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine

Pub Date : 2024-08-28 DOI: 10.1016/j.cmpb.2024.108399

<div><p>Magnetic Resonance (MR) parameters mapping in muscle Magnetic Resonance Imaging (mMRI) is predominantly performed using pattern recognition-based algorithms, which are characterised by high computational costs and scalability issues in the context of multi-parametric mapping.</p><p>Deep Learning (DL) has been demonstrated to be a robust and efficient method for rapid MR parameters mapping. However, its application in mMRI domain to investigate Neuromuscular Disorders (NMDs) has not yet been explored. In addition, data-driven DL models suffered in interpretation and explainability of the learning process. We developed a Physics Informed Neural Network called Myo-Regressor Deep Informed Neural NetwOrk (Myo-DINO) for efficient and explainable Fat Fraction (FF), water-T<sub>2</sub> (wT<sub>2</sub>) and B1 mapping from a cohort of NMDs.A total of 2165 slices (232 subjects) from Multi-Echo Spin Echo (MESE) images were selected as the input dataset for which FF, wT<sub>2</sub>,B1 ground truth maps were computed using the MyoQMRI toolbox. This toolbox exploits the Extended Phase Graph (EPG) theory with a two-component model (water and fat signal) and slice profile to simulate the signal evolution in the MESE framework. A customized U-Net architecture was implemented as the Myo-DINO architecture. The squared L<sub>2</sub> norm loss was complemented by two distinct physics models to define two ‘Physics-Informed’ loss functions: <em>Cycling Loss 1</em> embedded a mono-exponential model to describe the relaxation of water protons, while <em>Cycling Loss 2</em> incorporated the EPG theory with slice profile to model the magnetization dephasing under the effect of gradients and RF pulses. The Myo-DINO was trained with the hyperparameter value of the 'Physics-Informed' component held constant, i.e. λ<sub>model</sub> = 1, while different hyperparameter values (λ<sub>cnn</sub>) were applied to the squared L<sub>2</sub> norm component in both the cycling loss. In particular, hard (λ<sub>cnn</sub>=10), normal (λ<sub>cnn</sub>=1) and self-supervised (λ<sub>cnn</sub>=0) constraints were applied to gradually decrease the impact of the squared L<sub>2</sub> norm component on the ‘Physics Informed’ term during the Myo-DINO training process.</p><p>Myo-DINO achieved higher performance with <em>Cycling Loss 2</em> for FF, wT<sub>2</sub> and B1 prediction. In particular, high reconstruction similarity and quality (Structural Similarity Index > 0.92, Peak Signal to Noise ratio > 30.0 db) and small reconstruction error (Normalized Root Mean Squared Error < 0.038) to the reference maps were shown with self-supervised weighting of the <em>Cycling Loss 2</em>. In addition muscle-wise FF, wT<sub>2</sub> and B1 predicted values showed good agreement with the reference values. The Myo-DINO has been demonstrated to be a robust and efficient workflow for MR parameters mapping in the context of mMRI. This provides preliminary evidence that it can be an effective alter

肌肉磁共振成像（mMRI）中的磁共振（MR）参数映射主要是使用基于模式识别的算法进行的，这些算法的特点是计算成本高，而且在多参数映射中存在可扩展性问题。然而，深度学习在 mMRI 领域用于研究神经肌肉疾病（NMD）的应用尚未得到探索。此外，数据驱动的 DL 模型在学习过程的解释性和可解释性方面存在缺陷。我们开发了一种名为 "肌回归深度神经网络"（Myo-Regressor Deep Informed Neural NetwOrk，Myo-DINO）的物理信息神经网络，用于从一组 NMDs 患者中高效、可解释的脂肪分数（FF）、水-T2（wT2）和 B1 映射。我们从多回波自旋回波（MESE）图像中选取了总共 2165 张切片（232 名受试者）作为输入数据集，并使用 MyoQMRI 工具箱计算了 FF、wT2、B1 地面真值映射。该工具箱利用扩展相位图（EPG）理论和双分量模型（水和脂肪信号）以及切片轮廓来模拟 MESE 框架中的信号演变。定制的 U-Net 架构作为 Myo-DINO 架构得以实现。平方 L2 常模损失由两个不同的物理模型补充，以定义两个 "物理信息 "损失函数：Cycling Loss 1 嵌入了单指数模型来描述水质子的弛豫，而 Cycling Loss 2 则结合了带有切片轮廓的 EPG 理论来模拟梯度和射频脉冲作用下的磁化消相。在训练 Myo-DINO 时，"物理信息 "分量的超参数值保持不变，即 λmodel = 1，而两个循环损失中的平方 L2 准则分量采用了不同的超参数值（λcnn）。特别是，在 Myo-DINO 训练过程中，应用了硬约束（λcnn=10）、正常约束（λcnn=1）和自我监督约束（λcnn=0），以逐渐减少 L2 准则平方分量对 "物理信息 "项的影响。特别是，在循环损失 2 的自我监督加权下，与参考图相比，重建相似度和质量高（结构相似度指数为 0.92，峰值信噪比为 30.0 db），重建误差小（归一化均方根误差为 0.038）。此外，肌肉方面的 FF、wT2 和 B1 预测值与参考值显示出良好的一致性。事实证明，Myo-DINO 是在 mMRI 背景下绘制 MR 参数图的稳健而高效的工作流程。这初步证明它可以有效替代参考后处理算法。此外，我们的研究结果表明，在这项多参数回归任务中，结合了扩展相位图（EPG）模型的循环损失 2 为 Myo-DINO 提供了最稳健、最相关的物理约束。使用带有自我监督约束的 Cycling Loss 2 提高了学习过程的可解释性，因为网络完全是根据 EPG 模型的假设获得领域知识的。

{"title":"Myo-regressor Deep Informed Neural NetwOrk (Myo-DINO) for fast MR parameters mapping in neuromuscular disorders","authors":"","doi":"10.1016/j.cmpb.2024.108399","DOIUrl":"10.1016/j.cmpb.2024.108399","url":null,"abstract":"<div><p>Magnetic Resonance (MR) parameters mapping in muscle Magnetic Resonance Imaging (mMRI) is predominantly performed using pattern recognition-based algorithms, which are characterised by high computational costs and scalability issues in the context of multi-parametric mapping.</p><p>Deep Learning (DL) has been demonstrated to be a robust and efficient method for rapid MR parameters mapping. However, its application in mMRI domain to investigate Neuromuscular Disorders (NMDs) has not yet been explored. In addition, data-driven DL models suffered in interpretation and explainability of the learning process. We developed a Physics Informed Neural Network called Myo-Regressor Deep Informed Neural NetwOrk (Myo-DINO) for efficient and explainable Fat Fraction (FF), water-T<sub>2</sub> (wT<sub>2</sub>) and B1 mapping from a cohort of NMDs.A total of 2165 slices (232 subjects) from Multi-Echo Spin Echo (MESE) images were selected as the input dataset for which FF, wT<sub>2</sub>,B1 ground truth maps were computed using the MyoQMRI toolbox. This toolbox exploits the Extended Phase Graph (EPG) theory with a two-component model (water and fat signal) and slice profile to simulate the signal evolution in the MESE framework. A customized U-Net architecture was implemented as the Myo-DINO architecture. The squared L<sub>2</sub> norm loss was complemented by two distinct physics models to define two ‘Physics-Informed’ loss functions: <em>Cycling Loss 1</em> embedded a mono-exponential model to describe the relaxation of water protons, while <em>Cycling Loss 2</em> incorporated the EPG theory with slice profile to model the magnetization dephasing under the effect of gradients and RF pulses. The Myo-DINO was trained with the hyperparameter value of the 'Physics-Informed' component held constant, i.e. λ<sub>model</sub> = 1, while different hyperparameter values (λ<sub>cnn</sub>) were applied to the squared L<sub>2</sub> norm component in both the cycling loss. In particular, hard (λ<sub>cnn</sub>=10), normal (λ<sub>cnn</sub>=1) and self-supervised (λ<sub>cnn</sub>=0) constraints were applied to gradually decrease the impact of the squared L<sub>2</sub> norm component on the ‘Physics Informed’ term during the Myo-DINO training process.</p><p>Myo-DINO achieved higher performance with <em>Cycling Loss 2</em> for FF, wT<sub>2</sub> and B1 prediction. In particular, high reconstruction similarity and quality (Structural Similarity Index > 0.92, Peak Signal to Noise ratio > 30.0 db) and small reconstruction error (Normalized Root Mean Squared Error < 0.038) to the reference maps were shown with self-supervised weighting of the <em>Cycling Loss 2</em>. In addition muscle-wise FF, wT<sub>2</sub> and B1 predicted values showed good agreement with the reference values. The Myo-DINO has been demonstrated to be a robust and efficient workflow for MR parameters mapping in the context of mMRI. This provides preliminary evidence that it can be an effective alter","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169260724003924/pdfft?md5=8ce4157dd71dac18b8abf08374f3fc22&pid=1-s2.0-S0169260724003924-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137111","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}

引用次数: 0

Machine learning approach to investigate pregnancy and childbirth risk factors of sleep problems in early adolescence: Evidence from two cohort studies 用机器学习方法调查青少年早期睡眠问题的怀孕和分娩风险因素：来自两项队列研究的证据

IF 4.9 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine

Pub Date : 2024-08-28 DOI: 10.1016/j.cmpb.2024.108402

Background

This study aimed to predict early adolescent sleep problems using pregnancy and childbirth risk factors through machine learning algorithms, and to evaluate model performance internally and externally.

Methods

Data from the China Jintan Child Cohort study (CJCC; n=848) for model development and the US Healthy Brain and Behavior Study (HBBS; n=454) for external validation were employed. Maternal pregnancy histories, obstetric data, and adolescent sleep problems were collected. Several machine learning techniques were employed, including least absolute shrinkage and selection operator, logistic regression, random forest, naïve bayes, extreme gradient boosting, decision tree, and neural network. The area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, and root mean square of residuals were used to evaluate model performance.

Results

Key predictors for CJCC adolescents’ sleep problems include gestational age, birthweight, duration of delivery, and maternal happiness during pregnancy. In HBBS adolescents, the duration of postnatal depressive emotions was the primary perinatal predictor. The prediction models developed in the CJCC had good-to-excellent internal validation performance but poor performance in predicting the sleep problems in HBBS adolescents.

Conclusion

The identification of specific perinatal risk factors associated with adolescent sleep problems can inform targeted interventions during and after pregnancy to mitigate these risks. Health providers should consider integrating these predictive factors into routine pre- and postnatal assessments to identify at-risk populations. The variability in model performance across different cohorts highlights the need for context-specific models and the cautious application of predictive analytics across diverse populations. Future research should focus on refining predictive models to account for such variations, potentially through the incorporation of additional socio-cultural factors and genetic markers. This study emphasizes the importance of personalized and culturally sensitive approaches in the prediction and management of adolescent sleep problems, leveraging advanced computational methods to enhance maternal and child health outcomes.

背景本研究旨在通过机器学习算法，利用妊娠和分娩风险因素预测青少年早期睡眠问题，并对模型的内部和外部性能进行评估。方法采用中国金坛儿童队列研究（CJCC；n=848）的数据进行模型开发，并采用美国健康脑与行为研究（HBBS；n=454）的数据进行外部验证。研究收集了母亲的怀孕史、产科数据和青少年的睡眠问题。研究采用了多种机器学习技术，包括最小绝对收缩和选择算子、逻辑回归、随机森林、天真贝叶斯、极梯度提升、决策树和神经网络。结果 CJCC 青少年睡眠问题的主要预测因素包括胎龄、出生体重、分娩时间和孕期母亲的幸福感。在 HBBS 青少年中，产后抑郁情绪持续时间是围产期的主要预测因素。结论识别与青少年睡眠问题相关的特定围产期风险因素可为孕期和产后有针对性的干预措施提供依据，以降低这些风险。医疗服务提供者应考虑将这些预测因素纳入常规产前和产后评估，以识别高危人群。模型在不同人群中的表现存在差异，这凸显了针对具体情况建立模型的必要性，以及在不同人群中谨慎应用预测分析的必要性。未来的研究应侧重于完善预测模型，以考虑到这些差异，可能通过纳入更多的社会文化因素和遗传标记。这项研究强调了在青少年睡眠问题的预测和管理中采用个性化和文化敏感方法的重要性，利用先进的计算方法提高母婴健康水平。

{"title":"Machine learning approach to investigate pregnancy and childbirth risk factors of sleep problems in early adolescence: Evidence from two cohort studies","authors":"","doi":"10.1016/j.cmpb.2024.108402","DOIUrl":"10.1016/j.cmpb.2024.108402","url":null,"abstract":"<div><h3>Background</h3><p>This study aimed to predict early adolescent sleep problems using pregnancy and childbirth risk factors through machine learning algorithms, and to evaluate model performance internally and externally.</p></div><div><h3>Methods</h3><p>Data from the China Jintan Child Cohort study (CJCC; n=848) for model development and the US Healthy Brain and Behavior Study (HBBS; n=454) for external validation were employed. Maternal pregnancy histories, obstetric data, and adolescent sleep problems were collected. Several machine learning techniques were employed, including least absolute shrinkage and selection operator, logistic regression, random forest, naïve bayes, extreme gradient boosting, decision tree, and neural network. The area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, and root mean square of residuals were used to evaluate model performance.</p></div><div><h3>Results</h3><p>Key predictors for CJCC adolescents’ sleep problems include gestational age, birthweight, duration of delivery, and maternal happiness during pregnancy. In HBBS adolescents, the duration of postnatal depressive emotions was the primary perinatal predictor. The prediction models developed in the CJCC had good-to-excellent internal validation performance but poor performance in predicting the sleep problems in HBBS adolescents.</p></div><div><h3>Conclusion</h3><p>The identification of specific perinatal risk factors associated with adolescent sleep problems can inform targeted interventions during and after pregnancy to mitigate these risks. Health providers should consider integrating these predictive factors into routine pre- and postnatal assessments to identify at-risk populations. The variability in model performance across different cohorts highlights the need for context-specific models and the cautious application of predictive analytics across diverse populations. Future research should focus on refining predictive models to account for such variations, potentially through the incorporation of additional socio-cultural factors and genetic markers. This study emphasizes the importance of personalized and culturally sensitive approaches in the prediction and management of adolescent sleep problems, leveraging advanced computational methods to enhance maternal and child health outcomes.</p></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142121788","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}

引用次数: 0

Beyond pixel: Superpixel-based MRI segmentation through traditional machine learning and graph convolutional network 超越像素：通过传统机器学习和图卷积网络进行基于超像素的磁共振成像分割

IF 4.9 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine

Pub Date : 2024-08-28 DOI: 10.1016/j.cmpb.2024.108398

Background and Objective:

Tendon segmentation is crucial for studying tendon-related pathologies like tendinopathy, tendinosis, etc. This step further enables detailed analysis of specific tendon regions using automated or semi-automated methods. This study specifically aims at the segmentation of Achilles tendon, the largest tendon in the human body.

Methods:

This study proposes a comprehensive end-to-end tendon segmentation module composed of a preliminary superpixel-based coarse segmentation preceding the final segmentation task. The final segmentation results are obtained through two distinct approaches. In the first approach, the coarsely generated superpixels are subjected to classification using Random Forest (RF) and Support Vector Machine (SVM) classifiers to classify whether each superpixel belongs to a tendon class or not (resulting in tendon segmentation). In the second approach, the arrangements of superpixels are converted to graphs instead of being treated as conventional image grids. This classification process uses a graph-based convolutional network (GCN) to determine whether each superpixel corresponds to a tendon class or not.

Results:

All experiments are conducted on a custom-made ankle MRI dataset. The dataset comprises 76 subjects and is divided into two sets: one for training (Dataset 1, trained and evaluated using leave-one-group-out cross-validation) and the other as unseen test data (Dataset 2). Using our first approach, the final test AUC (Area Under the ROC Curve) scores using RF and SVM classifiers on the test data (Dataset 2) are 0.992 and 0.987, respectively, with sensitivities of 0.904 and 0.966. On the other hand, using our second approach (GCN-based node classification), the AUC score for the test set is 0.933 with a sensitivity of 0.899.

Conclusions:

Our proposed pipeline demonstrates the efficacy of employing superpixel generation as a coarse segmentation technique for the final tendon segmentation. Whether utilizing RF, SVM-based superpixel classification, or GCN-based classification for tendon segmentation, our system consistently achieves commendable AUC scores, especially the non-graph-based approach. Given the limited dataset, our graph-based method did not perform as well as non-graph-based superpixel classifications; however, the results obtained provide valuable insights into how well the models can distinguish between tendons and non-tendons. This opens up opportunities for further exploration and improvement.

背景和目的：肌腱分割对于研究肌腱相关病症（如肌腱病、肌腱变性等）至关重要。通过这一步骤，可以使用自动化或半自动化方法进一步对特定肌腱区域进行详细分析。方法：本研究提出了一个全面的端到端肌腱分割模块，由基于超像素的初步粗分割和最终分割任务组成。最终的分割结果通过两种不同的方法获得。在第一种方法中，使用随机森林（RF）和支持向量机（SVM）分类器对粗略生成的超像素进行分类，以确定每个超像素是否属于肌腱类别（从而进行肌腱分割）。在第二种方法中，超像素的排列被转换成图，而不是传统的图像网格。这一分类过程使用基于图的卷积网络（GCN）来确定每个超像素是否对应于肌腱类别。数据集由 76 名受试者组成，分为两组：一组用于训练（数据集 1，采用 "留一弃组 "交叉验证法进行训练和评估），另一组作为未见测试数据（数据集 2）。使用我们的第一种方法，RF 和 SVM 分类器在测试数据（数据集 2）上的最终测试 AUC（ROC 曲线下面积）得分分别为 0.992 和 0.987，灵敏度分别为 0.904 和 0.966。另一方面，使用我们的第二种方法（基于 GCN 的节点分类），测试集的 AUC 得分为 0.933，灵敏度为 0.899。无论是利用射频、基于 SVM 的超像素分类，还是基于 GCN 的分类进行肌腱分割，我们的系统都能获得值得称赞的 AUC 分数，尤其是非基于图谱的方法。由于数据集有限，我们基于图的方法的表现不如非基于图的超像素分类方法；但是，所获得的结果为我们了解模型如何区分肌腱和非肌腱提供了宝贵的见解。这为进一步探索和改进提供了机会。

{"title":"Beyond pixel: Superpixel-based MRI segmentation through traditional machine learning and graph convolutional network","authors":"","doi":"10.1016/j.cmpb.2024.108398","DOIUrl":"10.1016/j.cmpb.2024.108398","url":null,"abstract":"<div><h3>Background and Objective:</h3><p>Tendon segmentation is crucial for studying tendon-related pathologies like tendinopathy, tendinosis, etc. This step further enables detailed analysis of specific tendon regions using automated or semi-automated methods. This study specifically aims at the segmentation of Achilles tendon, the largest tendon in the human body.</p></div><div><h3>Methods:</h3><p>This study proposes a comprehensive end-to-end tendon segmentation module composed of a preliminary superpixel-based coarse segmentation preceding the final segmentation task. The final segmentation results are obtained through two distinct approaches. In the first approach, the coarsely generated superpixels are subjected to classification using Random Forest (RF) and Support Vector Machine (SVM) classifiers to classify whether each superpixel belongs to a tendon class or not (resulting in tendon segmentation). In the second approach, the arrangements of superpixels are converted to graphs instead of being treated as conventional image grids. This classification process uses a graph-based convolutional network (GCN) to determine whether each superpixel corresponds to a tendon class or not.</p></div><div><h3>Results:</h3><p>All experiments are conducted on a custom-made ankle MRI dataset. The dataset comprises 76 subjects and is divided into two sets: one for training (Dataset 1, trained and evaluated using leave-one-group-out cross-validation) and the other as unseen test data (Dataset 2). Using our first approach, the final test AUC (Area Under the ROC Curve) scores using RF and SVM classifiers on the test data (Dataset 2) are 0.992 and 0.987, respectively, with sensitivities of 0.904 and 0.966. On the other hand, using our second approach (GCN-based node classification), the AUC score for the test set is 0.933 with a sensitivity of 0.899.</p></div><div><h3>Conclusions:</h3><p>Our proposed pipeline demonstrates the efficacy of employing superpixel generation as a coarse segmentation technique for the final tendon segmentation. Whether utilizing RF, SVM-based superpixel classification, or GCN-based classification for tendon segmentation, our system consistently achieves commendable AUC scores, especially the non-graph-based approach. Given the limited dataset, our graph-based method did not perform as well as non-graph-based superpixel classifications; however, the results obtained provide valuable insights into how well the models can distinguish between tendons and non-tendons. This opens up opportunities for further exploration and improvement.</p></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169260724003912/pdfft?md5=ec546df96619be9e2501c6602cc6ef74&pid=1-s2.0-S0169260724003912-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137112","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}

引用次数: 0

Latent disentanglement in mesh variational autoencoders improves the diagnosis of craniofacial syndromes and aids surgical planning 网状变异自动编码器中的潜伏纠缠改善了颅面综合征的诊断并有助于手术规划

IF 4.9 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine

Pub Date : 2024-08-26 DOI: 10.1016/j.cmpb.2024.108395

Background and objective:

The use of deep learning to undertake shape analysis of the complexities of the human head holds great promise. However, there have traditionally been a number of barriers to accurate modelling, especially when operating on both a global and local level.

Methods:

In this work, we will discuss the application of the Swap Disentangled Variational Autoencoder (SD-VAE) with relevance to Crouzon, Apert and Muenke syndromes. The model is trained on a dataset of 3D meshes of healthy and syndromic patients which was increased in size with a novel data augmentation technique based on spectral interpolation. Thanks to its semantically meaningful and disentangled latent representation, SD-VAE is used to analyse and generate head shapes while considering the influence of different anatomical sub-units.

Results:

Although syndrome classification is performed on the entire mesh, it is also possible, for the first time, to analyse the influence of each region of the head on the syndromic phenotype. By manipulating specific parameters of the generative model, and producing procedure-specific new shapes, it is also possible to approximate the outcome of a range of craniofacial surgical procedures.

Conclusion:

This work opens new avenues to advance diagnosis, aids surgical planning and allows for the objective evaluation of surgical outcomes. Our code is available at github.com/simofoti/CraniofacialSD-VAE.

背景和目的：利用深度学习对人类头部的复杂性进行形状分析大有可为。方法：在这项工作中，我们将讨论交换离散变异自动编码器（SD-VAE）在克鲁宗、阿珀特和穆恩克综合征中的应用。该模型在健康和综合征患者的三维网格数据集上进行了训练，该数据集通过基于光谱插值的新型数据扩增技术扩大了规模。结果：虽然综合征分类是在整个网格上进行的，但也首次实现了分析头部每个区域对综合征表型的影响。结论：这项工作开辟了推进诊断的新途径，有助于手术规划，并可对手术结果进行客观评估。我们的代码位于 github.com/simofoti/CraniofacialSD-VAE。

{"title":"Latent disentanglement in mesh variational autoencoders improves the diagnosis of craniofacial syndromes and aids surgical planning","authors":"","doi":"10.1016/j.cmpb.2024.108395","DOIUrl":"10.1016/j.cmpb.2024.108395","url":null,"abstract":"<div><h3>Background and objective:</h3><p>The use of deep learning to undertake shape analysis of the complexities of the human head holds great promise. However, there have traditionally been a number of barriers to accurate modelling, especially when operating on both a global and local level.</p></div><div><h3>Methods:</h3><p>In this work, we will discuss the application of the Swap Disentangled Variational Autoencoder (SD-VAE) with relevance to Crouzon, Apert and Muenke syndromes. The model is trained on a dataset of 3D meshes of healthy and syndromic patients which was increased in size with a novel data augmentation technique based on spectral interpolation. Thanks to its semantically meaningful and disentangled latent representation, SD-VAE is used to analyse and generate head shapes while considering the influence of different anatomical sub-units.</p></div><div><h3>Results:</h3><p>Although syndrome classification is performed on the entire mesh, it is also possible, for the first time, to analyse the influence of each region of the head on the syndromic phenotype. By manipulating specific parameters of the generative model, and producing procedure-specific new shapes, it is also possible to approximate the outcome of a range of craniofacial surgical procedures.</p></div><div><h3>Conclusion:</h3><p>This work opens new avenues to advance diagnosis, aids surgical planning and allows for the objective evaluation of surgical outcomes. Our code is available at <span><span>github.com/simofoti/CraniofacialSD-VAE</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169260724003882/pdfft?md5=7bb50e4fab3ca51c5867372f1437b450&pid=1-s2.0-S0169260724003882-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142097710","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}

引用次数: 0