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ECG classification based on guided attention mechanism 基于注意力引导机制的心电图分类
IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-03 DOI: 10.1016/j.cmpb.2024.108454
Yangcheng Huang , Wenjing Liu , Ziyi Yin , Shuaicong Hu , Mingjie Wang , Wenjie Cai

Background and Objective

Integrating domain knowledge into deep learning models can improve their effectiveness and increase explainability. This study aims to enhance the classification performance of electrocardiograms (ECGs) by customizing specific guided mechanisms based on the characteristics of different cardiac abnormalities.

Methods

Two novel guided attention mechanisms, Guided Spatial Attention (GSA) and CAM-based spatial guided attention mechanism (CGAM), were introduced. Different attention guidance labels were created based on clinical knowledge for four ECG abnormality classification tasks: ST change detection, premature contraction identification, Wolf-Parkinson-White syndrome (WPW) classification, and atrial fibrillation (AF) detection. The models were trained and evaluated separately for each classification task. Model explainability was quantified using Shapley values.

Results

GSA improved the F1 score of the model by 5.74%, 5%, 8.96%, and 3.91% for ST change detection, premature contraction identification, WPW classification, and AF detection, respectively. Similarly, CGAM exhibited improvements of 3.89%, 5.40%, 8.21%, and 1.80% for the respective tasks. The combined use of GSA and CGAM resulted in even higher improvements of 6.26%, 5.58%, 8.85%, and 4.03%, respectively. Moreover, when all four tasks were conducted simultaneously, a notable overall performance boost was achieved, demonstrating the broad adaptability of the proposed model. The quantified Shapley values demonstrated the effectiveness of the guided attention mechanisms in enhancing the model's explainability.

Conclusions

The guided attention mechanisms, utilizing domain knowledge, effectively directed the model's attention, leading to improved classification performance and explainability. These findings have significant implications in facilitating accurate automated ECG classification.
背景和目的:将领域知识整合到深度学习模型中可以提高模型的有效性和可解释性。本研究旨在根据不同心脏异常的特点定制特定的引导机制,从而提高心电图(ECG)的分类性能:方法:引入了两种新颖的引导注意机制,即空间引导注意机制(GSA)和基于 CAM 的空间引导注意机制(CGAM)。根据临床知识为四种心电图异常分类任务创建了不同的注意力引导标签:ST变化检测、早搏识别、沃尔夫-帕金森-怀特综合征(WPW)分类和心房颤动(AF)检测。每个分类任务都分别对模型进行了训练和评估。使用 Shapley 值对模型的可解释性进行量化:在 ST 变化检测、早搏识别、WPW 分类和房颤检测方面,GSA 模型的 F1 分数分别提高了 5.74%、5%、8.96% 和 3.91%。同样,CGAM 在相应任务中的得分也分别提高了 3.89%、5.40%、8.21% 和 1.80%。结合使用 GSA 和 CGAM 后,改进幅度更大,分别为 6.26%、5.58%、8.85% 和 4.03%。此外,当同时执行所有四项任务时,整体性能也得到了显著提升,这证明了所提出模型的广泛适应性。量化的夏普利值证明了引导注意力机制在提高模型可解释性方面的有效性:结论:利用领域知识的引导注意力机制有效地引导了模型的注意力,从而提高了分类性能和可解释性。这些发现对促进准确的自动心电图分类具有重要意义。
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引用次数: 0
Brain color-coded diffusion imaging: Utility of ACPC reorientation of gradients in healthy subjects and patients 大脑彩色编码扩散成像:ACPC 梯度重新定向在健康受试者和患者中的实用性。
IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-02 DOI: 10.1016/j.cmpb.2024.108449
Omar Ouachikh , Remi Chaix , Anna Sontheimer , Jerome Coste , Omar Ait Aider , Aigerim Dautkulova , Kamel Abdelouahab , Aziz Hafidi , Maha Ben Salah , Bruno Pereira , Jean-Jacques Lemaire

Background and Objective

The common structural interpretation of diffusion color-encoded (DCE) maps assumes that the brain is aligned with the gradients of the MRI machine. This is seldom achieved in the field, leading to incorrect red (R), green (G) and blue (B) DCE values for the expected orientation of fiber bundles. We studied the virtual reorientation of gradients according to the anterior commissure – posterior commissure (ACPC) system on the RGB derivatives.

Methods

We measured mean ± standard deviation of average, standard deviation, skewness and kurtosis of RGB derivatives, before (rO) and after (acpcO) gradient reorientation, in one healthy-subject group with head routinely positioned (HS-routine), and in two patient groups, one with essential tremor (ET-Opti), and one with Parkinson's disease (PD-Opti), with head position optimized according to ACPC before acquisition. We studied the pitch, roll and yaw angles of reorientation, and we compared rO and acpcO conditions, and groups (ad hoc statistics).

Results

Pitch (maximum in the HS-routine group) was greater than roll and yaw. After reorientation of gradients, in the HS-routine group, DCE average increased, and Stddev, skewness and kurtosis decreased; R, G and B average increased, and R and B skewness and kurtosis decreased. By contrast, in the ET-Opti group and the PD-Opti group, R, G and B, average and Stddev increased, and skewness and kurtosis decreased. In both rO and acpcO conditions, in the ET-Opti and PD-Opti groups, average and standard deviation were higher, while skewness and kurtosis were lower.

Conclusions

DCE map interpretability depends on brain orientation. Reorientation realigns gradients with the anatomic and physiologic position of the head and brain, as exemplified.
背景和目的:弥散彩色编码(DCE)图的常见结构解释假定大脑与磁共振成像机的梯度对齐。这在现场很少能实现,导致纤维束预期方向的红色(R)、绿色(G)和蓝色(B)DCE 值不正确。我们根据 RGB 衍生物上的前会阴-后会阴(ACPC)系统研究了梯度的虚拟重新定向:方法:我们在一组头部常规定位(HS-routine)的健康受试者和两组在采集前根据 ACPC 系统优化头部位置的患者中,分别测量了 RGB 衍生物在梯度重新定向前(rO)和后(acpcO)的平均值、标准偏差、偏斜度和峰度(± 标准偏差)。我们研究了调整方向时的俯仰角、滚动角和偏航角,并对 rO 和 acpcO 条件以及各组进行了比较(特别统计):结果:俯仰角(HS 常规组最大)大于滚动角和偏航角。梯度调整后,常规 HS 组的 DCE 平均值增加,stddev、偏斜度和峰度降低;R、G 和 B 平均值增加,R 和 B 偏斜度和峰度降低。相比之下,在 ET-Opti 组和 PD-Opti 组,R、G 和 B 平均值和 Stddev 增加,偏度和峰度减少。在rO和acpcO条件下,ET-Opti组和PD-Opti组的平均值和标准差较高,而偏度和峰度较低:DCE图谱的可解释性取决于大脑定向。如图所示,重新定向可根据头部和大脑的解剖和生理位置重新调整梯度。
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引用次数: 0
Concept-based AI interpretability in physiological time-series data: Example of abnormality detection in electroencephalography 生理时间序列数据中基于概念的人工智能可解释性:脑电图异常检测实例
IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-30 DOI: 10.1016/j.cmpb.2024.108448
Alexander Brenner , Felix Knispel , Florian P. Fischer , Peter Rossmanith , Yvonne Weber , Henner Koch , Rainer Röhrig , Julian Varghese , Ekaterina Kutafina

Background and Objective

Despite recent performance advancements, deep learning models are not yet adopted in clinical practice on a wide scale. The intrinsic intransparency of such systems is commonly cited as one major reason for this reluctance. This has motivated methods that aim to provide explanations of model functioning. Known limitations of feature-based explanations have led to an increased interest in concept-based interpretability. Testing with Concept Activation Vectors (TCAV) employs human-understandable, abstract concepts to explain model behavior. The method has previously been applied to the medical domain in the context of electronic health records, retinal fundus images and magnetic resonance imaging.

Methods

We explore the usage of TCAV for building interpretable models on physiological time series, using an example of abnormality detection in electroencephalography (EEG). For this purpose, we adopt the XceptionTime model, which is suitable for multi-channel physiological data of variable sizes. The model provides state-of-the-art performance on raw EEG data and is publically available. We propose and test several ideas regarding concept definition through metadata mining, using additional labeled EEG data and extracting interpretable signal characteristics in the form of frequencies. By including our own hospital data with analog labeling, we further evaluate the robustness of our approach.

Results

The tested concepts show a TCAV score distribution that is in line with the clinical expectations, i.e. concepts known to have strong links with EEG pathologies (such as epileptiform discharges) received higher scores than the neutral concepts (e.g. sex). The scores were consistent across the applied concept generation strategies.

Conclusions

TCAV has the potential to improve interpretability of deep learning applied to multi-channel signals as well as to detect possible biases in the data. Still, further work on developing the strategies for concept definition and validation on clinical physiological time series is needed to better understand how to extract clinically relevant information from the concept sensitivity scores.
背景与目标尽管最近在性能方面取得了进步,但深度学习模型尚未在临床实践中得到广泛采用。这类系统固有的不透明性通常被认为是造成这种不情愿的一个主要原因。这就促使人们采用旨在解释模型功能的方法。基于特征的解释存在已知的局限性,因此人们对基于概念的可解释性越来越感兴趣。概念激活向量测试(TCAV)采用人类可理解的抽象概念来解释模型行为。我们以脑电图(EEG)中的异常检测为例,探讨了如何利用 TCAV 建立生理时间序列上的可解释模型。为此,我们采用了 XceptionTime 模型,该模型适用于不同规模的多通道生理数据。该模型在原始脑电图数据上提供了最先进的性能,并已公开发布。我们提出并测试了通过元数据挖掘、使用附加标记的脑电图数据和提取频率形式的可解释信号特征来定义概念的若干想法。结果经测试的概念显示,TCAV 分数分布符合临床预期,即与脑电图病理有密切联系的概念(如癫痫样放电)的得分高于中性概念(如性别)。结论TCAV 有潜力提高应用于多通道信号的深度学习的可解释性,并检测数据中可能存在的偏差。不过,为了更好地理解如何从概念灵敏度分数中提取临床相关信息,还需要进一步开发概念定义策略并在临床生理时间序列上进行验证。
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引用次数: 0
In silico data-based comparison of the accuracy and error source of various methods for noninvasively estimating central aortic blood pressure 对各种无创估测中心主动脉血压方法的准确性和误差源进行基于硅数据的比较。
IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-30 DOI: 10.1016/j.cmpb.2024.108450
Xujie Zhang , Zhaojun Li , Zhi Zhang , Tianqi Wang , Fuyou Liang

Background and objectives

The higher clinical significance of central aortic blood pressure (CABP) compared to peripheral blood pressures has been extensively demonstrated. Accordingly, many methods for noninvasively estimating CABP have been proposed. However, there still lacks a systematic comparison of existing methods, especially in terms of how they differ in the ability to tolerate individual differences or measurement errors. The present study was designed to address this gap.

Methods

A large-scale ‘virtual subject’ dataset (n = 600) was created using a computational model of the cardiovascular system, and applied to examine several classical CABP estimation methods, including the direct method, generalized transfer function (GTF) method, n-point moving average (NPMA) method, second systolic pressure of periphery (SBP2) method, physical model-based wave analysis (MBWA) method, and suprasystolic cuff-based waveform reconstruction (SCWR) method. The errors of CABP estimation were analyzed and compared among methods with respect to the magnitude/distribution, correlations with physiological/hemodynamic factors, and sensitivities to noninvasive measurement errors.

Results

The errors of CABP estimation exhibited evident inter-method differences in terms of the mean and standard deviation (SD). Relatively, the estimation errors of the methods adopting pre-trained algorithms (i.e., the GTF and SCWR methods) were overall smaller and less sensitive to variations in physiological/hemodynamic conditions and random errors in noninvasive measurement of brachial arterial blood pressure (used for calibrating peripheral pulse wave). The performances of all the methods worsened following the introduction of random errors to peripheral pulse wave (used for deriving CABP), as characterized by the enlarged SD and/or increased mean of the estimation errors. Notably, the GTF and SCWR methods did not exhibit a better capability of tolerating pulse wave errors in comparison with other methods.

Conclusions

Classical noninvasive methods for estimating CABP were found to differ considerably in both the accuracy and error source, which provided theoretical evidence for understanding the specific advantages and disadvantages of each method. Knowledge about the method-specific error source and sensitivities of errors to different physiological/hemodynamic factors may contribute as theoretical references for interpreting clinical observations and exploring factors underlying large estimation errors, or provide guidance for optimizing existing methods or developing new methods.
背景和目的:与外周血压相比,中心主动脉血压(CABP)具有更高的临床意义,这一点已得到广泛证实。因此,人们提出了许多无创估测 CABP 的方法。然而,目前仍缺乏对现有方法的系统性比较,尤其是在对个体差异或测量误差的耐受能力方面。本研究旨在填补这一空白:方法:使用心血管系统计算模型创建了一个大规模 "虚拟受试者 "数据集(n = 600),并应用该数据集检验了几种经典的 CABP 估算方法,包括直接法、广义传递函数(GTF)法、n 点移动平均(NPMA)法、外周第二收缩压(SBP2)法、基于物理模型的波形分析(MBWA)法和基于袖带的收缩上心动图波形重建(SCWR)法。分析并比较了各种方法的 CABP 估算误差的大小/分布、与生理/血流动力学因素的相关性以及对无创测量误差的敏感性:从平均值和标准差(SD)来看,不同方法的 CABP 估测误差存在明显差异。相对而言,采用预训练算法的方法(即 GTF 和 SCWR 方法)的估计误差总体较小,对生理/血流动力学条件的变化和肱动脉血压(用于校准外周脉搏波)无创测量随机误差的敏感性较低。在外周脉搏波(用于推导 CABP)中引入随机误差后,所有方法的性能都有所下降,表现为估计误差的标度扩大和/或平均值增加。值得注意的是,与其他方法相比,GTF 和 SCWR 方法对脉搏波误差的耐受能力并不强:结论:研究发现,估测 CABP 的经典无创方法在准确性和误差源方面都存在很大差异,这为了解每种方法的具体优缺点提供了理论依据。有关特定方法的误差源和误差对不同生理/血流动力学因素的敏感性的知识可作为解释临床观察结果和探索导致估计误差较大的因素的理论参考,或为优化现有方法或开发新方法提供指导。
{"title":"In silico data-based comparison of the accuracy and error source of various methods for noninvasively estimating central aortic blood pressure","authors":"Xujie Zhang ,&nbsp;Zhaojun Li ,&nbsp;Zhi Zhang ,&nbsp;Tianqi Wang ,&nbsp;Fuyou Liang","doi":"10.1016/j.cmpb.2024.108450","DOIUrl":"10.1016/j.cmpb.2024.108450","url":null,"abstract":"<div><h3>Background and objectives</h3><div>The higher clinical significance of central aortic blood pressure (CABP) compared to peripheral blood pressures has been extensively demonstrated. Accordingly, many methods for noninvasively estimating CABP have been proposed. However, there still lacks a systematic comparison of existing methods, especially in terms of how they differ in the ability to tolerate individual differences or measurement errors. The present study was designed to address this gap.</div></div><div><h3>Methods</h3><div>A large-scale ‘virtual subject’ dataset (n = 600) was created using a computational model of the cardiovascular system, and applied to examine several classical CABP estimation methods, including the direct method, generalized transfer function (GTF) method, n-point moving average (NPMA) method, second systolic pressure of periphery (SBP2) method, physical model-based wave analysis (MBWA) method, and suprasystolic cuff-based waveform reconstruction (SCWR) method. The errors of CABP estimation were analyzed and compared among methods with respect to the magnitude/distribution, correlations with physiological/hemodynamic factors, and sensitivities to noninvasive measurement errors.</div></div><div><h3>Results</h3><div>The errors of CABP estimation exhibited evident inter-method differences in terms of the mean and standard deviation (SD). Relatively, the estimation errors of the methods adopting pre-trained algorithms (i.e., the GTF and SCWR methods) were overall smaller and less sensitive to variations in physiological/hemodynamic conditions and random errors in noninvasive measurement of brachial arterial blood pressure (used for calibrating peripheral pulse wave). The performances of all the methods worsened following the introduction of random errors to peripheral pulse wave (used for deriving CABP), as characterized by the enlarged SD and/or increased mean of the estimation errors. Notably, the GTF and SCWR methods did not exhibit a better capability of tolerating pulse wave errors in comparison with other methods.</div></div><div><h3>Conclusions</h3><div>Classical noninvasive methods for estimating CABP were found to differ considerably in both the accuracy and error source, which provided theoretical evidence for understanding the specific advantages and disadvantages of each method. Knowledge about the method-specific error source and sensitivities of errors to different physiological/hemodynamic factors may contribute as theoretical references for interpreting clinical observations and exploring factors underlying large estimation errors, or provide guidance for optimizing existing methods or developing new methods.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108450"},"PeriodicalIF":4.9,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142380231","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
The impact of different degrees of stenosis on platelet deposition in the left anterior descending branch of the coronary artery 不同狭窄程度对冠状动脉左前降支血小板沉积的影响。
IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-29 DOI: 10.1016/j.cmpb.2024.108445
Yiming Zhao , Haoyao Cao , Yongtao Wei , Tinghui Zheng

Background and objective

This study aimed to investigate the impact of different stenotic degrees on platelet deposition in the left anterior descending branch of the coronary artery.

Methods

The idealized model of coronary artery stenosis of 30 %, 40 %, 50 %, 60 %, 70 % and four patient-specific models of 22.17 %, 34.88 %, 51.23 % and 62.96 % were established. A discrete phase model was used to calculate the deposition of platelet particles in blood.

Results

(1) As the stenotic degree increased from 30 % to 70 %, the maximum deposition rates were 4.23e-2 kg/(m2 ·s), 3.47e-2 kg/(m2 ·s), 0.14 kg/(m2 ·s), 0.15 kg/(m2 ·s), and 0.38 kg/(m2 ·s), respectively. (2) The greater the stenotic degree, the more points of platelet deposition. (3) Platelets were mainly deposited at the proximal segment of mild stenosis. When the stenotic degree exceeded 50 %, the deposition position moved to the distal segment of the stenosis. (4) The results in the real coronary artery models were similar to those in the idealized model.

Conclusion

The study suggests that the location and number of platelet deposition are related to the degree of stenosis. Moderate to severe stenosis is more likely to spread downstream.
背景和目的:本研究旨在探讨不同狭窄程度对冠状动脉左前降支血小板沉积的影响:建立了 30%、40%、50%、60%、70% 的理想化冠状动脉狭窄模型和 22.17%、34.88%、51.23%、62.96% 四种患者特异性模型。结果:(1)随着狭窄程度从 30% 增加到 70%,最大沉积率分别为 4.23e-2 kg/(m2 -s)、3.47e-2 kg/(m2 -s)、0.14 kg/(m2 -s)、0.15 kg/(m2 -s)和 0.38 kg/(m2 -s)。(2)狭窄程度越大,血小板沉积点越多。(3)血小板主要沉积在轻度狭窄的近端。当狭窄程度超过 50%时,血小板沉积位置转移到狭窄远端。(4)真实冠状动脉模型中的结果与理想化模型中的结果相似:研究表明,血小板沉积的位置和数量与狭窄程度有关。中度至重度狭窄更有可能向下游扩散。
{"title":"The impact of different degrees of stenosis on platelet deposition in the left anterior descending branch of the coronary artery","authors":"Yiming Zhao ,&nbsp;Haoyao Cao ,&nbsp;Yongtao Wei ,&nbsp;Tinghui Zheng","doi":"10.1016/j.cmpb.2024.108445","DOIUrl":"10.1016/j.cmpb.2024.108445","url":null,"abstract":"<div><h3>Background and objective</h3><div>This study aimed to investigate the impact of different stenotic degrees on platelet deposition in the left anterior descending branch of the coronary artery.</div></div><div><h3>Methods</h3><div>The idealized model of coronary artery stenosis of 30 %, 40 %, 50 %, 60 %, 70 % and four patient-specific models of 22.17 %, 34.88 %, 51.23 % and 62.96 % were established. A discrete phase model was used to calculate the deposition of platelet particles in blood.</div></div><div><h3>Results</h3><div>(1) As the stenotic degree increased from 30 % to 70 %, the maximum deposition rates were 4.23e<sup>-</sup><sup>2</sup> kg/(m<sup>2</sup> ·s), 3.47e<sup>-</sup><sup>2</sup> kg/(m<sup>2</sup> ·s), 0.14 kg/(m<sup>2</sup> ·s), 0.15 kg/(m<sup>2</sup> ·s), and 0.38 kg/(m<sup>2</sup> ·s), respectively. (2) The greater the stenotic degree, the more points of platelet deposition. (3) Platelets were mainly deposited at the proximal segment of mild stenosis. When the stenotic degree exceeded 50 %, the deposition position moved to the distal segment of the stenosis. (4) The results in the real coronary artery models were similar to those in the idealized model.</div></div><div><h3>Conclusion</h3><div>The study suggests that the location and number of platelet deposition are related to the degree of stenosis. Moderate to severe stenosis is more likely to spread downstream.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108445"},"PeriodicalIF":4.9,"publicationDate":"2024-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142380232","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
Machine learning for post-liver transplant survival: Bridging the gap for long-term outcomes through temporal variation features 肝移植后存活率的机器学习:通过时间变化特征缩小长期结果的差距。
IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-29 DOI: 10.1016/j.cmpb.2024.108442
Kiruthika Balakrishnan , Sawyer Olson , Gyorgy Simon , Lisiane Pruinelli

Background

The long-term survival of liver transplant (LT) recipients is essential for optimizing organ allocation and estimating mortality outcomes. While models like the Model-for-End-Stage-Liver-Disease (MELD) predict 90-day mortality on the waiting list, they do not predict post-LT survival accurately. There is a need for predictive models that can forecast post-LT survival beyond the immediate period after transplantation.

Method

This study introduces new temporal variation features for predicting post-LT survival during the waiting list period. Cox Proportional-Hazards regression (CoxPH), Random Survival Forest (RSF), and Extreme Gradient Boosting (XGB) models are utilized, along with patient demographics and waiting list duration. Data from 716 LT patients from the University of Minnesota CTSI (2011–2021) are used to develop, evaluate, and compare post-LT survival prediction models.

Results

The temporal variation features, particularly when combined with the RSF model, proved most effective in predicting post-LT survival, with a C-index of 0.71 and an IBS of 0.151. This outperformed the predictive capability of the most recent MELD score, which had a C-index of <0.51 in the same cohort.

Conclusions

Incorporating temporal variation features with the RSF model enhances long-term post-LT survival predictions. These insights can assist clinicians and patients in making more informed decisions about organ allocation and understanding the utility of LT, ultimately leading to improved patient outcomes.
背景:肝移植(LT)受者的长期存活率对于优化器官分配和估计死亡率结果至关重要。虽然肝病终末期模型(MELD)等模型可以预测候选名单上的 90 天死亡率,但却不能准确预测肝移植后的存活率。因此,我们需要能预测移植术后存活率的预测模型:本研究引入了新的时间变化特征,用于预测等待名单期间的 LT 后存活率。方法:本研究引入了新的时间变化特征,用于预测等待名单期间的 LT 后存活率。研究利用了 Cox 比例-危险回归(CoxPH)、随机生存森林(RSF)和极端梯度提升(XGB)模型,以及患者人口统计学和等待名单持续时间。明尼苏达大学CTSI的716名LT患者(2011-2021年)的数据被用来开发、评估和比较LT后生存预测模型:结果:时间变化特征,尤其是与 RSF 模型相结合时,在预测长管治疗后生存率方面被证明是最有效的,C 指数为 0.71,IBS 为 0.151。这优于最新 MELD 评分的预测能力,后者的 C 指数为结论:将时间变化特征与 RSF 模型相结合可提高 LT 后的长期生存预测能力。这些见解可帮助临床医生和患者就器官分配做出更明智的决定,并了解 LT 的效用,最终改善患者的预后。
{"title":"Machine learning for post-liver transplant survival: Bridging the gap for long-term outcomes through temporal variation features","authors":"Kiruthika Balakrishnan ,&nbsp;Sawyer Olson ,&nbsp;Gyorgy Simon ,&nbsp;Lisiane Pruinelli","doi":"10.1016/j.cmpb.2024.108442","DOIUrl":"10.1016/j.cmpb.2024.108442","url":null,"abstract":"<div><h3>Background</h3><div>The long-term survival of liver transplant (LT) recipients is essential for optimizing organ allocation and estimating mortality outcomes. While models like the Model-for-End-Stage-Liver-Disease (MELD) predict 90-day mortality on the waiting list, they do not predict post-LT survival accurately. There is a need for predictive models that can forecast post-LT survival beyond the immediate period after transplantation.</div></div><div><h3>Method</h3><div>This study introduces new temporal variation features for predicting post-LT survival during the waiting list period. Cox Proportional-Hazards regression (CoxPH), Random Survival Forest (RSF), and Extreme Gradient Boosting (XGB) models are utilized, along with patient demographics and waiting list duration. Data from 716 LT patients from the University of Minnesota CTSI (2011–2021) are used to develop, evaluate, and compare post-LT survival prediction models.</div></div><div><h3>Results</h3><div>The temporal variation features, particularly when combined with the RSF model, proved most effective in predicting post-LT survival, with a C-index of 0.71 and an IBS of 0.151. This outperformed the predictive capability of the most recent MELD score, which had a C-index of &lt;0.51 in the same cohort.</div></div><div><h3>Conclusions</h3><div>Incorporating temporal variation features with the RSF model enhances long-term post-LT survival predictions. These insights can assist clinicians and patients in making more informed decisions about organ allocation and understanding the utility of LT, ultimately leading to improved patient outcomes.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108442"},"PeriodicalIF":4.9,"publicationDate":"2024-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142379218","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
Pre-anesthetic brain network metrics as predictors of individual propofol sensitivity 麻醉前大脑网络指标作为个体异丙酚敏感性的预测指标。
IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-29 DOI: 10.1016/j.cmpb.2024.108447
Yun Zhang , Fei Yan , Qiang Wang , Yubo Wang , Liyu Huang

Background and Objective

Numerous factors, including demographic characteristics, have been implicated in modulating individual sensitivity to propofol; however, substantial inter-individual differences persist even after accounting for these factors. This study thus aimed to explore whether pre-anesthesia brain functional network metrics correlate with an individual's sensitivity to propofol.

Methods

A total of 54 subjects, including 30 patients and 24 healthy volunteers, were enrolled. Propofol was administered via a target-controlled infusion device, and anesthesia depth was monitored using a bispectral index monitor. Sensitivity to propofol was quantified using the induction time, measured from infusion onset to the bispectral index, which reached 60. Brain functional network metrics indicative of functional integration and segregation, centrality, and network resilience were computed from pre-anesthetic 60-channel EEG recordings. Linear regression analysis and machine learning predictive models were applied to evaluate the contribution of pre-anesthesia network metrics in predicting individual sensitivity to propofol.

Results

Our analysis results revealed that subjects could be categorized into high- or low-sensitivity groups based on their induction time. Individuals with low sensitivity exhibited a greater network degree, clustering coefficient, global efficiency, and betweenness centrality, along with reduced modularity and assortativity coefficient in the alpha band. Furthermore, alpha band network metrics were significantly correlated with individual induction time. Leveraging these network metrics as features enabled the classification of individuals into high- or low-sensitivity groups with an accuracy of 94%.

Conclusions

Using a clinically relevant endpoint that signifies the level of anesthesia suitable for surgical procedures, this study underscored the robust correlation between pre-anesthesia alpha-band network metrics and individual sensitivity to propofol in a cohort that included both patients and healthy volunteers. Our findings offer preliminary insights into the potential utility of pre-anesthetic brain status assessment to predict propofol sensitivity on an individual basis, which may help to develop a more accurate personalized anesthesia plan.
背景和目的:包括人口统计学特征在内的许多因素被认为会影响个体对丙泊酚的敏感性;然而,即使考虑了这些因素,个体间的巨大差异依然存在。因此,本研究旨在探讨麻醉前大脑功能网络指标是否与个体对丙泊酚的敏感性相关:方法:共招募了 54 名受试者,包括 30 名患者和 24 名健康志愿者。方法:共招募了 54 名受试者,包括 30 名患者和 24 名健康志愿者。通过靶控输注装置注射异丙酚,并使用双谱指数监测仪监测麻醉深度。对丙泊酚的敏感性通过诱导时间进行量化,诱导时间是指从输注开始到双谱指数达到 60 的时间。根据麻醉前的 60 通道脑电图记录,计算了表明功能整合和分离、中心性和网络弹性的大脑功能网络指标。应用线性回归分析和机器学习预测模型来评估麻醉前网络指标在预测个体对丙泊酚敏感性方面的贡献:结果:我们的分析结果表明,受试者可根据诱导时间被分为高敏感性组和低敏感性组。低敏感度个体表现出更高的网络度、聚类系数、全局效率和间度中心性,同时降低了α波段的模块性和同类系数。此外,α波段网络指标与个体诱导时间显著相关。利用这些网络指标作为特征,可以将个体划分为高敏或低敏群体,准确率高达 94%:这项研究使用了一个临床相关终点,该终点标志着适合外科手术的麻醉水平,它强调了麻醉前阿尔法波段网络指标与个体对丙泊酚的敏感性之间的紧密相关性。我们的研究结果初步揭示了麻醉前大脑状态评估在预测个体对丙泊酚敏感性方面的潜在作用,这可能有助于制定更准确的个性化麻醉计划。
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引用次数: 0
IG-Net: An Instrument-guided real-time semantic segmentation framework for prostate dissection during surgery for low rectal cancer IG-Net:低位直肠癌手术中用于前列腺切除的仪器引导实时语义分割框架。
IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-28 DOI: 10.1016/j.cmpb.2024.108443
Bo Sun , Zhen Sun , Kexuan Li , Xuehao Wang , Guotao Wang , Wenfeng Song , Shuai Li , Aimin Hao , Yi Xiao

Background and Objective:

Accurate prostate dissection is crucial in transanal surgery for patients with low rectal cancer. Improper dissection can lead to adverse events such as urethral injury, severely affecting the patient’s postoperative recovery. However, unclear boundaries, irregular shape of the prostate, and obstructive factors such as smoke present significant challenges for surgeons.

Methods:

Our innovative contribution lies in the introduction of a novel video semantic segmentation framework, IG-Net, which incorporates prior surgical instrument features for real-time and precise prostate segmentation. Specifically, we designed an instrument-guided module that calculates the surgeon’s region of attention based on instrument features, performs local segmentation, and integrates it with global segmentation to enhance performance. Additionally, we proposed a keyframe selection module that calculates the temporal correlations between consecutive frames based on instrument features. This module adaptively selects non-keyframe for feature fusion segmentation, reducing noise and optimizing speed.

Results:

To evaluate the performance of IG-Net, we constructed the most extensive dataset known to date, comprising 106 video clips and 6153 images. The experimental results reveal that this method achieves favorable performance, with 72.70% IoU, 82.02% Dice, and 35 FPS.

Conclusions:

For the task of prostate segmentation based on surgical videos, our proposed IG-Net surpasses all previous methods across multiple metrics. IG-Net balances segmentation accuracy and speed, demonstrating strong robustness against adverse factors.
背景和目的:在对低位直肠癌患者进行经肛门手术时,准确的前列腺解剖至关重要。解剖不当会导致尿道损伤等不良事件,严重影响患者的术后恢复。然而,边界不清、前列腺形状不规则以及烟雾等阻塞因素给外科医生带来了巨大挑战:我们的创新贡献在于引入了一个新颖的视频语义分割框架 IG-Net,该框架结合了先前的手术器械特征,可实现实时、精确的前列腺分割。具体来说,我们设计了一个器械引导模块,该模块可根据器械特征计算外科医生的注意区域,执行局部分割,并将其与全局分割整合以提高性能。此外,我们还提出了一个关键帧选择模块,可根据仪器特征计算连续帧之间的时间相关性。该模块可自适应地选择非关键帧进行特征融合分割,从而减少噪声并优化速度:为了评估 IG-Net 的性能,我们构建了迄今为止已知的最广泛的数据集,其中包括 106 个视频片段和 6153 幅图像。实验结果表明,该方法性能良好,IoU 为 72.70%,Dice 为 82.02%,FPS 为 35:对于基于手术视频的前列腺分割任务,我们提出的 IG-Net 在多个指标上超越了之前的所有方法。IG-Net 兼顾了分割准确性和速度,在不利因素面前表现出很强的鲁棒性。
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引用次数: 0
Classification of mindfulness experiences from gamma-band effective connectivity: Application of machine-learning algorithms on resting, breathing, and body scan 通过伽玛波段有效连接对正念体验进行分类:将机器学习算法应用于静息、呼吸和身体扫描。
IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-28 DOI: 10.1016/j.cmpb.2024.108446
Ai-Ling Hsu , Chun-Yu Wu , Hei-Yin Hydra Ng , Chun-Hsiang Chuang , Chih-Mao Huang , Changwei W. Wu , Yi-Ping Chao

Background and Objective

Practicing mindfulness is a mental process toward interoceptive awareness, achieving stress reduction and emotion regulation through brain-function alteration. Literature has shown that electroencephalography (EEG)-derived connectivity possesses the potential to differentiate brain functions between mindfulness naïve and mindfulness experienced, where such quantitative differentiation could benefit telediagnosis for mental health. However, there is no prior guidance in model selection targeting on the mindfulness-experience prediction. Here we hypothesized that the EEG effective connectivity could reach a good prediction performance in mindfulness experiences with brain interpretability.

Methods

We aimed at probing direct Directed Transfer Function (dDTF) to classify the participants’ history of mindfulness-based stress reduction (MBSR), and aimed at optimizing the prediction accuracy by comparing multiple machine learning (ML) algorithms. Targeting the gamma-band effective connectivity, we evaluated the EEG-based prediction of the mindfulness experiences across 7 machine learning (ML) algorithms and 3 sessions (i.e., resting, focus-breathing, and body-scan).

Results

The support vector machine and naïve Bayes classifiers exhibited significant accuracies above the chance level across all three sessions, and the decision tree algorithm reached the highest prediction accuracy of 91.7 % with the resting state, compared to the classification accuracies with the other two mindful states. We further conducted the analysis on essential EEG channels to preserve the classification accuracy, revealing that preserving just four channels (F7, F8, T7, and P7) out of 19 yielded the accuracy of 83.3 %. Delving into the contribution of connectivity features, specific connectivity features predominantly located in the frontal lobe contributed more to classifier construction, which aligned well with the existing mindfulness literature.

Conclusion

In the present study, we initiated a milestone of developing an EEG-based classifier to detect a person's mindfulness experience objectively. The prediction accuracy of the decision tree was optimal to differentiate the mindfulness experiences using the local resting-state EEG data. The suggested algorithm and key channels on the mindfulness-experience prediction may provide guidance for predicting mindfulness experiences using the EEG-based classification embedded in future wearable neurofeedback systems or plausible digital therapeutics.
背景和目的:正念是一种心理过程,旨在通过改变大脑功能来实现互感意识、减轻压力和调节情绪。文献显示,脑电图(EEG)得出的连通性具有区分正念天真者和正念体验者大脑功能的潜力,这种定量区分有利于心理健康的远程诊断。然而,目前还没有针对正念经验预测的模型选择指南。在此,我们假设脑电图有效连通性可以在正念体验中达到良好的预测效果,并具有大脑可解释性:方法:我们旨在利用直接定向传递函数(dDTF)对参与者的正念减压(MBSR)历史进行分类,并通过比较多种机器学习(ML)算法来优化预测准确性。我们以伽玛波段有效连通性为目标,在7种机器学习(ML)算法和3个环节(即静息、专注-呼吸和身体扫描)中评估了基于脑电图的正念体验预测:支持向量机和天真贝叶斯分类器在所有三个环节中都表现出显著的准确率,高于偶然水平,与其他两种正念状态的分类准确率相比,决策树算法在静息状态下的预测准确率最高,达到 91.7%。我们进一步对重要的脑电图通道进行了分析,以保持分类的准确性,结果显示,在 19 个通道中只保留了 4 个通道(F7、F8、T7 和 P7),准确率就达到了 83.3%。深入分析连接特征的贡献,主要位于额叶的特定连接特征对分类器的构建贡献更大,这与现有的正念文献非常吻合:在本研究中,我们开发了一种基于脑电图的分类器来客观检测一个人的正念体验,这是一个里程碑。利用本地静息态脑电数据,决策树的预测准确率达到了区分正念体验的最佳水平。建议的正念体验预测算法和关键通道可为未来嵌入可穿戴神经反馈系统或可信数字疗法中的基于脑电图的分类法预测正念体验提供指导。
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引用次数: 0
Unraveling the complex interplay between abnormal hemorheology and shape asymmetry in flow through stenotic arteries 揭示异常血液流变学与流经狭窄动脉时形状不对称之间复杂的相互作用。
IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-27 DOI: 10.1016/j.cmpb.2024.108437
Soumen Chakraborty , Vishnu Teja Mantripragada , Aranyak Chakravarty , Debkalpa Goswami , Antarip Poddar

Background and Objective:

Stenosis or narrowing of arteries due to the buildup of plaque is a common occurrence in atherosclerosis and coronary artery disease (CAD), limiting blood flow to the heart and posing substantial cardiovascular risk. While the role of geometric irregularities in arterial stenosis is well-documented, the complex interplay between the abnormal hemorheology and asymmetric shape in flow characteristics remains unexplored.

Methods:

This study investigates the influence of varying hematocrit (Hct) levels, often caused by conditions such as diabetes and anemia, on flow patterns in an idealized eccentric stenotic artery using computational fluid dynamics simulations. We consider three physiological levels of Hct, 25%, 45%, and 65%, representing anemia, healthy, and diabetic conditions, respectively. The numerical simulations are performed for different combinations of shape eccentricity and blood rheological parameters, and hemodynamic indicators such as wall shear stress (WSS), oscillatory shear index (OSI), are relative residence time (RRT) are calculated to assess the arterial health.

Results:

Our results reveal the significant influence of Hct level on stenosis progression. CAD patients with anemia are exposed to lower WSS and higher OSI, which may increase the propensity for plaque progression and rupture. However, for CAD patients with high Hct level — as is often the case in diabetes — the WSS at the minimal lumen area increases rapidly, which may also lead to plaque rupture and cause adverse events such as heart attacks. These disturbances promote endothelial dysfunction, inflammation, and thrombus formation, thereby intensifying cardiovascular risk.

Conclusions:

Our findings underscore the significance of incorporating hemorheological parameters, such as Hct, into computational models for accurate assessment of flow dynamics. We envision that insights gained from this study will inform the development of tailored treatment strategies and interventions in CAD patients with common comorbidities such as diabetes and anemia, thus mitigating the adverse effects of abnormal hemorheology and reducing the ever-growing burden of cardiovascular diseases.
背景和目的:斑块堆积导致的动脉狭窄是动脉粥样硬化和冠状动脉疾病(CAD)的常见症状,它限制了心脏的血流量,并对心血管构成巨大风险。虽然几何形状不规则在动脉狭窄中的作用已得到充分证实,但异常血液流变学和不对称形状在血流特性中的复杂相互作用仍未得到探讨:本研究利用计算流体动力学模拟,研究了不同血细胞比容(Hct)水平(通常由糖尿病和贫血等疾病引起)对理想化偏心狭窄动脉中流动模式的影响。我们考虑了三种生理水平的 Hct:25%、45% 和 65%,分别代表贫血、健康和糖尿病情况。我们对不同的形状偏心率和血液流变参数组合进行了数值模拟,并计算了血流动力学指标,如壁剪切应力(WSS)、振荡剪切指数(OSI)和相对停留时间(RRT),以评估动脉健康状况:结果:我们的研究结果表明,血红蛋白(Hct)水平对血管狭窄的进展有显著影响。贫血的 CAD 患者面临较低的 WSS 和较高的 OSI,这可能会增加斑块进展和破裂的倾向。然而,对于高 Hct 水平的 CAD 患者(糖尿病患者通常如此),最小管腔区域的 WSS 会迅速增加,这也可能导致斑块破裂并引发心脏病发作等不良事件。这些干扰会促进内皮功能障碍、炎症和血栓形成,从而增加心血管风险:我们的研究结果强调了将血液流变学参数(如 Hct)纳入计算模型以准确评估流动动力学的重要性。我们设想,从这项研究中获得的见解将有助于为患有糖尿病和贫血等常见合并症的 CAD 患者制定量身定制的治疗策略和干预措施,从而减轻血液流变异常的不利影响,减轻日益加重的心血管疾病负担。
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引用次数: 0
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Computer methods and programs in biomedicine
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