Irbm最新文献_第4页

Multi-Task Hierarchical Model of Medical Images Based on Alternate Interaction 基于交替交互的医学图像多任务分层模型

IF 5.6 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm

Pub Date : 2025-04-28 DOI: 10.1016/j.irbm.2025.100892

Yucheng Song , Chenxi Li , Kangxu Fan , Lifeng Li , Jia Guo , Muhammad Ayoub , Zhifang Liao , Yitao Zuo

In the field of medical image segmentation and classification, deep learning models can automatically extract features and perform high-performance inference, thus aiding physicians in efficient and accurate automated decision support. However, these models are typically trained for a single task, leading to limitations such as neglect of task relevance and poor scalability. To address these issues, we propose a novel Alternating Multi-Task Hierarchical Network (AMTH-Net) for medical image segmentation and classification. This model is divided into three hierarchical modules: Pathology Region Clarity (PRC) as an auxiliary module to enhance the capabilities of segmentation and classification, Multi-Resolution Attention (MRA) segmentation module that uses deep supervision to focus on image information at various resolution levels to improve segmentation accuracy, and Cascaded Multi-Scale Information (CMSI) classification module which employs a cascaded multi-scale mechanism to gradually integrate discrete information from different network layers, thereby enhancing classification performance. Additionally, we introduce a novel Alternating Interaction Loss (AI-Loss) based on Multi-Gradient Information Feedback (MGIF) algorithm to further enhance the model's segmentation and diagnostic performance. Our experiments on the COVID CXR and F BUSI Breast Ultrasound datasets show that AMTH-Net achieves superior performance in both segmentation and classification tasks. Specifically, on the COVID chest X-ray (COVID CXR) dataset, the Dice coefficient of AMTH-Net reaches 98.33%, the Intersection over Union (IOU) is 96.31%, and the accuracy rate is 91.49%, outperforming existing methods in terms of performance. On the F BUSI dataset, its Dice coefficient is 96.76%, the Intersection over Union (IOU) is 95.92%, and the accuracy rate is 95.87%, surpassing other methods once again. These results confirm the effectiveness and superiority of the model we proposed.

在医学图像分割和分类领域，深度学习模型可以自动提取特征并进行高性能推理，从而帮助医生进行高效、准确的自动化决策支持。然而，这些模型通常是针对单个任务进行训练的，这导致了诸如忽略任务相关性和较差的可伸缩性等限制。为了解决这些问题，我们提出了一种新的用于医学图像分割和分类的交替多任务分层网络（AMTH-Net）。该模型分为三个层次模块：病理学区域清晰度（PRC）作为辅助模块，增强分割和分类能力；多分辨率关注（MRA）分割模块，利用深度监督来关注不同分辨率水平的图像信息，以提高分割精度；级联多尺度信息（CMSI）分类模块，采用级联多尺度机制，逐步整合来自不同网络层的离散信息。从而提高分类性能。此外，我们引入了一种新的基于多梯度信息反馈（MGIF）的交替交互损失（AI-Loss）算法，以进一步提高模型的分割和诊断性能。我们在COVID CXR和F BUSI乳腺超声数据集上的实验表明，AMTH-Net在分割和分类任务上都取得了优异的性能。具体而言，在COVID胸片（COVID CXR）数据集上，AMTH-Net的Dice系数达到98.33%,Intersection over Union （IOU）为96.31%，准确率为91.49%，在性能上优于现有方法。在F BUSI数据集上，其Dice系数为96.76%,Intersection over Union （IOU）为95.92%，准确率为95.87%，再次超越其他方法。这些结果证实了我们提出的模型的有效性和优越性。

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引用次数: 0

MISA-Net: A Multi-Scale Feature Interaction Network for Brain Tumor Segmentation MISA-Net：一种用于脑肿瘤分割的多尺度特征交互网络

IF 5.6 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm

Pub Date : 2025-04-25 DOI: 10.1016/j.irbm.2025.100891

Xiaobao Liu , Junfeng Xia , Wenjuan Gu , Tingqiang Yao , Jihong Shen , Dan Tang

Background and Objective

Accurate segmentation of brain tumor images is crucial in medical auxiliary diagnosis. However, the complex morphology and ambiguous boundary contours of brain tumors pose significant challenges to precise segmentation.

Methods

To address these issues, we developed MISA-Net, which is based on enhanced multi-scale feature interactions and selective feature fusion attention. Initially, a Multi-Scale Feature Interaction (MSFI) module was implemented to enhance the interaction between features at different scales, resolving issues of misclassification in regions with complex tumor morphologies. Subsequently, a Selective Feature Fusion Attention (SFFA) mechanism was introduced to reduce the interference of redundant information in skip connections on crucial features.

Results

Experiments on the BraTS 2019 dataset show that MISA-Net achieved Dice coefficients of 80.02%, 88.86%, and 86.02% in the enhancing, core, and whole tumor areas, respectively. Additionally, the Dice coefficient for the whole tumor area impressively reached 90.33% on the Kaggle LGG dataset; the Dice coefficient for the whole tumor area impressively reached 84.97% on the Figshare dataset.

Conclusions

Compared to existing mainstream models, MISA-Net demonstrates superior performance in brain tumor segmentation tasks, highlighting its potential and advantages in clinical diagnosis and treatment.

背景与目的脑肿瘤图像的准确分割是医学辅助诊断的关键。然而，脑肿瘤的复杂形态和模糊的边界轮廓给精确分割带来了很大的挑战。方法为了解决这些问题，我们开发了基于增强的多尺度特征交互和选择性特征融合注意的MISA-Net。首先，实现多尺度特征交互（Multi-Scale Feature Interaction， MSFI）模块，增强不同尺度特征之间的交互作用，解决肿瘤形态复杂区域的误分类问题。随后，引入了选择性特征融合注意（SFFA）机制，以减少关键特征上的跳过连接中冗余信息的干扰。结果在BraTS 2019数据集上的实验表明，MISA-Net在增强区、核心区和全区分别实现了80.02%、88.86%和86.02%的Dice系数。此外，在Kaggle LGG数据集上，整个肿瘤区域的Dice系数达到了90.33%；在Figshare数据集上，整个肿瘤区域的Dice系数达到了令人印象深刻的84.97%。结论与现有主流模型相比，MISA-Net在脑肿瘤分割任务中表现优异，在临床诊断和治疗中具有一定的潜力和优势。

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引用次数: 0

Case-Based Simulation to Support Complex Active Catheterization: Preliminary Results 基于病例的模拟以支持复杂的主动导管：初步结果

IF 5.6 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm

Pub Date : 2025-04-10 DOI: 10.1016/j.irbm.2025.100890

Arif Badrou , Aurélien de Turenne , Nathan Lescanne , Jérôme Szewczyk , Raphaël Blanc , Nahiène Hamila , Nicolas Tardif , Aline Bel-Brunon , Pascal Haigron

Objective

Active catheters are intended to support endovascular navigation in complex anatomies. Nevertheless, their configuration and utilization are challenging. Finite element (FE) modeling representing the navigation of active guidewires alongside catheters can be considered at an early stage to identify the best parameters and support physicians in their planning and procedure. However, FE simulations require significant computation time. We introduce the concept of case-based simulation (CBS) to quickly find adequate configuration parameters for complex catheterization scenarios.

Method

Combining case-based reasoning and FE simulation, CBS approach is considered to reuse design and navigation parameters from previous simulations. A case base is made of successful catheterization simulations performed on reference aorta geometries. For a new patient, a distance metric based on a statistical shape model is used to determine appropriate catheterization parameters from previously simulated cases. The proof-of-concept of this method is performed in the case of the navigation from the aortic arch to the left carotid artery. Among 11 patient-specific aortic arches, three were selected for the reference FE simulations of the left carotid artery hooking to constitute the case base and three others were selected for evaluation.

Results

The retrieved parameters allowed a successful simulated navigation in 100% of the test cases. This demonstrates that the proposed approach can effectively and instantaneously determine appropriate design and navigation parameters for complex catheterization scenarios.

目的主动导管用于复杂解剖结构的血管内导航。然而，它们的配置和利用是具有挑战性的。有限元（FE）建模可以在早期阶段考虑主动导丝与导管的导航，以确定最佳参数并支持医生的计划和程序。然而，有限元模拟需要大量的计算时间。我们引入了基于病例的模拟（CBS）的概念，以便为复杂的导管场景快速找到适当的配置参数。方法将基于实例的推理与有限元仿真相结合，采用CBS方法重用前人仿真中的设计参数和导航参数。一个案例基础是成功的导管模拟执行参考主动脉几何形状。对于新患者，基于统计形状模型的距离度量用于从先前模拟的病例中确定适当的导管参数。这种方法的概念证明是在从主动脉弓到左颈动脉的导航中进行的。在11例患者特异性主动脉弓中，选择3例作为左颈动脉钩的参考有限元模拟，构成病例库，选择另外3例进行评价。结果检索的参数允许在100%的测试用例中成功模拟导航。这表明，所提出的方法可以有效和即时地确定适当的设计和导航参数的复杂的导管场景。

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引用次数: 0

Patchwise Trabecular Bone Reconstruction of a 2D Proximal Femur Using Deep Learning and Seamless Quilting Algorithm 基于深度学习和无缝拼接算法的二维股骨近端骨块重建

IF 5.6 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm

Pub Date : 2025-04-08 DOI: 10.1016/j.irbm.2025.100889

Bong Ju Chun , Sang Min Sin , Hyukjin Koh , Jung Jin Kim , In Gwun Jang

Background and Objective

Current in vivo imaging modalities such as CT and MRI provide low-resolution (LR) skeletal images of a limited resolution (400 to 600 μm), which is insufficient to precisely evaluate bone strength. Similarly, recent deep learning technologies show a limitation in terms of upscale ratio and image size. They also require a large number of high-resolution (HR) reference images for training, which are unavailable to acquire in clinical practice. Although topology optimization shows the potential to reconstruct HR skeletal images from CT scan data, it requires extreme computing cost for a limited region of interest (ROI). The goal of this study is to acquire a 2D HR full proximal femur image by reconstructing HR patch images via a deep neural network and merging them seamlessly.

Methods

Topology optimization was conducted to generate synthetic proximal femur images. After these HR images were downscaled 10 times, finite element analysis was conducted to evaluate the structural behavior of the downscaled LR images. By dividing the proximal femur images into a set of patches which share their cut boundary, we could generate a total of 52,000 pairs of the HR and LR image patches and the LR structural behavior (nodal displacement in this study). Then, these patch-wise data were used to train three different deep neural networks: ResNet, U-Net, and SRGAN. Finally, after the HR patch images were upscaled 10 times by the trained networks, they were seamlessly merged by minimizing a structural discontinuity on the patch boundary.

Results

The reconstructed HR proximal femur images were evaluated at three different ROIs in terms of image quality, apparent stiffness, and trabecular morphometric indices. They showed characteristic trabecular patterns with no visible structural discontinuity between the patches in all ROIs. Among three networks, ResNet showed the best performance in all quantitative measures.

Conclusion

This study proposes a novel framework that incorporates deep learning-based patchwise reconstruction and seamless quilting algorithm. Because the proposed method requires a very small number of reference HR images (only 11 synthetic full proximal femur images in total), it could be expanded to reconstruct trabecular bone from 3D clinical CT scan data for more reliable bone strength assessment in clinical practice.

背景和目的目前的体内成像方式，如CT和MRI提供的低分辨率（LR）骨骼图像分辨率有限（400至600 μm），这不足以准确评估骨骼强度。同样，最近的深度学习技术在高端比例和图像大小方面也存在局限性。它们还需要大量的高分辨率（HR）参考图像进行训练，而这些图像在临床实践中是无法获得的。尽管拓扑优化显示了从CT扫描数据重建HR骨骼图像的潜力，但对于有限的感兴趣区域（ROI），它需要极高的计算成本。本研究的目的是通过深度神经网络重建HR补丁图像并无缝合并，获得二维HR近端全股骨图像。方法采用形态学优化方法合成股骨近端图像。将这些HR图像缩小10倍后，进行有限元分析以评估缩小后的LR图像的结构行为。通过将股骨近端图像划分为一组共享其切割边界的斑块，我们可以生成总共52,000对HR和LR图像斑块和LR结构行为（本研究的节点位移）。然后，这些数据被用于训练三种不同的深度神经网络：ResNet、U-Net和SRGAN。最后，经过训练的网络将HR patch图像放大10倍后，通过最小化patch边界上的结构不连续，实现了HR patch图像的无缝合并。结果对重建的股骨近端HR图像在三种不同roi下的图像质量、表观刚度和小梁形态计量指标进行评价。他们表现出典型的小梁模式，在所有roi中斑块之间没有可见的结构不连续。在三个网络中，ResNet在所有定量测量中表现最佳。本研究提出了一种融合了基于深度学习的拼接重建和无缝拼接算法的框架。由于所提出的方法只需要非常少的参考HR图像（总共只有11张合成的股骨近端完整图像），因此可以扩展到从临床CT三维扫描数据重建小梁骨，从而在临床实践中更可靠地评估骨强度。

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引用次数: 0

An Approach to Compute Fetal Cardiac Biomarkers from the Abdominal Electrocardiogram 从腹部心电图计算胎儿心脏生物标志物的方法

IF 5.6 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm

Pub Date : 2025-03-14 DOI: 10.1016/j.irbm.2025.100886

Paula Romina Soria , Pablo Daniel Cruces , César Federico Caiafa , Pedro David Arini

Objective: The fetal electrocardiogram (FECG) can be recorded from the

20^{t h}

week of gestation. The aim of this work is to determine fetal cardiac biomarkers from non-invasive cardiac signals that may be useful in the assessment of fetal health. Methods: We have developed an algorithm to obtain FECG fiducial points. It started by discriminating fetal heartbeats based on the relative location between fetal and maternal QRS complexes. An average beat is derived from the abdominal electrocardiogram (AECG) using 20 beats with a correlation greater than 0.95 and stable RR-interval, based on data from 12 fetuses (

38^{t h}

-

42^{n d}

weeks). We have implemented a combination between quaternion algebra and principal component analysis (Q-PCA method) to determine the onset and end of FECG waves by analyzing the angular velocity of the heart electrical vector. To validate our findings, we compared them with measurements obtained from the direct fetal electrocardiogram (DFECG), as a benchmark. Results: The values calculated by the Q-PCA method, as well as their correlation and the p-value in relation to the DFECG, were as follows: PR interval:

125.1 \pm 19.8

ms (

ρ = 0.97

, p

< 2.39 e - 7

), QRS complex:

73.0 \pm 4.4

ms (

ρ = 0.67

, p

< 1.74 e - 2

), QT interval:

261.1 \pm 28.5

ms (

ρ = 0.84

, p

< 7.05 e - 4

) and QTc interval:

388.3 \pm 35.9

ms (

ρ = 0.79

, p

< 2.27 e - 3

). Conclusion: Given its importance and the measurement performance achieved, the methodology presented represents a significant potential tool for improving the diagnosis of fetal health.

目的：从妊娠第20周开始记录胎儿心电图。这项工作的目的是从非侵入性心脏信号中确定胎儿心脏生物标志物，这可能对评估胎儿健康有用。方法：我们开发了一种获取FECG基准点的算法。它首先根据胎儿和母体QRS复合物之间的相对位置来区分胎儿的心跳。根据12个胎儿（38 - 42周）的数据，通过20次腹部心电图（AECG）得出平均心跳，相关性大于0.95，rr -间隔稳定。我们实现了四元数代数和主成分分析（Q-PCA）的结合，通过分析心脏电矢量的角速度来确定feg波的开始和结束。为了验证我们的发现，我们将其与直接胎儿心电图（DFECG）作为基准进行了比较。结果：用Q-PCA方法计算的值及其与DFECG的相关性和p值分别为：PR区间：125.1±19.8 ms (ρ=0.97, p<2.39e−7)，QRS复合体：73.0±4.4 ms (ρ=0.67, p<1.74e−2)，QT间期：261.1±28.5 ms (ρ=0.84, p<7.05e−4)，QTc区间：388.3±35.9 ms （ρ=0.79, p<2.27e−3）。结论：鉴于其重要性和测量性能的实现，所提出的方法是一个重要的潜在工具，以提高胎儿健康的诊断。

{"title":"An Approach to Compute Fetal Cardiac Biomarkers from the Abdominal Electrocardiogram","authors":"Paula Romina Soria , Pablo Daniel Cruces , César Federico Caiafa , Pedro David Arini","doi":"10.1016/j.irbm.2025.100886","DOIUrl":"10.1016/j.irbm.2025.100886","url":null,"abstract":"<div><div><strong>Objective:</strong> The fetal electrocardiogram (FECG) can be recorded from the <span><math><msup><mrow><mn>20</mn></mrow><mrow><mi>t</mi><mi>h</mi></mrow></msup></math></span> week of gestation. The aim of this work is to determine fetal cardiac biomarkers from non-invasive cardiac signals that may be useful in the assessment of fetal health. <strong>Methods:</strong> We have developed an algorithm to obtain FECG fiducial points. It started by discriminating fetal heartbeats based on the relative location between fetal and maternal QRS complexes. An average beat is derived from the abdominal electrocardiogram (AECG) using 20 beats with a correlation greater than 0.95 and stable RR-interval, based on data from 12 fetuses (<span><math><msup><mrow><mn>38</mn></mrow><mrow><mi>t</mi><mi>h</mi></mrow></msup></math></span> - <span><math><msup><mrow><mn>42</mn></mrow><mrow><mi>n</mi><mi>d</mi></mrow></msup></math></span> weeks). We have implemented a combination between quaternion algebra and principal component analysis (Q-PCA method) to determine the onset and end of FECG waves by analyzing the angular velocity of the heart electrical vector. To validate our findings, we compared them with measurements obtained from the direct fetal electrocardiogram (DFECG), as a benchmark. <strong>Results:</strong> The values calculated by the Q-PCA method, as well as their correlation and the p-value in relation to the DFECG, were as follows: <em>PR</em> interval: <span><math><mn>125.1</mn><mo>±</mo><mn>19.8</mn></math></span> ms (<span><math><mi>ρ</mi><mo>=</mo><mn>0.97</mn></math></span>, p<span><math><mo><</mo><mn>2.39</mn><mi>e</mi><mo>−</mo><mn>7</mn></math></span>), <em>QRS</em> complex: <span><math><mn>73.0</mn><mo>±</mo><mn>4.4</mn></math></span> ms (<span><math><mi>ρ</mi><mo>=</mo><mn>0.67</mn></math></span>, p<span><math><mo><</mo><mn>1.74</mn><mi>e</mi><mo>−</mo><mn>2</mn></math></span>), <em>QT</em> interval: <span><math><mn>261.1</mn><mo>±</mo><mn>28.5</mn></math></span> ms (<span><math><mi>ρ</mi><mo>=</mo><mn>0.84</mn></math></span>, p<span><math><mo><</mo><mn>7.05</mn><mi>e</mi><mo>−</mo><mn>4</mn></math></span>) and <em>QTc</em> interval: <span><math><mn>388.3</mn><mo>±</mo><mn>35.9</mn></math></span> ms (<span><math><mi>ρ</mi><mo>=</mo><mn>0.79</mn></math></span>, p<span><math><mo><</mo><mn>2.27</mn><mi>e</mi><mo>−</mo><mn>3</mn></math></span>). <strong>Conclusion:</strong> Given its importance and the measurement performance achieved, the methodology presented represents a significant potential tool for improving the diagnosis of fetal health.</div></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"46 3","pages":"Article 100886"},"PeriodicalIF":5.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Objective Assessment of Pull Test Scores in Parkinson's Disease Under Dynamic Conditions 目的评价动态条件下帕金森病患者的拉力测试得分

IF 5.6 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm

Pub Date : 2025-03-05 DOI: 10.1016/j.irbm.2025.100884

Marta Cardoso , Cristiana Pinheiro , Helena R. Gonçalves , Ana Margarida Rodrigues , Cristina P. Santos

Background

Postural instability is considered one of the most incapacitating motor symptoms and a primary cause of falls in Parkinson's disease (PD), compromising patients' autonomy and well-being. The traditional clinical examination used to evaluate this symptom designed by pull test is difficult to standardize and is not sensitive to subtle but significant postural changes. Inertial measurement units have emerged as a portable and cost-effective solution to measure on-body patients' postural sway allowing them to obtain more sensitive metrics able to capture postural instability. However, further studies are required to monitor patients' postural conditions under dynamic conditions.

Methods

The proposed research focused on investigating the hypothesis of whether it is possible to differentiate between all the scores of the pull test through postural and gait metrics extracted from raw acceleration and angular velocity signals from the centre of mass of patients with PD acquired while performing basic daily tasks. A new cross-sectional study was conducted with 23 patients to determine which gait and postural-associated metrics are considered significant to distinguish between the different levels of pull test, and which metrics are more correlated with the pull test score.

Results

Achieved results showed that most of the estimated metrics can differentiate the pull test scores (

ρ -value \leq 0.048

, R

{}^{2}\geq 0.513

). The duration of the activity, root-mean-square and range of motion of vertical and mediolateral angular velocity, as also most of the gait-associated metrics, presented the most significant differences in all trials which involved motion tasks, such as sitting, lying, walking and turning.

Conclusions:

Overall, promising results were achieved as the statistical analysis revealed that gait and postural metrics estimated under dynamic conditions were considered relevant to distinguish between the scores of the pull test.

背景姿势不稳定被认为是最令人丧失能力的运动症状之一，也是帕金森病（PD）患者跌倒的主要原因，损害了患者的自主性和幸福感。传统的临床检查通过拉力测试来评估这一症状，但这种方法很难标准化，而且对细微但显著的姿势变化不敏感。惯性测量装置作为一种便携式、经济高效的解决方案出现，可用于测量患者的体位摇摆，从而获得更灵敏的指标，捕捉体位不稳定性。该研究的假设是，是否有可能通过从帕金森病患者在执行基本日常任务时从质心获得的原始加速度和角速度信号中提取的姿势和步态指标来区分拉力测试的所有得分。对 23 名患者进行了一项新的横断面研究，以确定哪些步态和姿势相关指标被认为对区分不同级别的牵拉试验具有重要意义，以及哪些指标与牵拉试验得分的相关性更高。活动持续时间、垂直和内外侧角速度的均方根和运动范围，以及大多数与步态相关的指标，在所有涉及运动任务（如坐、卧、行走和转身）的试验中都显示出最显著的差异。

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引用次数: 0

Unlocking Cognitive Potential: Exploring a Virtual Environment for Cognitive Training in Healthy Aging and Mild Cognitive Impairment 释放认知潜能：探索健康老年和轻度认知障碍认知训练的虚拟环境

IF 5.6 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm

Pub Date : 2025-03-05 DOI: 10.1016/j.irbm.2025.100885

Pierre-Alexandre Andrieu-Devilly , Marc Gandit , Didier Schwab , Lisa Quillion-Dupré , Emmanuel Monfort

Objective

This study aims to investigate how cognitive impairment and social presence influence goal attainment in an ecological virtual environment. It also examines the role of interactive features in improving computer-assisted cognitive training for older adults, both with and without mild cognitive impairment (MCI).

Materials and Methods

A virtual supermarket was used to simulate a realistic exploration task, incorporating social interactions and adaptive assistance strategies. Error analysis was conducted to identify performance patterns linked to cognitive profiles.

Results

Participants with MCI exhibited a significantly higher frequency of wandering and uncorrected orientation errors, compared to cognitively healthy older adults. While verbal support was beneficial in facilitating virtual task progress, it did not fully mitigate performance deficits in those with MCI. Additionally, all older participants, regardless of cognitive status, reported significantly lower perceptions of social presence compared to younger participants.

Conclusion

Virtual environments constitute a promising tool for the assessment and enhancement of functional abilities in older adults with neurocognitive impairments. The integration of tailored cognitive training protocols and adaptive support strategies holds potential to optimize cognitive stimulation and task performance.

目的探讨虚拟生态环境中认知障碍和社会存在对目标实现的影响。它还研究了互动功能在改善老年人计算机辅助认知训练方面的作用，包括有和没有轻度认知障碍（MCI）的老年人。材料与方法利用虚拟超市模拟现实探索任务，结合社会互动和适应性援助策略。错误分析是为了确定与认知概况相关的表现模式。结果与认知健康的老年人相比，MCI患者表现出更高频率的徘徊和未纠正的方向错误。虽然口头支持有助于促进虚拟任务的进展，但它并不能完全缓解轻度认知障碍患者的表现缺陷。此外，与年轻参与者相比，所有年龄较大的参与者，无论认知状况如何，都报告了明显较低的社会存在感。结论虚拟环境是评估和增强老年神经认知障碍患者功能能力的有效工具。整合定制的认知训练协议和适应性支持策略具有优化认知刺激和任务表现的潜力。

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引用次数: 0

Facial Palsy Characterization Using Dual Regression Trees 使用对偶回归树表征面瘫

IF 5.6 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm

Pub Date : 2025-02-20 DOI: 10.1016/j.irbm.2025.100882

Soualmi Ameur , Mohd Saquib Khan , Régis Fournier , Marina Guihard , Laurent Chatelain , Marjolaine Baude , Amine Nait-ali

1) Objectives: The current facial recognition tools are inefficient in predicting landmarks for facial palsy patients. Noticeable asymmetry in the face results in inaccurate results as the prediction models are trained on symmetrical faces. In this study, a method is proposed which takes advantage of the existing powerful machine learning tools which are trained on datasets of healthy subjects with symmetric facial movements to create a system that can analyze and localize facial landmarks on both healthy as well as facial palsy subjects.

2) Methods: The task is accomplished by a simple image processing algorithm where two symmetric faces are generated from a non-symmetric face image representing the left and right sides of the original image. This method was tested against two other methods. One, which uses the cascade of regression trees (CRT) algorithm and the other which is a retrained version of the CRT algorithm on a dataset of facial palsy cases called Massachusetts Eye and Ear database and model (MEE).

3) Results: The methods were compared on 3 different types of test datasets containing a total 125 images. The proposed method outperforms other two methods in cases of asymmetrical faces from healthy people and palsy patients with approximately 7% lesser error compared to the CRT method and 39% lesser error than the MEE method.

4) Conclusion: The proposed method had a considerably better performance compared to the other two methods, which opens new perspectives to address the problem of face landmarks localization problem on facial palsy cases.

1)目的：目前的面部识别工具在面瘫患者地标预测方面效率低下。由于预测模型是在对称的面部上训练的，因此面部明显的不对称会导致预测结果不准确。在本研究中，我们提出了一种方法，利用现有强大的机器学习工具，在具有对称面部运动的健康受试者数据集上进行训练，创建一个可以分析和定位健康和面瘫受试者面部标志的系统。该任务通过一种简单的图像处理算法完成，该算法从非对称的人脸图像中生成两个对称的人脸，分别表示原始图像的左右两侧。这种方法与另外两种方法进行了对比试验。一种是使用级联回归树（CRT）算法，另一种是在面瘫病例数据集马萨诸塞州眼耳数据库和模型（MEE）上对CRT算法进行再训练。3)结果：在3种不同类型的测试数据集上进行比较，共包含125张图像。该方法在健康人群和瘫痪患者面部不对称情况下的定位误差比CRT法小约7%，比MEE法小39%，优于其他两种方法。4)结论：该方法的定位误差明显优于其他两种方法，为解决面瘫患者面部标志定位问题开辟了新的视角。

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引用次数: 0

Complementary Split-Ring Resonator for Non-Invasive Diagnosis of Carotid Artery Atherosclerosis: Towards Future in-Vivo Measurements 用于颈动脉粥样硬化无创诊断的互补裂环谐振器：走向未来的体内测量

IF 5.6 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm

Pub Date : 2025-02-14 DOI: 10.1016/j.irbm.2025.100883

Joséphine Dupeyron Masini , Frédérique Deshours , Georges Alquie , Rania Shahbaz , Sylvain Feruglio , Olivier Meyer , Dimitri Galayko , Hamid Kokabi , Jean-Michel Davaine

Objectives

The limited penetration depth of electromagnetic (EM) waves into biological tissues is a significant challenge for the use of microwave sensors in medical diagnostics. This study proposes a sensor based on a complementary split-ring resonator (CSRR) for the non-invasive detection of carotid atherosclerotic plaques, designed to be placed on the patient's neck.

Material and methods

The sensor employs a widened feed line and an optimized sensing area to concentrate the electric field and store a significant amount of energy within the biological tissue. Validation includes EM simulations and ex-vivo measurements on fresh animal tissues using monolayer and multilayer configurations to simulate human neck anatomy. A three-dimensional carotid artery model is also introduced to extend the analysis to deeper tissue layers and simulate different degrees of stenosis between 25% and 75%.

Results

The sensor demonstrates a sensitivity of 0.72% and a detection resolution of 14 MHz for a dielectric constant range from 1 to 52 in material measurements, which has contributed to enhancing the EM penetration depth in neck tissues. Simulation results for atherosclerotic plaques in the carotid artery revealed a frequency shift difference induced by stable and vulnerable plaques of around 1 to 2 MHz.

Conclusion

These findings highlight the sensor's potential for future use in the in- vivo diagnosis of carotid artery atherosclerosis.

目的电磁（EM）波对生物组织的穿透深度有限是微波传感器在医学诊断中应用的一个重大挑战。本研究提出了一种基于互补裂环谐振器（CSRR）的传感器，用于无创检测颈动脉粥样硬化斑块，设计用于患者颈部。材料和方法该传感器采用加宽的馈线和优化的传感区域来集中电场并在生物组织内存储大量能量。验证包括EM模拟和新鲜动物组织的离体测量，使用单层和多层配置来模拟人体颈部解剖。还引入了三维颈动脉模型，将分析扩展到更深的组织层，并模拟25%至75%之间不同程度的狭窄。结果该传感器在介电常数1 ~ 52范围内的材料测量灵敏度为0.72%，检测分辨率为14 MHz，有助于提高颈部组织的电磁穿透深度。对颈动脉粥样硬化斑块的模拟结果显示，稳定斑块和易损斑块引起的频移差异约为1至2 MHz。结论该传感器在颈动脉粥样硬化的体内诊断中具有广阔的应用前景。

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引用次数: 0

Comparison of Different Sensor Locations on Freezing-of-Gait Ratio Results 不同传感器位置对步态冻结率结果的比较

IF 5.6 4区医学 Q1 ENGINEERING, BIOMEDICAL

Irbm

Pub Date : 2025-02-14 DOI: 10.1016/j.irbm.2025.100881

Slavka Netukova , Lucie Horakova , Evžen Růžička , Petr Dusek , Zoltan Szabo , Radim Krupička

Background

Freezing of gait (FoG) is a walking disturbance in the Parkinson's disease (PD). The freezing ratio (FoG-ratio) is a parameter used to quantify overall freezing severity rather than to assess single freezing episodes. Originally the FoG-ratio was designed to be computed from lower limb acceleration. However, some available measurement systems get their data from a single sensor located elsewhere, e.g. on the lower back.

Purpose

The objective of our paper is to analyse whether acceleration signals measured on different body locations result in a consistent FoG-ratio.

Methods

Eighty-four people with PD and 65 people without neurological disorders completed an instrumented Timed Up&Go Test (iTUG) twice. The FoG-ratios from inertial units placed on the chest, lower back, left and right lower limbs were calculated.

Findings

There were significant differences between the tested FoG-ratios in the control group as well as in the PD group for both segments. Four significant, but not consistent, correlations were revealed for the turn segment in the PD group. Eight correlations were revealed in the control group. The inter-trial reliability of all the tested cases for gait was good (rho>0.75) but only in one case for turning.

Conclusion

In conclusion, the placement of sensors affected the FoG-ratio parameter output. The different FoG-ratios reflect different amounts of power in the locomotion band of body segments. This could result in inconclusive validity and incomparability of freezing severity presented in studies when the sensor is placed somewhere other than on the lower limbs.

步态冻结（FoG）是帕金森病（PD）的一种行走障碍。冻结率（FoG-ratio）是一个用于量化整体冻结严重程度的参数，而不是评估单个冻结事件。最初设计的fog比率是根据下肢加速度计算的。然而，一些可用的测量系统从位于其他地方的单个传感器获得数据，例如在背部下部。本文的目的是分析在不同身体位置测量的加速度信号是否会产生一致的加速度加速度比。方法84例PD患者和65例非神经系统疾病患者分别进行了2次仪器定时起跳测试（iTUG）。计算了放置在胸部、下背部、左下肢和右下肢的惯性单元的fog -ratio。结果：在对照组和PD组的两个节段测试的fogg比率之间存在显著差异。PD组的转弯节段有四个显著但不一致的相关性。对照组有8项相关。所有测试病例的步态试验间信度都很好（rho>0.75），但只有一个病例的转弯试验间信度良好。结论传感器的放置位置影响FoG-ratio参数输出。不同的fog -ratio反映了身体各节段运动带的不同功率量。当传感器放置在下肢以外的其他地方时，这可能导致研究中提出的冻结严重程度的不确定性有效性和不可比较性。

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