Medical Engineering & Physics最新文献_第9页

Heart rate estimation for U-Net and LSTM models combining multiple attention mechanisms 结合多注意机制的U-Net和LSTM模型的心率估计

IF 2.3 4区医学 Q3 ENGINEERING, BIOMEDICAL

Medical Engineering & Physics

Pub Date : 2025-08-10 DOI: 10.1016/j.medengphy.2025.104406

Ahui Li , Jun Cai

Accurate heart rate (HR) monitoring is pivotal in modern medical technology, particularly for disease prevention and health management. This study proposes a novel HR estimation framework that leverages advanced deep learning techniques to accurately extract HR information from noisy photoplethysmography (PPG) signals. The proposed model, termed DRL-Unet, integrates a Denoising Autoencoder (DAE), U-Net architecture, and Long Short-Term Memory (LSTM) networks. To further enhance robustness and precision, the model incorporates a Multi-Head Attention mechanism and Residual Network (ResNet) modules. Comparative experiments demonstrate that DRL-Unet outperforms conventional deep learning models, achieving a Mean Absolute Error (MAE) of 1.69 bpm, Mean Squared Error (MSE) of 3.05 bpm², Root Mean Squared Error (RMSE) of 1.71 bpm, Mean Absolute Percentage Error (MAPE) of 1.15%, and Bias of 0.05 bpm. The model's effectiveness is validated on a public dataset from the IEEE Signal Processing Cup, confirming its superior performance under complex noise conditions. These findings highlight the potential of DRL-Unet to significantly improve the accuracy and reliability of HR estimation, thereby offering valuable advancements for early cardiovascular disease diagnosis and continuous health monitoring.

准确的心率（HR）监测是现代医疗技术的关键，特别是对疾病预防和健康管理。本研究提出了一种新的人力资源估计框架，该框架利用先进的深度学习技术从噪声光容积脉搏波（PPG）信号中准确提取人力资源信息。该模型被称为DRL-Unet，集成了去噪自动编码器（DAE）、U-Net架构和长短期记忆（LSTM）网络。为了进一步提高鲁棒性和精度，该模型结合了多头注意机制和残余网络（ResNet）模块。对比实验表明，DRL-Unet优于传统的深度学习模型，平均绝对误差（MAE）为1.69 bpm，均方误差（MSE）为3.05 bpm²，均方根误差（RMSE）为1.71 bpm，平均绝对百分比误差（MAPE）为1.15%，偏差为0.05 bpm。该模型的有效性在IEEE信号处理杯的公共数据集上得到了验证，证实了其在复杂噪声条件下的优越性能。这些发现突出了DRL-Unet在显著提高HR估计的准确性和可靠性方面的潜力，从而为早期心血管疾病诊断和持续健康监测提供了有价值的进展。

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引用次数: 0

Technique for three-dimensional assessment of prosthesis alignment after radial head arthroplasty: A technical note 桡骨头置换术后假体对准的三维评估技术：技术说明

IF 2.3 4区医学 Q3 ENGINEERING, BIOMEDICAL

Medical Engineering & Physics

Pub Date : 2025-08-08 DOI: 10.1016/j.medengphy.2025.104413

Ausberto R. Velasquez Garcia , Adam J. Wentworth , Jennifer M. Oettinger , James S. Fitzsimmons , Jeffrey F. Marsh Jr. , Mark E. Morrey , Shawn W. O'Driscoll

Objective

To present a novel evaluation technique for assessing three-dimensional (3D) prosthesis alignment after radial head arthroplasty (RHA) and to identify potential measurement errors associated with this method.

Materials/Methods

Virtual surgical planning of a simulated irreparable fracture of the radial head was performed to select and place optimal implants. Of the six 3D-printed bone models, three were fitted with 3D-printed implants and three with metallic implants. After the procedure, 3D models were derived from 3D scans and dual-energy computed tomography with and without metal artifact reduction. Deviations in rotation and translation from the pre-procedure plan as well as measurement errors were assessed.

Results

The technique demonstrated the ability to accurately identify minor deviations in prosthesis alignment post-RHA. Deviations ranged from 0 to 14° in rotation and 0 to 1.3 mm in translation. The method also showed high measurement accuracy against 3D reference models, with mean rotational errors of 0.3–0.5° and translation errors of 0.1–0.3 mm.

Conclusion

This technique provides an accurate and precise method for assessing prosthesis alignment in RHA, with minimal measurement errors. Its potential as a valuable clinical tool has substantial implications in improving preoperative planning and postoperative evaluation. Further validation and advancements in reducing operator dependency are necessary for clinical adoption.

目的介绍一种评估桡骨头置换术（RHA）后假体三维定位的新方法，并确定该方法可能存在的测量误差。材料/方法通过模拟桡骨头不可修复骨折的虚拟手术计划来选择和放置最佳的植入物。在6个3d打印的骨模型中，3个安装了3d打印的植入物，3个安装了金属植入物。手术后，通过三维扫描和双能计算机断层扫描获得三维模型，并进行金属伪影还原。对术前计划的旋转和平移偏差以及测量误差进行了评估。结果该技术能够准确识别rha后假体对准的微小偏差。旋转偏差为0 ~ 14°，平移偏差为0 ~ 1.3 mm。该方法相对于三维参考模型具有较高的测量精度，平均旋转误差为0.3-0.5°，平移误差为0.1-0.3 mm。结论该技术为RHA假体对准评估提供了一种准确、精确的方法，测量误差最小。它作为一种有价值的临床工具的潜力在改善术前计划和术后评估方面具有重大意义。进一步的验证和进展，以减少操作者的依赖是必要的临床采用。

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引用次数: 0

An adaptive bin-stream network based on frequency decomposition for classifying atrial fibrillation with low SNR data 基于频率分解的自适应双流网络在低信噪比房颤分类中的应用

IF 2.3 4区医学 Q3 ENGINEERING, BIOMEDICAL

Medical Engineering & Physics

Pub Date : 2025-08-07 DOI: 10.1016/j.medengphy.2025.104412

Jilin Wang , Tengqun Shen , Mengfan Li , Yijun Ma , Guozhen Sun , Yatao Zhang

To detect atrial fibrillation (AF) in ECG signals with low signal-to-noise ratio (SNR), this study introduces the adaptive bin-stream network (ABNet) based on frequency decomposition. The ABNet offers notable advantages: it exhibits high robustness in identifying AF amidst noisy environments, it decomposes the ECG signals into 32-frequency channel recordings to refine frequency ranges for better identifying AF, and it designs an adaptive bin-stream network to gain the optimal results. The method utilizes a 5-level Haar wavelet packet decomposition to decompose the preprocessed ECG signals into their corresponding 32-frequency channel recordings, and the preprocessing signals and the recordings are fed into waveform stream and frequency stream of the bin-stream network, respectively. Finally, an adaptive approach is employed to obtain the optimal classification results. The ABNet was validated for the PhysioNet/Computing in Cardiology Challenge 2017 database (CinC 2017 Db) to classify 4 categories i.e., normal sinus rhythm (N), AF, other abnormal rhythms (O) and noise (P), and it achieved accuracy (acc) 93.08 %, precision (ppv) 78.68 %, sensitivity (sen) 81.84 %, specificity (spec) 94.00 %, and F₁ 0.8382. In addition, it achieved the acc 97.98, ppv 96.40, sen 98.37 %, spec 98.41 %, and F₁ 0.9595 for a synthetic Db consisting of Shandong provincial hospital AF database (SPH AF Db) and CinC 2011 Db for classifying 3 categories i.e., N, AF and P. These results underscore the effectiveness of the ABNet in capturing detailed information about waveform and different frequencies in ECG signals.

为了在低信噪比的心电信号中检测房颤，本研究引入了基于频率分解的自适应帧流网络（ABNet）。ABNet具有显著的优势：它在嘈杂环境中识别AF方面具有很高的鲁棒性，它将心电信号分解为32个频率通道记录以优化频率范围以更好地识别AF，并设计了自适应bin-stream网络以获得最佳结果。该方法利用5级Haar小波包分解将预处理后的心电信号分解为相应的32频通道记录，将预处理后的信号和记录分别送入bin-stream网络的波形流和频率流。最后，采用自适应方法获得最优分类结果。在PhysioNet/Computing in Cardiology Challenge 2017数据库（CinC 2017 Db）中对ABNet进行了验证，对正常窦性心律(N)、AF、其他异常心律(O)和噪声(P) 4类进行了分类，准确率（acc）为93.08%，精密度（ppv）为78.68%，灵敏度（sen）为81.84%，特异性（spec）为94.00%，F1为0.8382。此外，由山东省医院房颤数据库（SPH AF Db）和CinC 2011 Db组成的合成Db对N、AF和p 3个类别进行分类，其准确度为97.98，ppv为96.40，sen为98.37%，spec为98.41%，F1为0.9595，这些结果表明ABNet在捕获心电信号中波形和不同频率的详细信息方面是有效的。

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引用次数: 0

A user-defined element for simulating hydrogel injection into trabecular bone: Numerical simulations and experimental validation 一个用户定义的元素模拟水凝胶注射到小梁骨：数值模拟和实验验证

IF 2.3 4区医学 Q3 ENGINEERING, BIOMEDICAL

Medical Engineering & Physics

Pub Date : 2025-08-05 DOI: 10.1016/j.medengphy.2025.104411

Georgios F. Samaras , Vincent Dischl , Anita Fung , Vincent A. Stadelmann , Ulrike Kettenberger , Stephen J. Ferguson , Benedikt Helgason

In this study, we present a comprehensive numerical model to simulate the injection of hydrogel into femurs. The model is designed to capture the complex interactions between the hydrogel rheological properties and the biomechanical environment of the femur. The coupled mechanical-flow formulation, based on the Theory of Porous Media, is implemented in an open source Abaqus UEL subroutine, where displacements, pressure and saturation are the unknowns. The rheological properties of the hydrogel were calibrated against experimental augmentations in three femurs and the calibrated model was then applied to three different femurs where the hydrogel patterns were compared to experimental data. Furthermore, the simulations demonstrated the effect of injection flow rate and heterogeneous permeability on the hydrogel patterns and quantified the trabecular matrix's solid strains developed during the injection process. The simulations captured well the volume distribution with an average dice coefficient of 0.75 for the three tested specimens. In addition, the calculated solid strains were below the tensile yield limit for the tested flow rate range. A description of the constitutive equations and the implementation into an Abaqus user element subroutine is provided. Overall, our modeling methodology provides a computational tool that can be used to more accurately model bone augmentation and furthermore plan more safely the treatment of osteoporotic patients.

在这项研究中，我们提出了一个综合的数值模型来模拟水凝胶注入股骨。该模型旨在捕捉水凝胶流变特性与股骨生物力学环境之间复杂的相互作用。基于多孔介质理论的耦合力学-流动公式在开源的Abaqus UEL子程序中实现，其中排量、压力和饱和度是未知的。根据三个股骨的实验增强对水凝胶的流变特性进行校准，然后将校准模型应用于三个不同的股骨，并将水凝胶模式与实验数据进行比较。此外，模拟显示了注入流量和非均质渗透率对水凝胶形态的影响，并量化了注入过程中小梁基质的固体应变。模拟结果较好地反映了三种试样的体积分布，平均骰子系数为0.75。此外，计算出的固体应变低于测试流速范围内的拉伸屈服极限。给出了本构方程的描述及其在Abaqus用户元素子程序中的实现。总的来说，我们的建模方法提供了一种计算工具，可以用来更准确地模拟骨增强，并进一步计划更安全的骨质疏松症患者的治疗。

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引用次数: 0

Fast geometric deep learning for intraoperative soft tissue deformation estimation: Towards real-time AR guidance in liver surgery 快速几何深度学习用于术中软组织变形估计：面向肝脏手术实时AR引导

IF 2.3 4区医学 Q3 ENGINEERING, BIOMEDICAL

Medical Engineering & Physics

Pub Date : 2025-08-05 DOI: 10.1016/j.medengphy.2025.104409

Zixuan Zhai , Enpeng Wang , Xiaojun Chen

The real-time computation of the intraoperative spatial positioning of soft tissues, particularly those not visible within the body, such as blood vessels, is crucial for augmented reality navigation systems. Conventional biomechanical models face challenges in real-time computation and the acquisition of boundary conditions. A novel deep learning framework is proposed, integrating an optimized PointNet++ architecture for modelling liver and vascular deformation. The framework utilizes multi-scale feature extraction, lightweight self-attention mechanisms, and residual feature propagation to predict vascular displacement fields and normal vectors. A hybrid loss function that integrates Chamfer distance and MSE losses improves geometric consistency and deformation accuracy. The proposed approach, utilizing finite element method (FEM)-simulated datasets of liver stretching procedures, exhibits enhanced performance with root mean square errors (RMSE) of 2.78 ± 0.69 mm for hepatic veins and 1.81 ± 0.74 mm for portal veins. This method surpasses conventional techniques by 37.5% in accuracy and reduces inference time to 0.25 seconds. The optimized network exhibits a computation speed that is 83.9% faster than leading non-rigid registration algorithms. Subsequent tumour localization experiments demonstrate a targeting accuracy of 3.2 mm via vascular topology analysis, confirming clinical relevance. This research develops an effective framework for predicting deformation in real-time, providing a significant advancement for navigation in AR-guided hepatobiliary surgery.

术中软组织空间定位的实时计算，特别是那些在体内不可见的软组织，如血管，对于增强现实导航系统至关重要。传统的生物力学模型在实时计算和边界条件获取方面面临挑战。提出了一种新的深度学习框架，集成了优化的PointNet++架构，用于肝脏和血管变形建模。该框架利用多尺度特征提取、轻量级自关注机制和残差特征传播来预测血管位移场和法向量。混合损失函数集成了倒角距离和MSE损失，提高了几何一致性和变形精度。该方法利用有限元法（FEM）模拟肝脏拉伸过程的数据集，显示出增强的性能，肝静脉的均方根误差（RMSE）为2.78±0.69 mm，门静脉的均方根误差为1.81±0.74 mm。该方法的准确率比传统方法提高了37.5%，推理时间缩短到0.25秒。优化后的网络计算速度比现有的非刚性配准算法快83.9%。随后的肿瘤定位实验表明，通过血管拓扑分析，靶向精度为3.2毫米，证实了临床相关性。本研究开发了一种实时预测变形的有效框架，为ar引导的肝胆手术导航提供了重大进展。

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引用次数: 0

Comparative analysis of Electrospun PLA fibers incorporating bioactive glass nanoparticles: morphological, biological, and osteogenic properties for bone regeneration 含有生物活性玻璃纳米颗粒的静电纺PLA纤维的比较分析：形态学、生物学和骨再生的成骨特性

IF 2.3 4区医学 Q3 ENGINEERING, BIOMEDICAL

Medical Engineering & Physics

Pub Date : 2025-07-31 DOI: 10.1016/j.medengphy.2025.104410

Brunna da Silva Nobrega Souza , Lilian de Siqueira , Marina Santos Fernandes , Joyce Rodrigues de Souza , Elisa Camargo Kukulka , Letícia Adrielly Dias Grisante , Tiago Moreira Bastos Campos , Luana Marotta Reis de Vasconcellos , Alexandre Luiz Souto Borges

Polylactic acid (PLA) is widely studied for bone repair due to its biodegradability, biocompatibility, and bioresorbability. However, its limited bioactivity and hydrophobic surface hinder optimal cell interaction and integration. Incorporating bioactive glass (BG) particles into PLA scaffolds via electrospinning and electrospray techniques has emerged as a promising strategy to improve biological performance. This study aimed to fabricate and characterize PLA scaffolds, both with incorporated and surface-coated BG, and to assess their osteogenic potential for tissue engineering applications. Scaffold morphology was evaluated by scanning electron microscopy, and biological performance was assessed through in vitro assays using mesenchymal stem cells derived from Wistar rat bone marrow. Cell viability, total protein content, alkaline phosphatase (ALP) activity, and mineralized nodule formation were analyzed. The scaffolds displayed porous, interconnected structures with fiber diameters influenced by BG incorporation method. All groups demonstrated cytocompatibility, while scaffolds containing BG both incorporated and sprayed—showed significantly higher ALP activity, suggesting enhanced osteogenic differentiation. Mineralization nodules further confirmed the induction of osteogenesis. These findings highlight the potential of PLA/BG composite scaffolds, especially when functionalized via combined electrospinning and electrospray methods, as a promising platform for bone tissue engineering.

聚乳酸（PLA）由于其生物可降解性、生物相容性和生物可吸收性而被广泛研究用于骨修复。然而，其有限的生物活性和疏水表面阻碍了最佳的细胞相互作用和整合。通过静电纺丝和电喷雾技术将生物活性玻璃（BG）颗粒掺入聚乳酸支架中是一种很有前途的提高生物性能的策略。本研究旨在制备和表征PLA支架，包括掺入和表面包覆的BG，并评估其在组织工程中的成骨潜力。利用扫描电镜观察支架形态，利用Wistar大鼠骨髓间充质干细胞体外检测支架生物学性能。分析细胞活力、总蛋白含量、碱性磷酸酶（ALP）活性和矿化结节形成情况。受BG掺入法影响，支架呈现出多孔、互联的结构，纤维直径受到影响。所有组均表现出细胞相容性，而含有BG的支架（包括掺入和喷雾）均表现出更高的ALP活性，表明成骨分化增强。矿化结节进一步证实了骨生成的诱导作用。这些发现突出了PLA/BG复合支架的潜力，特别是当通过静电纺丝和电喷雾相结合的方法实现功能化时，作为骨组织工程的一个有前途的平台。

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引用次数: 0

Reduced model aided fluid-structure interaction design framework for shunt systems 简化模型辅助分流系统流固交互设计框架

IF 2.3 4区医学 Q3 ENGINEERING, BIOMEDICAL

Medical Engineering & Physics

Pub Date : 2025-07-28 DOI: 10.1016/j.medengphy.2025.104403

Elizabeth Hayman , Van Dung Nguyen , Ian S. McFarlane , Juliette Pech , Jayaratnam Jayamohan , José-Maria Peña Sánchez , Sarah Waters , Antoine Jerusalem

Traditionally, clinical devices are designed, tested and improved through lengthy and expensive laboratory experiments and clinical trials [1]. More recently, computational methods have allowed for rapid testing, speeding up the design process and enabling far more complete searches of design space. While computational models cannot fully capture the complexities of biological systems, they provide valuable insights into crucial underlying mechanisms, such as the effects of fluid-structure interactions (FSIs). In this paper we present a modular, partitioned, computational FSI pipeline whereby 2D reduced order models guide the 3D design of the problem of interest. This framework is applied to the problem of hydrocephalus shunt occlusion. Hydrocephalus is a medical condition characterised by an excess of cerebrospinal fluid (CSF) in the brain, and is commonly treated with the insertion of a shunt system. This system includes a ventricular catheter component – a hollow tube with inlet holes arranged in the tube wall close to the closed tip – which is positioned in the lateral ventricles of the brain. Despite recent improvements in the catheter material, this treatment still has high failure rates, most often due to the blockage of the catheter by the Choroid Plexus (ChP) tissue. We use an idealised FSI model to compare existing catheter designs by considering the deformation of the ChP under CSF flow in the ventricle environment in an hydrocephalus scenario. To the best of our knowledge, this is the first computational framework to directly incorporate the deformation of the ChP to discriminate between catheter designs. The faster 2D model is used in a comprehensive parameter sweep of the catheter design domain, and motivates a new design, then confirmed to be an improvement when tested in the full 3D domain. This approach demonstrates the success of using reduced order methods to guide the design of a more complex problem.

传统上，临床设备是通过漫长而昂贵的实验室实验和临床试验来设计、测试和改进的。最近，计算方法允许快速测试，加速设计过程，并使更完整的设计空间搜索成为可能。虽然计算模型不能完全捕捉生物系统的复杂性，但它们为关键的潜在机制提供了有价值的见解，例如流固相互作用（FSIs）的影响。在本文中，我们提出了一个模块化，分区，计算FSI管道，其中二维降阶模型指导感兴趣的问题的三维设计。该框架适用于脑积水分流闭塞的问题。脑积水是一种以脑脊液（CSF）过多为特征的医学病症，通常通过插入分流系统进行治疗。该系统包括一个心室导管组件——一个位于大脑侧脑室的空心管，其入口孔位于靠近封闭尖端的管壁上。尽管最近导管材料有所改进，但这种治疗仍然有很高的失败率，最常见的原因是脉络膜丛（ChP）组织阻塞导管。我们使用一个理想化的FSI模型来比较现有的导管设计，通过考虑脑积水情况下脑室环境中脑脊液流下ChP的变形。据我们所知，这是第一个直接结合ChP变形来区分导管设计的计算框架。更快的2D模型用于导管设计领域的综合参数扫描，并激发新的设计，然后在全3D领域进行测试时确认是一种改进。这种方法证明了使用降阶方法来指导更复杂问题的设计的成功。

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引用次数: 0

A numerical investigation of the kinematic and fluid dynamic behaviour of an intramuscular autoinjector designed for optimising injection efficiency 为优化注射效率而设计的肌肉内自动注射器的运动学和流体动力学行为的数值研究

IF 2.3 4区医学 Q3 ENGINEERING, BIOMEDICAL

Medical Engineering & Physics

Pub Date : 2025-07-26 DOI: 10.1016/j.medengphy.2025.104407

Sudesh Sivarasu , Ntokozo Magubane , Chibuike Mbanefo , Malebogo Ngoepe

The usability and versatility of autoinjectors in managing chronic and autoimmune diseases have made them increasingly attractive in medicine. However, investigations into autoinjector designs require an understanding of the kinematic properties and fluid behaviour during injection. To optimise injection efficiency, this study develops a mathematical and computational fluid dynamics (CFD) model of an IM autoinjector by investigating the effects of viscosity, needle length, needle diameter, and medication volume on the injection process. The model was verified and validated using a comparator experiment and optimised using a parameter sensitivity analysis. The mathematical model results show plunger displacement increases linearly in low viscous fluids (v < 20 cP), allowing faster injections. CFD simulations show that high-viscosity fluids (v > 20 cP) reduce injectability and increase syringeability. Needle gauges below 20 exhibited constant dynamic pressure and negligible shear stress, while gauges between 20 and 25 showed higher shear stress and pressure variability. Longer needles and larger medication volumes increase dynamic pressure and shear stress, prolonging injection time. The mathematical and CFD models matched experimental measurements within a 1.1 % and 4.8 % margin of error, respectively. These findings inform the design of efficient autoinjectors, enhancing drug delivery, patient comfort, and compliance.

自体注射器在治疗慢性和自身免疫性疾病方面的可用性和多功能性使其在医学上越来越有吸引力。然而，对自动进样器设计的研究需要了解注入过程中的运动特性和流体行为。为了优化注射效率，本研究通过研究粘度、针头长度、针头直径和药物体积对注射过程的影响，建立了IM自动注射器的数学和计算流体动力学（CFD）模型。采用比较器实验对模型进行了验证和验证，并采用参数敏感性分析对模型进行了优化。数学模型结果表明，柱塞位移在低粘性流体中呈线性增加(v <；20cp)，从而加快注射速度。CFD模拟表明，高粘度流体(v >；20cp)降低可注射性，增加可注射性。针规在20以下表现出恒定的动压力和可忽略的剪切应力，而在20和25之间的针规表现出较高的剪切应力和压力变异性。更长的针头和更大的药量增加了动压力和剪切应力，延长了注射时间。数学模型和CFD模型与实验测量值的匹配误差分别在1.1%和4.8%以内。这些发现为设计高效的自体注射器提供了信息，增强了药物输送、患者舒适度和依从性。

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引用次数: 0

Explainable deep learning framework for brain tumor detection: Integrating LIME, Grad-CAM, and SHAP for enhanced accuracy 用于脑肿瘤检测的可解释的深度学习框架：整合LIME， Grad-CAM和SHAP以提高准确性

IF 2.3 4区医学 Q3 ENGINEERING, BIOMEDICAL

Medical Engineering & Physics

Pub Date : 2025-07-25 DOI: 10.1016/j.medengphy.2025.104405

Abdurrahim Akgündoğdu , Şerife Çelikbaş

Deep learning approaches have improved disease diagnosis efficiency. However, AI-based decision systems lack sufficient transparency and interpretability. This study aims to enhance the explainability and training performance of deep learning models using explainable artificial intelligence (XAI) techniques for brain tumor detection. A two-stage training approach and XAI methods were implemented. The proposed convolutional neural network achieved 97.20% accuracy, 98.00% sensitivity, 96.40% specificity, and 98.90% ROC-AUC on the BRATS2019 dataset. It was analyzed with explainability techniques including Local Interpretable Model-Agnostic Explanations (LIME), Gradient-weighted Class Activation Mapping (Grad-CAM), and Shapley Additive Explanations (SHAP). The masks generated from these analyses enhanced the dataset, leading to a higher accuracy of 99.40%, 99.20% sensitivity, 99.60% specificity, 99.60% precision, and 99.90% ROC-AUC in the final stage. The integration of LIME, Grad-CAM, and SHAP showed significant success by increasing the accuracy performance of the model from 97.20% to 99.40%. Furthermore, the model was evaluated for fidelity, stability, and consistency and showed reliable and stable results. The same strategy was applied to the BR35H dataset to test the generalizability of the model, and the accuracy increased from 96.80% to 99.80% on this dataset as well, supporting the effectiveness of the method on different data sources.

深度学习方法提高了疾病诊断效率。然而，基于人工智能的决策系统缺乏足够的透明度和可解释性。本研究旨在利用可解释人工智能（XAI）技术增强深度学习模型的可解释性和训练性能，用于脑肿瘤检测。采用两阶段训练方法和XAI方法。本文提出的卷积神经网络在BRATS2019数据集上的准确率为97.20%，灵敏度为98.00%，特异性为96.40%，ROC-AUC为98.90%。采用局部可解释模型不可知解释（LIME）、梯度加权类激活映射（Grad-CAM）和Shapley加性解释（SHAP）等可解释性技术对其进行分析。从这些分析中生成的掩模增强了数据集，在最后阶段达到99.40%的准确率、99.20%的灵敏度、99.60%的特异性、99.60%的精度和99.90%的ROC-AUC。LIME、Grad-CAM和SHAP的整合取得了显著的成功，将模型的准确率从97.20%提高到99.40%。对模型进行保真度、稳定性和一致性评价，结果可靠稳定。将相同的策略应用于BR35H数据集，测试模型的泛化性，该数据集的准确率也从96.80%提高到99.80%，支持了该方法在不同数据源上的有效性。

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引用次数: 0

A machine learning approach to concussive group classification using discrete outcome measures from a low-cost movement-based assessment system 一种机器学习方法，使用来自低成本的基于运动的评估系统的离散结果测量进行震荡组分类

IF 2.3 4区医学 Q3 ENGINEERING, BIOMEDICAL

Medical Engineering & Physics

Pub Date : 2025-07-24 DOI: 10.1016/j.medengphy.2025.104402

Jacob M. Thomas , Jamie B. Hall , Rebecca Bliss , Emily Leary , Stephen P. Sayers , Praveen Rao , Trent M. Guess

Measurable neuromotor control deficits during functional task performance could provide objective criteria to aid in concussion diagnosis. However, many tools which measure these constructs are unidimensional and not clinically feasible. The purpose of this study was to assess the classification accuracy of a machine learning model using features measured by a clinically feasible movement-based assessment system (Mizzou Point-of-care Assessment System (MPASS) between athletes with and without concussion. Forty collegiate athletes participated. Twenty (19.40 ± 1.04 yrs., 11 females) suffered concussion within two weeks of data collection (5.40 ± 3.68 days). Twenty (19.85 ± 1.20 yrs.) sex, sport, and position-matched athletes had no concussions in the past year. All participants completed three 30-second static balance trials with eyes closed on foam surface under both single task and cognitive dual task conditions, four trials of gait under normal, head shaking, and dual task conditions, and reaction time tasks. Kinematics, kinetics, and reaction times were recorded by MPASS. Measures were used as features for a XGBoost machine learning model. Five-fold cross-validation yielded mean (across 5-folds): 82.5 % accuracy, 75 % sensitivity, 90 % specificity, 88.2 % positive predictive value, and 78.3 % negative predictive value. Results indicate promise for using movement-based features from a low-cost movement-based assessment system to improve the objectivity of concussion diagnosis decision-making.

在功能性任务表现中可测量的神经运动控制缺陷可为脑震荡诊断提供客观标准。然而，许多测量这些结构的工具是单向度的，在临床上不可行。本研究的目的是评估机器学习模型的分类准确性，使用临床可行的基于运动的评估系统（Mizzou Point-of-care assessment system， MPASS）在有脑震荡和没有脑震荡的运动员之间测量的特征。40名大学生运动员参加了比赛。20（19.40±1.04）年。11例女性)在收集数据的2周内（5.40±3.68天）出现脑震荡。20名（19.85±1.20岁）性别、运动和位置匹配的运动员在过去一年中没有发生过脑震荡。所有被试分别在单任务和认知双任务条件下完成3个30秒闭眼泡沫表面静态平衡试验，在正常、摇头和双任务条件下完成4个步态试验，以及反应时间任务。通过MPASS记录运动学、动力学和反应时间。度量被用作XGBoost机器学习模型的特征。5倍交叉验证的平均准确率为82.5%，灵敏度为75%，特异性为90%，阳性预测值为88.2%，阴性预测值为78.3%。结果表明，利用基于运动特征的低成本评估系统可以提高脑震荡诊断决策的客观性。

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