Informatics in Medicine Unlocked最新文献_第2页

Impact assessment of digital ecosystem in healthcare services: A qualitative case study of hospital data management in Bikaner District in India 医疗保健服务中数字生态系统的影响评估：印度比卡内尔地区医院数据管理的定性案例研究

Q1 Medicine

Informatics in Medicine Unlocked

Pub Date : 2026-01-10 DOI: 10.1016/j.imu.2026.101735

Nikhil Maurya , Arya Veer Singh Chauhan , Inder Puri , Mukesh Kumar Rohil , Sanjay Kumar Kochar , Tanmaya Mahapatra

The proliferation of digitalization, along with advanced computational techniques, in the healthcare ecosystem has expedited the process of patient care, treatment, and disease diagnosis globally. Medical research, especially involving computational techniques, is heavily dependent on the availability of high-quality datasets generated at the point of care for effective translational research. Our study aims to understand the state of the digital ecosystem (i.e., digitalization, usage of electronic health records (EHRs), and medical data) for the purpose of improving healthcare services and research in hospitals. We conducted a questionnaire-based survey at 16 upper-primary health care centers and public hospitals in the district of Bikaner, Rajasthan, India, to understand the current practices of medical data digitalization and data repository development. The survey results have been analyzed using Principal Component Factor Analysis (PCFA) and statistical tests, including Cronbach's Alpha, the Kaiser-Meyer-Olkin (KMO) measure, and Bartlett's test of sampling adequacy, which indicate that the state of digitalization is in its initial phase. Among technical professionals, 35.6 % agreed that digitalization has been implemented, while 12.3 % remained neutral and 52.1 % disagreed. For the same, 41.4 % agreed, 13.0 % remained neutral, and 45.6 % disagreed among non-technical professionals. These highlight that almost half of the groups recognize slow progress in this area, implying that digitalization is still in its initial phase. Our study also indicates that the lack of access to structured and semi-structured medical datasets is a key barrier to applying Artificial Intelligence (AI) and Machine Learning (ML) in Indian healthcare research, where these technologies could play a crucial role in improving healthcare diagnostics, outcome prediction, and enhancing clinical decision-making, for better healthcare services, esp. in resource-constrained settings.

在医疗保健生态系统中，数字化的普及以及先进的计算技术加快了全球患者护理、治疗和疾病诊断的进程。医学研究，特别是涉及计算技术的医学研究，严重依赖于在护理点生成的高质量数据集的可用性，以便进行有效的转化研究。我们的研究旨在了解数字生态系统的状态（即数字化，电子健康记录（EHRs）和医疗数据的使用），以改善医院的医疗服务和研究。我们对印度拉贾斯坦邦Bikaner地区的16家高级初级卫生保健中心和公立医院进行了问卷调查，以了解当前医疗数据数字化和数据存储库开发的实践。利用主成分因子分析（PCFA）和统计检验（包括Cronbach's Alpha、Kaiser-Meyer-Olkin （KMO）测量和Bartlett抽样充分性检验）对调查结果进行了分析，表明数字化的状态处于初级阶段。在技术专业人士中，35.6%的人同意数字化已经实施，而12.3%的人保持中立，52.1%的人不同意。同样，41.4%的人同意，13.0%保持中立，45.6%的非技术专业人员不同意。这些数据突出表明，几乎一半的集团认识到这一领域的进展缓慢，这意味着数字化仍处于初级阶段。我们的研究还表明，缺乏对结构化和半结构化医疗数据集的访问是在印度医疗保健研究中应用人工智能（AI）和机器学习（ML）的关键障碍，这些技术可以在改善医疗保健诊断、结果预测和加强临床决策方面发挥关键作用，以获得更好的医疗保健服务，特别是在资源受限的环境中。

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引用次数: 0

Interpretable deep learning for rotator cuff tear diagnosis: A novel convolutional neural network with Grad-CAM visualization on MRI 可解释的深度学习用于肩袖撕裂诊断：一种新的卷积神经网络与MRI上的Grad-CAM可视化

Q1 Medicine

Informatics in Medicine Unlocked

Pub Date : 2026-01-06 DOI: 10.1016/j.imu.2026.101733

Mohammad Amin Esfandiari , Iman Ahanian , Ali Broumandnia , Nader Jafarnia Dabanloo

Accurate diagnosis of rotator cuff tears from magnetic resonance imaging (MRI) is essential for effective clinical management and treatment planning. In this study, we propose a novel convolutional neural network (CNN) architecture specifically designed for classifying rotator cuff tears, integrated with gradient-weighted class activation mapping (Grad-CAM) to provide interpretable insights into the model's decision-making process. We utilized MRI data from 150 subjects, equally divided between normal and pathological cases, and applied data augmentation techniques, including rotation, scaling, and reflection, to enhance model generalization. The proposed CNN demonstrated superior performance, achieving an average accuracy of 94.5 %, sensitivity of 94.6 %, precision of 94.1 %, and specificity of 93.4 %, outperforming established lightweight models such as MobileNetV2 and SqueezeNet. Grad-CAM visualizations confirmed that the model accurately focused on anatomically relevant regions associated with tendon ruptures, thereby enhancing trust in its predictions. These results underscore the potential of our interpretable deep learning framework to deliver reliable, transparent, and clinically actionable diagnostic support for shoulder injuries, paving the way for improved decision-making in orthopedic care. This approach highlights the synergy of advanced CNN design and explainable AI for robust medical imaging applications.

通过磁共振成像（MRI）准确诊断肩袖撕裂对于有效的临床管理和治疗计划至关重要。在这项研究中，我们提出了一种新颖的卷积神经网络（CNN）架构，专门用于对肩袖撕裂进行分类，并与梯度加权类激活映射（Grad-CAM）相结合，为模型的决策过程提供可解释的见解。我们利用150名受试者的MRI数据，将正常病例和病理病例平均划分，并应用数据增强技术，包括旋转、缩放和反射，以增强模型的泛化。所提出的CNN表现出优异的性能，平均准确率为94.5%，灵敏度为94.6%，精度为94.1%，特异性为93.4%，优于已建立的轻量级模型，如MobileNetV2和SqueezeNet。Grad-CAM可视化证实了该模型准确地聚焦于与肌腱断裂相关的解剖相关区域，从而增强了其预测的可信度。这些结果强调了我们可解释的深度学习框架在为肩伤提供可靠、透明和临床可操作的诊断支持方面的潜力，为改善骨科护理决策铺平了道路。这种方法强调了先进的CNN设计和可解释的人工智能在强大的医学成像应用中的协同作用。

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引用次数: 0

A joint frailty model to assess the relationship between time to curative treatment and biochemical recurrence in prostate cancer patients 一个评估前列腺癌患者治愈时间与生化复发关系的关节衰弱模型

Q1 Medicine

Informatics in Medicine Unlocked

Pub Date : 2026-01-01 DOI: 10.1016/j.imu.2025.101727

Abderrahim Oussama Batouche , Denis Rustand , Eugen Czeizler , Håvard Rue , Tuomas Mirtti , Antti Rannikko

Objective:

Conflicting evidence exists regarding the effect of delaying prostate cancer (PCa) treatment on outcomes after curative treatment. Ideally, modelling this would require a joint analysis of the time to treatment initiation and the time to PCa recurrence. However, traditional Cox models are limited to analysing a single time-to-event outcome. In this study, we build a joint frailty model to assess the effect of delayed or expedited primary curative treatment (surgery or radiotherapy) on the risk of biochemical recurrence (BCR).

Methods:

We used HUS (Helsinki Metropolitan Hospital District) data lake for data mining and to categorise PCa patients by Gleason grade group (1-5), treatment, and biochemical recurrence for a final sample size of n=9934 patients (1993–2019). A broader definition of BCR was established considering secondary treatments as an additional indicator of relapse alongside traditional PSA cut-offs. We applied the INLA method for Bayesian inference, utilising the INLAJoint R package, to fit a shared frailty joint survival model. This model was used to analyse the relationship between the time from diagnosis to curative treatment (treatment risk) and the time from diagnosis to biochemical recurrence (relapse risk).

Results:

Conditional on covariates, including age at diagnosis and Gleason grade group, our joint survival model revealed a significant association

γ

= -1.32 [-1.50, -1.14] between the risk of treatment and risk of BCR. As an example, regardless of the grade group, patients within the top 5% with the lowest risk of receiving treatment (i.e., the longest time to treatment) exhibited an HR=5.27 [4.28, 6.62] fold (increased) risk of recurrence compared to the average patient.

Conclusion:

We successfully employed a joint frailty model to simultaneously model the effect of time to curative primary treatment on time to biochemical recurrence. We show that the time to curative treatment is associated with the risk of relapse. Patients of the same age, same diagnostic PSA, and same grade group who are treated early are less likely to develop biochemical recurrence.

目的：关于延迟前列腺癌（PCa）治疗对治愈性治疗后预后的影响，存在相互矛盾的证据。理想情况下，建模这将需要联合分析开始治疗的时间和PCa复发的时间。然而，传统的Cox模型仅限于分析单一的时间到事件的结果。在这项研究中，我们建立了一个关节脆弱模型来评估延迟或加速初级治愈治疗（手术或放疗）对生化复发（BCR）风险的影响。方法：我们使用HUS（赫尔辛基大都会医院区）数据湖进行数据挖掘，并根据Gleason分级组（1-5）、治疗和生化复发对PCa患者进行分类，最终样本量为n=9934例患者（1993-2019）。BCR的更广泛的定义被建立，考虑到二次治疗作为复发的附加指标，与传统的PSA切断。我们应用INLA方法进行贝叶斯推理，利用INLAJoint R包，拟合共享脆弱关节生存模型。该模型用于分析从诊断到治愈治疗的时间（治疗风险）与从诊断到生化复发的时间（复发风险）之间的关系。结果：根据协变量，包括诊断年龄和Gleason分级组，我们的联合生存模型显示治疗风险与BCR风险之间存在显著关联γ = -1.32[-1.50, -1.14]。例如，无论分级组如何，接受治疗风险最低（即治疗时间最长）的前5%患者的复发风险比平均患者高5.27[4.28,6.62]倍。结论：我们成功地建立了关节脆弱模型，同时模拟了首次治疗治愈时间对生化复发时间的影响。我们表明，治愈治疗的时间与复发的风险有关。相同年龄、相同诊断PSA、相同分级组的患者早期治疗后发生生化复发的可能性较小。

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引用次数: 0

PAL-Net: A point-wise CNN with patch-attention for 3D anatomical facial landmark localization PAL-Net：一种具有斑块注意的点向CNN，用于三维解剖面部地标定位

Q1 Medicine

Informatics in Medicine Unlocked

Pub Date : 2026-01-01 DOI: 10.1016/j.imu.2025.101729

Ali Shadman Yazdi , Annalisa Cappella , Benedetta Baldini , Riccardo Solazzo , Gianluca Tartaglia , Chiarella Sforza , Giuseppe Baselli

Manual annotation of anatomical landmarks on 3D facial scans is a time-consuming and expertise-dependent task, yet it remains critical for clinical assessments, morphometric analysis, and craniofacial research. While several deep learning methods have been proposed for facial landmark localization, most focus on pseudo-landmarks or require complex input representations, limiting their clinical applicability. This study presents a fully automated deep learning pipeline (PAL-Net) for localizing 50 anatomical landmarks on facial models acquired via stereo-photogrammetry. The method combines coarse alignment, region-of-interest filtering, and an initial landmark approximation with a patch-based pointwise CNN enhanced by attention mechanisms. Trained and evaluated on 214 annotated scans from healthy adults, PAL-Net achieved a mean localization error of 3.686 mm and preserved relevant anatomical distances with an average error of 2.822 mm. While the geometric error exceeds expert intra-observer variability, the distance-wise error maintains structural integrity sufficient for high-throughput anthropometric analysis. To assess generalization, the model was further evaluated on 700 subjects from the FaceScape dataset, achieving a mean localization error of 0.41 mm and a distance error of 0.38 mm. Comparing with existing methods, PAL-Net offers a favorable trade-off between accuracy and computational cost. While performance degrades in regions with poor mesh quality (e.g., ears, hairline), the method demonstrates consistent accuracy across most anatomical regions. PAL-Net generalizes effectively across datasets and facial regions, outperforming existing methods in both point-wise and structural evaluations. It provides a lightweight, scalable solution for high-throughput 3D anthropometric analysis, with potential to support clinical workflows and reduce reliance on manual annotation. Source code can be accessed at https://github.com/Ali5hadman/PAL-Net-A-Point-Wise-CNN-with-Patch-Attention.

在3D面部扫描上手动标注解剖标志是一项耗时且依赖专业知识的任务，但它对于临床评估、形态计量分析和颅面研究仍然至关重要。虽然已经提出了几种用于面部地标定位的深度学习方法，但大多数方法都侧重于伪地标或需要复杂的输入表示，限制了它们的临床适用性。本研究提出了一种全自动深度学习管道（PAL-Net），用于定位通过立体摄影测量获得的面部模型上的50个解剖地标。该方法将粗对齐、感兴趣区域过滤和初始地标近似与基于补丁的点向CNN相结合，并通过注意机制增强。PAL-Net对214张健康成人带注释的扫描图进行了训练和评估，平均定位误差为3.686 mm，保留相关解剖距离的平均误差为2.822 mm。虽然几何误差超过了专家内部观察者的可变性，但距离误差保持了结构完整性，足以进行高通量人体测量分析。为了评估该模型的泛化程度，我们对来自FaceScape数据集的700名受试者进行了进一步评估，平均定位误差为0.41 mm，距离误差为0.38 mm。与现有方法相比，PAL-Net在精度和计算成本之间提供了良好的平衡。虽然在网格质量较差的区域（如耳朵、发际线）性能会下降，但该方法在大多数解剖区域显示出一致的准确性。PAL-Net有效地泛化了数据集和面部区域，在点和结构评估方面都优于现有方法。它为高通量3D人体测量分析提供了一种轻量级、可扩展的解决方案，具有支持临床工作流程和减少对手动注释依赖的潜力。源代码可以在https://github.com/Ali5hadman/PAL-Net-A-Point-Wise-CNN-with-Patch-Attention上访问。

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引用次数: 0

A medical image captioning system for TeleOTIVA: Supporting SDGs-oriented cervical precancer screening in Indonesia TeleOTIVA的医学图像字幕系统：支持印尼面向可持续发展目标的宫颈癌前筛查

Q1 Medicine

Informatics in Medicine Unlocked

Pub Date : 2026-01-01 DOI: 10.1016/j.imu.2025.101719

Firdaus Firdaus , Siti Nurmaini , Rizki Ayunda Pratama , Elly Matul Imah , Muhammad Naufal Rachmatullah , Ade Iriani Sapitri , Annisa Darmawahyuni , Anggun Islami , Akhiar Wista Arum , Bambang Tutuko , Patiyus Agustiansyah , Rizal Sanif , Radiyati Umi Partan

Cervical cancer screening using Visual Inspection with Acetic Acid (VIA) remains a critical strategy in resource-limited settings. However, its effectiveness is often hindered by diagnostic variability arising from subjective interpretation. To address this challenge, we introduce TeleOTIVA, an AI-powered system designed to automatically detect and describe cervical lesions from VIA images. The system integrates YOLOv11-based lesion detection and segmentation with a Dense Residual Network and an embedding LSTM-based image captioning module, enabling it to generate clinically meaningful descriptions encompassing lesion borders, surface texture, and anatomical location. The performance of TeleOTIVA demonstrates promising results. Evaluations of the generated captions, compared to expert-annotated ground truth, yielded high scores across multiple metrics: BLEU (0.5711), METEOR (0.6726), and ROUGE-L (0.6929). These results indicate a high degree of n-gram similarity, semantic relevance, grammatical accuracy, and structural alignment with human-generated descriptions. In other words, the model not only mirrors expert-level vocabulary but also captures the clinical essence of VIA image interpretation. This synergy between advanced lesion detection and automated caption generation significantly enhances the accuracy, efficiency, and accessibility of cervical cancer screening. TeleOTIVA thus offers a powerful and scalable diagnostic aid, particularly impactful for improving early detection efforts in underserved and low-resource regions.

在资源有限的情况下，使用醋酸目视检查（VIA）进行宫颈癌筛查仍然是一种重要的策略。然而，其有效性往往受到主观解释引起的诊断可变性的阻碍。为了应对这一挑战，我们引入了TeleOTIVA，这是一种人工智能驱动的系统，旨在从VIA图像中自动检测和描述宫颈病变。该系统将基于yolov11的病变检测和分割与密集残差网络和基于嵌入lstm的图像标题模块相结合，使其能够生成包括病变边界、表面纹理和解剖位置在内的具有临床意义的描述。TeleOTIVA的性能显示出良好的效果。与专家注释的真实情况相比，对生成的字幕的评估在多个指标上获得了高分：BLEU（0.5711）、METEOR（0.6726）和ROUGE-L（0.6929）。这些结果表明，与人类生成的描述具有高度的n-gram相似性、语义相关性、语法准确性和结构一致性。换句话说，该模型不仅反映了专家级词汇，还捕捉到了VIA图像解释的临床本质。这种高级病变检测和自动字幕生成之间的协同作用显著提高了宫颈癌筛查的准确性、效率和可及性。因此，TeleOTIVA提供了一种强大且可扩展的诊断援助，对改善服务不足和资源匮乏地区的早期发现工作尤其有影响。

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引用次数: 0

Automated identification of left ventricular hypertrophy using cardiac ultrasound imaging: A systematic review of artificial intelligence driven approaches 使用心脏超声成像自动识别左心室肥厚：人工智能驱动方法的系统回顾

Q1 Medicine

Informatics in Medicine Unlocked

Pub Date : 2026-01-01 DOI: 10.1016/j.imu.2025.101730

Jimcymol James , Anjan Gudigar , U. Raghavendra , Jyothi Samanth , M. Maithri , Aryaman Kaprekar , Mukund A. Prabhu , Massimo Salvi , Filippo Molinari , Edward J. Ciaccio , U. Rajendra Acharya

Left Ventricular Hypertrophy (LVH) is a significant cardiovascular risk marker that manifests in several clinical conditions, including Hypertension (HTN), Chronic Kidney Disease (CKD), and Hypertrophic Cardiomyopathy (HCM). This systematic review examines Artificial Intelligence (AI) approaches for the automated identification of these conditions using cardiac Ultrasound (US) imaging. Following the PRISMA guidelines, 37 relevant articles (7 reviews, 30 research papers) published between 2010 and 2025 were analysed. The analysis revealed three primary methodological approaches: feature learning pipelines, end-to-end Deep Learning (DL), and hybrid methods that combine both techniques. For CKD detection, only one study using cardiac US was identified, which achieved 99.09 % classification accuracy using Support Vector Machine (SVM) with steerable Gaussian filters and entropy features. HTN classification studies have demonstrated high performance across different approaches: traditional Machine Learning (ML) classifiers (decision trees with transform features: 99.11 %, weighted k-nearest neighbors: 98 %) and DL methods (Area Under Curve (AUC): 0.92–0.94). HCM studies ranged from binary classification (42.3 % of studies) to multi-class problems of increasing complexity (3-class: 38.4 %, 4-class: 11.5 %, 5-class: 7.6 %), with SVM achieving 95.2 % average sensitivity and DL models reaching an average AUC of 0.94. Current limitations include a predominant focus on binary classification problems, limited research on cardiac-based CKD detection, and a lack of standardized datasets. Future research directions include developing hybrid methodologies that combine traditional and DL approaches, creating standardized multimodal databases, implementing explainable AI techniques, and integrating Internet of Things technologies for continuous monitoring.

左心室肥厚（LVH）是一项重要的心血管危险标志物，在多种临床情况下均有表现，包括高血压（HTN）、慢性肾病（CKD）和肥厚性心肌病（HCM）。本系统综述研究了使用心脏超声（US）成像自动识别这些疾病的人工智能（AI）方法。根据PRISMA指南，我们分析了2010年至2025年间发表的37篇相关文章（7篇综述，30篇研究论文）。分析揭示了三种主要的方法方法：特征学习管道，端到端深度学习（DL），以及结合这两种技术的混合方法。对于CKD的检测，仅鉴定了一项使用心脏US的研究，该研究使用具有可操纵高斯滤波器和熵特征的支持向量机（SVM）实现了99.09%的分类准确率。HTN分类研究已经证明了不同方法的高性能：传统机器学习（ML）分类器（具有变换特征的决策树：99.11%，加权k近邻：98%）和深度学习方法（曲线下面积（AUC）： 0.92-0.94）。HCM的研究范围从二元分类（42.3%的研究）到日益复杂的多类问题（3类：38.4%，4类：11.5%，5类：7.6%），SVM的平均灵敏度达到95.2%，DL模型的平均AUC达到0.94。目前的限制包括主要关注二分类问题，基于心脏的CKD检测研究有限，以及缺乏标准化数据集。未来的研究方向包括开发结合传统和深度学习方法的混合方法，创建标准化的多模态数据库，实施可解释的人工智能技术，以及集成物联网技术进行持续监测。

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引用次数: 0

Objective detection of Parkinson's disease motor states using Lasso-selected IMUs features 目的利用lasso选择imu特征检测帕金森病运动状态

Q1 Medicine

Informatics in Medicine Unlocked

Pub Date : 2026-01-01 DOI: 10.1016/j.imu.2026.101732

Mevludin Memedi

Background and objective

Machine learning (ML) models that use data captured from Inertial Measurement Units (IMUs) are widely applied in the clinical management of Parkinson's disease (PD). However, performance and interpretability of these models can be influenced by the selection of input features, especially when dealing with multi-dimensional data. This study investigates the performance of Lasso regularization in improving performance and simplicity of supervised and unsupervised ML models using IMUs data.

Methods

Data were collected using IMUs placed on the wrists and ankles of 19 patients (14 males and 5 females) with advanced PD (mean years with disease of 10 years). Participants performed different motor tests, and three movement disorder specialists rated the severity of motor states (Off and dyskinesia) on a Treatment Response Scale (TRS). Sensor data were processed, and features were reduced by Lasso regularization and used as inputs to Support Vector Machines (SVM) for classification and regression. In addition, clustering methods were employed to align the sensor data to clinical labels.

Results

Linear SVM correctly classified Off motor state from treatment-induced dyskinesia state with an accuracy of 93.9 % and 93.3 %, respectively. The correlation coefficient between the predicted TRS score derived by gaussian SVM and mean TRS score of the three specialists was 0.91. The clusters derived by the clustering algorithms separated well the instances when the patients were in Off and dyskinesia motor states.

Conclusions

Using Lasso regularization as a feature selection method coupled with ML models yielded good predictive performance when fusing multi-sensor and -activity data from IMUs. This approach can be used as a tool to objectively assess PD motor states and improve the management of the disease by individualizing treatments.

背景与目的利用惯性测量单元（imu）数据采集的机器学习（ML）模型在帕金森病（PD）的临床管理中得到了广泛的应用。然而，这些模型的性能和可解释性可能会受到输入特征选择的影响，特别是在处理多维数据时。本研究探讨了Lasso正则化在使用imu数据提高有监督和无监督ML模型的性能和简单性方面的性能。方法对19例（男14例，女5例）晚期PD患者（平均患病时间为10年）进行腕、踝部imu采集数据。参与者进行不同的运动测试，三位运动障碍专家在治疗反应量表（TRS）上评估运动状态（关闭和运动障碍）的严重程度。对传感器数据进行处理，通过Lasso正则化对特征进行约简，并将其作为支持向量机（SVM）的输入进行分类和回归。此外，采用聚类方法将传感器数据与临床标签对齐。结果线性支持向量机对治疗引起的运动障碍状态和运动关闭状态的分类准确率分别为93.9%和93.3%。高斯支持向量机预测的TRS评分与三位专家的平均TRS评分的相关系数为0.91。由聚类算法得到的聚类能很好地分离出患者在运动障碍状态和运动障碍状态下的实例。结论将Lasso正则化作为特征选择方法与ML模型相结合，在融合多传感器和imu活动数据时具有良好的预测性能。这种方法可以作为客观评估PD运动状态的工具，并通过个体化治疗改善疾病的管理。

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引用次数: 0

Noise-Aware Undersampling for imbalanced medical data (NAUS) 不平衡医疗数据的噪声感知欠采样（NAUS）

Q1 Medicine

Informatics in Medicine Unlocked

Pub Date : 2026-01-01 DOI: 10.1016/j.imu.2026.101731

Zholdas Buribayev , Ainur Yerkos , Zhibek Zhetpisbay , Markus Wolfien

Advancements in medical research have increasingly relied on robust data analytics to support diagnostic and treatment decisions. However, data analysis still faces challenges when investigating datasets with severe class imbalance, often stemming from the rarity of certain conditions and uneven disease distributions. To address this issue, we propose the Noise-Aware Undersampling with Subsampling (NAUS) algorithm. NAUS integrates clustering, noise removal, and Tomek-link identification techniques to create refined subsamples that assess the significance of individual observations, while systematically removing redundant and noisy data. The proposed approach was evaluated on datasets related to chronic kidney disease, liver disease, heart disease and its performance was compared to that of traditional oversampling methods (e.g., SMOTE, ADASYN, LoRAS) and undersampling techniques (e.g., random undersampling, Tomek-links). Our experimental results, based on machine learning classifiers (e.g. Random Forest, LightGBM, and Multilayer Perceptron). Data visualization further confirmed that NAUS effectively mitigates class imbalance, making it a promising tool for enhancing the reliability of medical data analysis.

医学研究的进步越来越依赖于强大的数据分析来支持诊断和治疗决策。然而，在调查具有严重类别不平衡的数据集时，数据分析仍然面临挑战，通常源于某些疾病的罕见性和不均匀的疾病分布。为了解决这个问题，我们提出了带有子采样的噪声感知欠采样（NAUS）算法。NAUS集成了聚类、去噪和Tomek-link识别技术，以创建精细的子样本，评估个人观察的重要性，同时系统地去除冗余和噪声数据。在慢性肾病、肝病和心脏病相关的数据集上对所提出的方法进行了评估，并将其性能与传统的过采样方法（例如SMOTE、ADASYN、LoRAS）和欠采样技术（例如随机欠采样、Tomek-links）进行了比较。我们的实验结果，基于机器学习分类器（例如Random Forest， LightGBM和Multilayer Perceptron）。数据可视化进一步证实了NAUS有效缓解了类别不平衡，使其成为提高医疗数据分析可靠性的有前景的工具。

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引用次数: 0

Evaluating cultural impact on subject-independent EEG-based emotion recognition approaches 评估文化对独立于主体的基于脑电图的情感识别方法的影响

Q1 Medicine

Informatics in Medicine Unlocked

Pub Date : 2026-01-01 DOI: 10.1016/j.imu.2025.101728

Anshul Sheoran , Camilo E. Valderrama

Culture plays a crucial role in shaping emotional expression and recognition, influencing how individuals perceive and regulate emotions. Electroencephalography (EEG) can capture electrical activity associated with human emotion processing from the scalp. The electrical activity can be processed using deep learning models to predict emotional states. Two approaches can be employed to develop these deep learning models: subject-dependent and subject-independent. The subject-independent approach is more practical as it trains the model on data from some individuals and tests it on entirely different individuals, ensuring it generalizes well to new users. However, because of the high variability of EEG across individuals, the subject-independent approach tends to yield low performance. Recent studies suggest incorporating demographic information along with EEG signals is one way to overcome this issue. By using the subject-independent approach, this study investigates how cultural factors impact emotion prediction. Specifically, we used a stacking model that combines deep learning with multinomial logistic regression to predict positive, neutral, and negative emotions among 15 Chinese, 8 French, and 8 German subjects. Our approach achieved accuracies of 77.3% for Chinese subjects, 73% for French subjects, and 65% for German subjects, which are comparable to or exceed accuracies reported by previous studies. Our approach highlighted that incorporating cultural information increases the likelihood of predicting positive emotions for Chinese participants and negative emotions for Europeans. Moreover, French and German subjects exhibited similar neural patterns across all emotions, suggesting a more common cultural sharing between those subjects. Overall, our findings emphasize the importance of integrating cultural considerations into emotion recognition models. This inclusion not only improves emotion prediction accuracy for subject-independent approaches but also promotes inclusivity and ethical practices in emotion recognition systems.

文化在塑造情绪表达和识别方面起着至关重要的作用，影响着个体如何感知和调节情绪。脑电图（EEG）可以从头皮捕捉到与人类情感处理相关的电活动。脑电活动可以通过深度学习模型来预测情绪状态。可以采用两种方法来开发这些深度学习模型：主题依赖和主题独立。独立于主题的方法更实用，因为它根据来自某些个体的数据训练模型，并在完全不同的个体上进行测试，以确保它可以很好地推广到新用户。然而，由于脑电图在个体之间的高度可变性，独立于受试者的方法往往产生较低的性能。最近的研究表明，将人口统计信息与脑电图信号结合起来是克服这一问题的一种方法。本研究采用受试者独立的方法，探讨文化因素对情绪预测的影响。具体来说，我们使用了一个将深度学习与多项逻辑回归相结合的叠加模型来预测15名中国、8名法国和8名德国受试者的积极、中性和消极情绪。我们的方法在中文受试者中达到了77.3%的准确率，在法语受试者中达到了73%，在德语受试者中达到了65%，这与之前的研究报告的准确率相当或超过了这些准确率。我们的方法强调，结合文化信息增加了预测中国参与者积极情绪和欧洲参与者消极情绪的可能性。此外，法语和德语受试者在所有情绪上都表现出相似的神经模式，这表明这些受试者之间存在更普遍的文化共享。总的来说，我们的研究结果强调了将文化因素整合到情感识别模型中的重要性。这种包容不仅提高了独立于主体方法的情感预测准确性，而且促进了情感识别系统的包容性和伦理实践。

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引用次数: 0

A systematic review on computer vision-based methods for cervical cancer detection 基于计算机视觉的宫颈癌检测方法综述

Q1 Medicine

Informatics in Medicine Unlocked

Pub Date : 2025-12-16 DOI: 10.1016/j.imu.2025.101726

Hope Mbelwa , Judith Leo , Crispin Kahesa , Elizabeth Mkoba

Cervical cancer is a leading cause of mortality among women globally, especially in regions where access to timely screening remains a challenge. With such concerns, accurate detection of cervical lesions is essential for effective diagnosis and treatment. This review aimed to explore the application of computer vision-based methods for detecting cervical cancer, identifying their potential, setbacks and areas for future development. A comprehensive literature search across Scopus, IEEE Xplore, PubMed, and Google Scholar identified 96 relevant studies published between 2014 and August 2025. These studies applied computer vision methods including CNNs, Vision Transformers, and multimodal models to cervical cancer detection using Pap smear, colposcopy, and histopathology images. They were analyzed based on the techniques employed, datasets used, evaluation metrics adopted, and reported results. This review highlights significant advancements in the field, particularly in lesion classification, precise segmentation of affected regions, and accurate detection of cancerous regions. However, some challenges were identified, including limited image datasets with insufficiently distributed normal and abnormal cases, aggravated by privacy issues and accurate labeling of medical images, which is critical and rigorous, often leading to annotation inconsistencies. Lastly, this study revealed that integrating Natural Language Processing and Computer Vision can enhance cervical cancer diagnosis through multi-modal models that combine both clinical text and imaging data. Additionally, this study proposes the use of techniques like annotation-efficient learning to manage limited labeled datasets using methods such as semi-supervised and transfer learning as well as the use of federated learning to ensure privacy in computer-aided diagnostic systems.

宫颈癌是全球妇女死亡的主要原因，特别是在获得及时筛查仍然是一项挑战的区域。考虑到这些问题，准确检测宫颈病变对于有效的诊断和治疗至关重要。本文旨在探讨基于计算机视觉的宫颈癌检测方法的应用，确定其潜力，挫折和未来发展的领域。在Scopus、IEEE explore、PubMed和b谷歌Scholar上进行了全面的文献检索，确定了2014年至2025年8月期间发表的96项相关研究。这些研究将计算机视觉方法包括cnn、视觉变形器和多模态模型应用于宫颈涂片、阴道镜检查和组织病理学图像的宫颈癌检测。根据采用的技术、使用的数据集、采用的评估指标和报告的结果对它们进行分析。这篇综述强调了该领域的重大进展，特别是在病变分类、受影响区域的精确分割和癌变区域的准确检测方面。然而，也发现了一些挑战，包括图像数据集有限，正常和异常病例分布不足，隐私问题和医学图像的准确标记（这是至关重要和严格的）加剧了这些问题，往往导致注释不一致。最后，本研究表明，将自然语言处理和计算机视觉相结合，可以通过结合临床文本和图像数据的多模态模型来提高宫颈癌的诊断。此外，本研究提出使用注释高效学习等技术来管理有限的标记数据集，使用半监督和迁移学习等方法，以及使用联邦学习来确保计算机辅助诊断系统中的隐私。

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引用次数: 0