Intelligence-based medicine最新文献_第2页

Capsule-augmented deep learning architectures for mental health detection from social media text 用于社交媒体文本心理健康检测的胶囊增强深度学习架构

Intelligence-based medicine

Pub Date : 2026-03-01 Epub Date: 2025-11-24 DOI: 10.1016/j.ibmed.2025.100319

Faheem Ahmad Wagay, Jahiruddin

Mental health detection from social media text has attracted growing research attention due to the global rise in mental health concerns. Traditional deep learning models, such as Bidirectional Long Short-Term Memory (BiLSTM) networks and hybrid Convolutional BiLSTM (Conv-BiLSTM) architectures, have demonstrated strong performance in text classification tasks. However, these models often struggle to capture the hierarchical and spatial relationships that are intrinsic to linguistic data. To address this limitation, this study investigates the integration of capsule networks with BiLSTM and Conv-BiLSTM architectures for mental health detection. Leveraging a real-world Reddit corpus, we conduct extensive experiments comparing baseline BiLSTM and Conv-BiLSTM models with their capsule-enhanced counterparts. Furthermore, we explore the role of advanced loss functions, such as focal loss and contrastive loss, in addressing class imbalance and mitigating boundary blurring among semantically overlapping disorders. Our findings indicate that incorporating capsule layers significantly strengthens feature representation, leading to notable improvements in accuracy and F1-score across multiple mental health categories. The study focuses on six key disorders, including depression, anxiety, borderline personality disorder (BPD), and bipolar disorder. In addition, model interpretability is enhanced using Local Interpretable Model-agnostic Explanations (LIME), which highlights the critical linguistic features driving predictions, thereby improving transparency and reliability in mental health evaluations.

由于全球对心理健康问题的关注日益增加，从社交媒体文本中检测心理健康引起了越来越多的研究关注。传统的深度学习模型，如双向长短期记忆（BiLSTM）网络和混合卷积BiLSTM （convl -BiLSTM）架构，在文本分类任务中表现出了很强的性能。然而，这些模型往往难以捕捉语言数据固有的层次和空间关系。为了解决这一限制，本研究探讨了胶囊网络与BiLSTM和convl -BiLSTM架构的整合，用于心理健康检测。利用真实世界的Reddit语料库，我们进行了广泛的实验，将基线BiLSTM和卷积BiLSTM模型与胶囊增强模型进行比较。此外，我们探讨了高级损失函数的作用，如焦点损失和对比损失，在解决类失衡和减轻语义重叠障碍中的边界模糊。我们的研究结果表明，结合胶囊层显着增强了特征表征，导致多个心理健康类别的准确性和f1得分显着提高。这项研究的重点是六种关键的疾病，包括抑郁症、焦虑症、边缘型人格障碍（BPD）和双相情感障碍。此外，使用局部可解释模型不可知论解释（LIME）增强了模型的可解释性，它突出了驱动预测的关键语言特征，从而提高了心理健康评估的透明度和可靠性。

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引用次数: 0

STViTDA-Net: An explainable transformer-based framework with STGAN-ViT-MAE and deformable attention for multi-class skin cancer classification STViTDA-Net：一个可解释的基于变压器的框架，具有stgan - vita - mae和可变形的关注，用于多类别皮肤癌分类

Intelligence-based medicine

Pub Date : 2026-03-01 Epub Date: 2026-01-22 DOI: 10.1016/j.ibmed.2026.100350

Ravula Jyothsna , K. Prasanna , U. Moulali , V. Surya Narayana Reddy , D. Ramya Krishna , T. Praveen Kumar

Skin cancer continues to pose a major global health challenge, and its early identification is essential for improving patient outcomes. Traditional diagnostic practices rely heavily on clinician expertise and manual interpretation of dermoscopic images, making the process subjective, inconsistent, and time-consuming. To address these limitations, this work introduces STViTDA-Net, an explainable transformer-based framework designed for fast, objective, and scalable multi-class skin cancer classification. The model integrates three key components: STGAN for class-balanced dermoscopic image augmentation, ViT-MAE for robust hierarchical feature learning through masked patch reconstruction, and a Deformable Attention Transformer Encoder that adaptively focuses on irregular lesion boundaries and subtle spatial variations. Preprocessing with Error Level Analysis (ELA) enhances fine-grained diagnostic cues, while Grad-CAM provides interpretable heatmaps that highlight the regions influencing the model's predictions. Unlike manual dermoscopic evaluation, STViTDA-Net performs end-to-end inference within milliseconds and delivers consistent, expert-independent predictions supported by visual explanations. When evaluated on the ISIC2019 dataset comprising nine lesion categories, the model achieves 99.35 % accuracy, 99.0 % precision, 99.5 % recall, 99.2 % F1-score, and 99.2 % AUC-ROC, surpassing existing CNN and transformer baselines. By unifying class-balanced augmentation, adaptive feature encoding, deformable attention, and explainable outputs, STViTDA-Net establishes a powerful and efficient solution for automated dermatological diagnosis.

皮肤癌继续构成一项重大的全球健康挑战，其早期识别对于改善患者预后至关重要。传统的诊断实践在很大程度上依赖于临床医生的专业知识和对皮肤镜图像的人工解读，这使得该过程主观、不一致且耗时。为了解决这些限制，这项工作引入了STViTDA-Net，这是一个可解释的基于变压器的框架，旨在快速，客观和可扩展的多类别皮肤癌分类。该模型集成了三个关键组件：用于类平衡皮肤镜图像增强的STGAN，通过掩膜斑块重建进行鲁棒分层特征学习的viti - mae，以及自适应关注不规则病变边界和微妙空间变化的可变形注意力转换器编码器。误差水平分析（ELA）的预处理增强了细粒度的诊断线索，而Grad-CAM提供了可解释的热图，突出了影响模型预测的区域。与手动皮肤镜评估不同，STViTDA-Net在几毫秒内执行端到端推理，并提供一致的、独立于专家的预测，并得到视觉解释的支持。当在包含9个病变类别的ISIC2019数据集上进行评估时，该模型达到99.35%的准确率、99.0%的精度、99.5%的召回率、99.2%的f1得分和99.2%的AUC-ROC，超过了现有的CNN和transformer基线。通过统一类平衡增强、自适应特征编码、可变形的注意力和可解释的输出，STViTDA-Net建立了一个强大而有效的自动皮肤病诊断解决方案。

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引用次数: 0

Cardiac Magnetic Resonance-to-Computed Tomography Angiography image conversion using diffusion models for Transcatheter Aortic Valve Implantation planning 利用扩散模型对经导管主动脉瓣植入计划进行心脏磁共振-计算机断层血管造影图像转换

Intelligence-based medicine

Pub Date : 2026-03-01 Epub Date: 2025-12-19 DOI: 10.1016/j.ibmed.2025.100335

Carmen Guadalupe Colin-Tenorio , Agnes Mayr , Christian Kremser , Markus Haltmeier , Enrique Almar-Munoz

Introduction:

Transcatheter Aortic Valve Implantation (TAVI) has become the preferred method for treating severe aortic stenosis, especially in patients who are unsuitable for traditional surgery. Typically, preoperative imaging for TAVI involves contrast-enhanced Computed Tomography Angiography (CTA). However, for patients with contraindications to contrast agents, Cardiac Magnetic Resonance imaging (CMR) is a viable alternative, albeit with its limitations in visualizing calcifications.

Methods:

This study explores the application of diffusion models to enhance CMR-to-CTA contrast-free image conversion, to avoid the use of contrast agents and ionizing radiation. We developed a pipeline incorporating Denoising Diffusion Probabilistic Models (DDPMs) and Stochastic Differential Equations (SDE) models to synthesize CTA-equivalent images from CMR scans. We evaluated this approach using an in-house dataset consisting of 39 paired CTA and CMR scans. For the training process, coregistration of both modalities was required, which we achieved by performing rigid registration using the segmented aorta masks.

Results:

Our results show that the synthesized CTA images maintain high fidelity to the actual scans. This is quantitatively supported by a mean Structural Similarity Index Measure (SSIM) of 0.8 and a Peak Signal-to-Noise Ratio (PSNR) of 22 dB using conditional Stochastic Differential Equations (SDE) and Prediction-Correction (PC), indicating strong structural preservation and low reconstruction error. However, the model occasionally fails to accurately detect valve calcifications, likely due to limitations in capturing subtle pathological details that are not visually discernible in CMR images.

Conclusion:

Diffusion models used to synthesize CTA images from CMR datasets achieve high accuracy, providing a contrast-free alternative for TAVI planning and potential insights into valvular calcification patterns. However, accurate visualization of valve calcification occasionally fails, and larger datasets are desirable for validation.

导论：经导管主动脉瓣植入术（Transcatheter Aortic Valve Implantation， TAVI）已成为治疗严重主动脉瓣狭窄的首选方法，特别是对于不适合传统手术治疗的患者。通常情况下，TAVI的术前成像包括对比增强计算机断层血管造影（CTA）。然而，对于有造影剂禁忌症的患者，心脏磁共振成像（CMR）是一种可行的替代方案，尽管在可视化钙化方面存在局限性。方法：本研究探索应用扩散模型增强cmr - cta无对比图像转换，避免使用造影剂和电离辐射。我们开发了一个结合去噪扩散概率模型（ddpm）和随机微分方程（SDE）模型的管道，以合成CMR扫描的cta等效图像。我们使用由39对CTA和CMR扫描组成的内部数据集来评估这种方法。对于训练过程，需要两种模式的共同注册，我们通过使用分段主动脉面罩进行刚性注册来实现。结果：合成的CTA图像与实际扫描保持了较高的保真度。基于条件随机微分方程（SDE）和预测校正（PC）的平均结构相似指数（SSIM）为0.8，峰值信噪比（PSNR）为22 dB，这在定量上支持了这一结论，表明结构保存性强，重建误差低。然而，该模型偶尔不能准确地检测到瓣膜钙化，这可能是由于在捕捉CMR图像中无法视觉识别的细微病理细节方面的限制。结论：用于从CMR数据集合成CTA图像的扩散模型具有较高的准确性，为TAVI规划提供了无对比度的替代方案，并可能深入了解瓣膜钙化模式。然而，瓣膜钙化的精确可视化有时会失败，需要更大的数据集进行验证。

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引用次数: 0

Advancing glaucoma diagnosis: Multi-modal deep learning with vision transformer architectures 推进青光眼诊断：基于视觉转换器架构的多模态深度学习

Intelligence-based medicine

Pub Date : 2026-03-01 Epub Date: 2026-01-31 DOI: 10.1016/j.ibmed.2026.100355

Vaibhav C. Gandhi , Priyesh P. Gandhi , Ali k. Abdul Raheem , Yasser Taha Alzubaidi , Kabul Khudaybergenov , Mohammad Khishe

One of the significant causes of irreversible blindness is glaucoma which develops without symptoms and is only revealed in time when the case is very severe. The existing diagnostic models that use single-modes imaging and convolutional neural networks (CNNs) have limitations of local features dependence, less interpretability, and a lack of accuracy. This paper suggests a multi-modal deep learning model combining retinal fundus images and optical coherence tomography (OCT) scans with Vision Transformer (ViT) networks that could improve the detection and progression analysis of glaucoma. In this work, multimodal refers to an architectural and representational fusing through a hybrid Vision Transformer design and not a concurrent multi-sensor data acquisition. The structural and contextual information across modalities provide the framework with the ability to capture subtle pathological changes than CNN baselines. The benchmark experiments prove that the suggested model achieves both an accuracy of 94.5 percent and AUC-ROC of 91.7 percent, being better than VGG16, ResNet-50, and InceptionV3. Such findings highlight the promise of transformer-based multi-modal solutions to enhance early detection of glaucoma and assist with more feasible and interpretable clinical judgment.

青光眼是不可逆失明的重要原因之一，它没有症状，只有在病情严重时才会及时发现。现有的使用单模成像和卷积神经网络（cnn）的诊断模型存在局部特征依赖、可解释性差和准确性不足的局限性。本文提出了一种结合视网膜眼底图像和光学相干断层扫描（OCT）与视觉变换（ViT）网络的多模态深度学习模型，可以提高青光眼的检测和进展分析。在这项工作中，多模态是指通过混合视觉转换器设计的架构和表征融合，而不是并发的多传感器数据采集。跨模式的结构和上下文信息为该框架提供了捕捉比CNN基线更细微的病理变化的能力。基准实验证明，该模型的准确率为94.5%，AUC-ROC为91.7%，优于VGG16、ResNet-50和InceptionV3。这些发现突出了基于变压器的多模式解决方案的前景，以加强青光眼的早期检测，并有助于更可行和可解释的临床判断。

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引用次数: 0

Advanced AI framework for accurate detection and classification of brain tumours from MRI images 用于从MRI图像中准确检测和分类脑肿瘤的先进人工智能框架

Intelligence-based medicine

Pub Date : 2026-03-01 Epub Date: 2026-01-19 DOI: 10.1016/j.ibmed.2026.100348

M. Rajesh , K. Swaminathan , K. Vengatesan , Usha Moorthy , Sathishkumar Veerappampalayam Easwaramoorthy

Brain tumours adversely affect patient outcomes owing to their intricacy and the difficulties associated with diagnosis. The accuracy and timeliness of diagnosis are hindered by the subjectivity and unpredictability inherent in manual magnetic resonance imaging (MRI) interpretation. We present novel research on artificial intelligence systems capable of detecting, segmenting, and categorising brain cancers utilising MRI data, which may assist in addressing these issues. The system utilises advanced convolutional neural network (CNN) designs and unique explainability methods; it is designed for application in therapeutic and evidential contexts. This approach addresses deficiencies in cancer categorisation, differentiation, and AI interpretability, hence enhancing the accuracy and reliability of diagnosis. The method's efficacy and practical utility were evidenced through validation on extensive MRI datasets encompassing gliomas, meningiomas, pituitary tumours, and healthy controls. An AI-driven diagnostic tool can increase clinical decision-making, decrease diagnostic error rates, expedite therapy initiation, and improve patient outcomes.

脑肿瘤由于其复杂性和与诊断相关的困难而对患者的预后产生不利影响。诊断的准确性和及时性受到人工磁共振成像（MRI）解释固有的主观性和不可预测性的阻碍。我们提出了利用MRI数据检测、分割和分类脑癌的人工智能系统的新研究，这可能有助于解决这些问题。该系统采用先进的卷积神经网络（CNN）设计和独特的可解释性方法；它被设计用于治疗和证据背景下的应用。这种方法解决了癌症分类、鉴别和人工智能可解释性方面的缺陷，从而提高了诊断的准确性和可靠性。通过对包括胶质瘤、脑膜瘤、垂体瘤和健康对照在内的广泛MRI数据集的验证，证明了该方法的有效性和实用性。人工智能驱动的诊断工具可以增加临床决策，降低诊断错误率，加快治疗启动，改善患者预后。

{"title":"Advanced AI framework for accurate detection and classification of brain tumours from MRI images","authors":"M. Rajesh , K. Swaminathan , K. Vengatesan , Usha Moorthy , Sathishkumar Veerappampalayam Easwaramoorthy","doi":"10.1016/j.ibmed.2026.100348","DOIUrl":"10.1016/j.ibmed.2026.100348","url":null,"abstract":"<div><div>Brain tumours adversely affect patient outcomes owing to their intricacy and the difficulties associated with diagnosis. The accuracy and timeliness of diagnosis are hindered by the subjectivity and unpredictability inherent in manual magnetic resonance imaging (MRI) interpretation. We present novel research on artificial intelligence systems capable of detecting, segmenting, and categorising brain cancers utilising MRI data, which may assist in addressing these issues. The system utilises advanced convolutional neural network (CNN) designs and unique explainability methods; it is designed for application in therapeutic and evidential contexts. This approach addresses deficiencies in cancer categorisation, differentiation, and AI interpretability, hence enhancing the accuracy and reliability of diagnosis. The method's efficacy and practical utility were evidenced through validation on extensive MRI datasets encompassing gliomas, meningiomas, pituitary tumours, and healthy controls. An AI-driven diagnostic tool can increase clinical decision-making, decrease diagnostic error rates, expedite therapy initiation, and improve patient outcomes.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"13 ","pages":"Article 100348"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantum computing in medical diagnostics and treatment: A systematic review of trends, challenges and future directions 医学诊断和治疗中的量子计算：趋势、挑战和未来方向的系统回顾

Intelligence-based medicine

Pub Date : 2026-03-01 Epub Date: 2026-02-16 DOI: 10.1016/j.ibmed.2026.100356

Najnin Sultana Shirin , Md Mehedi Hasan , Md Kishor Morol , Nafiz Fahad , Md Tanzib Hosain , Md Jakir Hossen , Dip Nandi

The growing digitalization of medical diagnostics has resulted in massive amounts of complicated data, including medical imaging and genomic sequences, clinical writing and real-time patient monitoring. Classical machine learning (CML) has achieved amazing success in evaluating such data. But its computational restrictions hinder scalability and efficiency when dealing with high-dimensional biomedical problems. Quantum machine learning (QML) combines the principles of quantum computing (QC) with advanced learning algorithms to offer a transformative paradigm for digital healthcare. This paper provides a systematic overview of QML foundations including quantum data encoding (QDC), variational quantum circuits (VQC), kernel methods, and hybrid quantum classical models. This paper also focuses on their applications in medical imaging, genomics, natural language processing (NLP) for electronic health records, drug discovery and healthcare security. We present comparative insights between classical and quantum approaches such as slow processing of high-dimensional data, limited scalability and inefficiency in complex optimization problems. This review also emphasizes the emerging directions towards quantum-based personalized digital healthcare approaches. By combining medical science with quantum QML has the potential to revolutionize the future of precision diagnostics, treatment optimization and healthcare data security. This study also provides a valuable resource for those interested in quantum computing and researchers who want to stay updated on the fast-growing area.

医疗诊断的日益数字化产生了大量复杂的数据，包括医学成像和基因组序列、临床写作和实时患者监测。经典机器学习（CML）在评估这些数据方面取得了惊人的成功。但在处理高维生物医学问题时，其计算限制阻碍了可扩展性和效率。量子机器学习（QML）将量子计算（QC）原理与先进的学习算法相结合，为数字医疗保健提供了一种变革性范例。本文系统概述了量子机器学习的基础，包括量子数据编码（QDC）、变分量子电路（VQC）、核方法和混合量子经典模型。本文还重点介绍了它们在医学成像、基因组学、电子健康记录的自然语言处理（NLP）、药物发现和医疗安全方面的应用。我们提出了经典和量子方法之间的比较见解，如高维数据处理缓慢，有限的可扩展性和复杂优化问题的低效率。这篇综述还强调了基于量子的个性化数字医疗方法的新兴方向。通过将医学科学与量子相结合，QML有可能彻底改变精准诊断、治疗优化和医疗数据安全的未来。这项研究也为那些对量子计算感兴趣的人以及想要在这个快速发展的领域保持更新的研究人员提供了宝贵的资源。

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引用次数: 0

The second-generation 3D-Printed localization grid for MRI-guided interventional procedures 用于mri引导介入手术的第二代3d打印定位网格

Intelligence-based medicine

Pub Date : 2026-03-01 Epub Date: 2026-01-06 DOI: 10.1016/j.ibmed.2026.100343

Queenie T.K. Shea , Wing Ki Wong , Louis Lee

Patient-specific three-dimensional (3D)-printed magnetic resonance imaging localization grids (MR-Grids) have demonstrated feasibility as an assistive tool for image-guided interventional procedures. However, a limitation of conventional designs arises when the target lesion lies in an imaging plane where the discrete grid markers are not visible. To address this challenge, we proposed a patient-specific MR-Grid incorporating a contrast-filled flexible tubing system arranged in a criss-cross pattern, ensuring visibility across all imaging slices.

The proposed MR-Grid comprises two primary components: (1) a 3D-printed patient-specific scaffold designed to conform to individual anatomical contours, and (2) a contrast-filled flexible tubing system inserted into the grooves of the scaffold.

The MR-Grid was tested in an interventional procedure using a biopsy phantom containing MRI-visible lesions to validate its utility. The grid facilitated precise needle insertion by identifying the optimal entry point under MR guidance, demonstrating its potential to improve accuracy and efficiency in image-guided interventions.

患者特异性三维（3D）打印的磁共振成像定位网格（MR-Grids）已被证明可作为图像引导介入手术的辅助工具。然而，当目标病变位于不可见的离散网格标记的成像平面时，传统设计的局限性就出现了。为了应对这一挑战，我们提出了一种针对患者的MR-Grid，其中包括以纵横交错模式排列的造影剂填充柔性管系统，确保所有成像切片的可见性。提出的MR-Grid包括两个主要组成部分：(1)3d打印的患者专用支架，设计符合个人解剖轮廓，以及(2)插入支架凹槽的对比填充柔性管道系统。MR-Grid在介入手术中进行了测试，使用包含mri可见病变的活检假体来验证其实用性。网格通过识别MR引导下的最佳切入点，促进了针的精确插入，展示了其在图像引导干预中提高准确性和效率的潜力。

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引用次数: 0

The potential of artificial intelligence in advancing neuroscience: A systematic review of current applications and models 人工智能在推进神经科学方面的潜力：对当前应用和模型的系统回顾

Intelligence-based medicine

Pub Date : 2026-03-01 Epub Date: 2025-12-27 DOI: 10.1016/j.ibmed.2025.100338

Fatemeh Afroughi , SeyedAhmad SeyedAlinaghi , Pegah Mirzapour , Shabnam Shirdel , Zohal Parmoon , Mohammad Musa Khorshidi , Somaye Mansouri , Mahdi Sheykhi , Yusuf Popoola , Esmaeil Mehraeen

Introduction

Artificial intelligence (AI) is the simulation of human intelligence, in which machines perform problem-solving like the human brain. AI and neuroscience are interrelated. In this study, a systematic review of current AI models and applications was conducted to consider the potential of AI in advancing neuroscience.

Methods

Relevant articles were selected based on a search in three reputable databases, including Web of Science, PubMed, and Scopus. Two independent researchers conducted the selection process in two stages.

Results

A total of 99 studies (2019–2024) met PRISMA criteria. Of these, 83 studies focused on specific brain disorders—most notably Alzheimer's disease (n = 26), stroke (n = 14), epilepsy (n = 7), and Parkinson's disease (n = 7)—while 22 addressed broader neuroscience applications. A range of AI methods were applied, including traditional machine learning techniques (e.g., Support Vector Machines (SVM), Random Forest) and deep learning approaches (e.g., Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs)), with several studies employing hybrid models. A comparative analysis of study designs revealed a heavy reliance on public datasets (e.g., Alzheimers Disease Neuroimaging Initiative (ADNI)) for Alzheimer's research, while studies on other disorders predominantly utilized private cohorts. Regarding validation, the majority of studies employed internal cross-validation strategies, with fewer utilizing independent external datasets to test generalizability.

Conclusion

The transformative potential of AI in advancing neuroscience lies in its ability to increase diagnostic accuracy, predict disease progression, and enhance imaging techniques. Future research should focus on refining AI methods to enhance generalizability and foster collaborations between AI practitioners and neuroscientists.

人工智能（AI）是对人类智能的模拟，机器可以像人脑一样解决问题。人工智能和神经科学是相互关联的。在本研究中，对当前人工智能模型和应用进行了系统回顾，以考虑人工智能在推进神经科学方面的潜力。方法在Web of Science、PubMed、Scopus三个知名数据库中检索相关文章。两位独立的研究人员分两个阶段进行了选择。结果2019-2024年共有99项研究符合PRISMA标准。其中，83项研究集中于特定的脑部疾病——最著名的是阿尔茨海默病（n = 26）、中风（n = 14）、癫痫（n = 7）和帕金森病（n = 7）——而22项研究涉及更广泛的神经科学应用。应用了一系列人工智能方法，包括传统的机器学习技术（例如，支持向量机（SVM），随机森林）和深度学习方法（例如，卷积神经网络（cnn），生成对抗网络（gan）），以及一些采用混合模型的研究。对研究设计的比较分析显示，阿尔茨海默病研究严重依赖公共数据集（例如，阿尔茨海默病神经影像学倡议（ADNI）），而对其他疾病的研究主要使用私人队列。在验证方面，大多数研究采用内部交叉验证策略，较少使用独立的外部数据集来测试概括性。结论人工智能在推进神经科学方面的变革潜力在于其提高诊断准确性、预测疾病进展和增强成像技术的能力。未来的研究应侧重于改进人工智能方法，以提高通用性，并促进人工智能从业者和神经科学家之间的合作。

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引用次数: 0

Emerging anomaly detection techniques for electronic health records: A survey 新兴的电子健康记录异常检测技术：综述

Intelligence-based medicine

Pub Date : 2026-03-01 Epub Date: 2026-01-21 DOI: 10.1016/j.ibmed.2026.100349

Soumendra N. Bhanja , Haoran Niu , Yang Chen , Olufemi A. Omitaomu , Angela Laurio , Amber Trickey , Vijayalakshmi Sampath , Jonathan R. Nebeker

Background

Anomaly detection in electronic health records (EHRs) is a cornerstone of biomedical informatics, with direct implications for patient safety, clinical decision-making, and the prevention of healthcare fraud. Once guided primarily by simple rule-based methods, the field has advanced rapidly, driven by increased computing power, richer and more detailed health data, and the rise of machine learning and deep learning techniques. The objective of this paper is to provide a comprehensive overview of modern approaches to detecting anomalies in EHRs, outlining their strengths, limitations, and relevance to key healthcare challenges. We review traditional statistical methods alongside newer ML- and DL-based strategies and hybrid models, with particular attention to how these techniques support transparency and build clinical trust.

Methods

This paper presents a thorough and critical survey through systematic review (PRISMA-based) of the latest anomaly detection strategies in time-sequence data domains within electronic health record systems.

Results

We explore a broad spectrum of methodologies, including statistical models, supervised and unsupervised learning approaches, hybrid frameworks, and state-of-the-art ML-based techniques that collectively advance the precision and scalability of detecting anomalies in complex clinical datasets. In addition to mapping current capabilities, we address the enduring challenges that hinder widespread implementation and provide a forward-looking perspective on the future of anomaly detection in the data-rich landscape of modern healthcare.

Summary

The advancement in AI-based approaches is reported along with the basic principles of the individual approaches and their applicability. The increased availability of high-quality data, advancements in DL approaches, and enhanced computation power are leading to more frequent adaptation of DL-based approaches. Emerging DL-based approaches that have been adapted in other domains or recently applied in the EHR domain are also discussed in detail. Although DL-based approaches can improve model predictions by incorporating comorbidities, their application is limited in low-frequency data domains (e.g., when the total available data remains in the single digits). Therefore, the user must carefully consider the application based on data availability.

电子健康记录（EHRs）中的异常检测是生物医学信息学的基石，对患者安全、临床决策和预防医疗欺诈具有直接影响。曾经主要由简单的基于规则的方法指导，该领域在计算能力增强、更丰富和更详细的健康数据以及机器学习和深度学习技术的兴起的推动下迅速发展。本文的目的是全面概述检测电子病历异常的现代方法，概述其优势、局限性以及与关键医疗保健挑战的相关性。我们回顾了传统的统计方法以及新的基于ML和dl的策略和混合模型，特别关注这些技术如何支持透明度和建立临床信任。方法通过系统回顾（基于prisma），对电子健康记录系统中时间序列数据域的最新异常检测策略进行了全面而批判性的调查。我们探索了广泛的方法，包括统计模型、监督和无监督学习方法、混合框架和最先进的基于ml的技术，这些技术共同提高了在复杂临床数据集中检测异常的精度和可扩展性。除了映射当前功能之外，我们还解决了阻碍广泛实施的持久挑战，并提供了在数据丰富的现代医疗保健环境中异常检测的前瞻性视角。综述了基于人工智能方法的研究进展，介绍了各种方法的基本原理及其适用性。高质量数据可用性的增加、深度学习方法的进步以及计算能力的增强导致了基于深度学习的方法的更频繁的适应。还详细讨论了在其他领域中采用或最近在EHR领域中应用的新兴基于dl的方法。尽管基于dl的方法可以通过纳入合并症来改进模型预测，但它们的应用仅限于低频数据域（例如，当总可用数据保持在个位数时）。因此，用户必须根据数据可用性仔细考虑应用程序。

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引用次数: 0

Hybrid spatiotemporal feature fusion for robust lesion detection and tracking in breast ultrasound video data 基于混合时空特征融合的乳腺超声视频数据鲁棒病灶检测与跟踪

Intelligence-based medicine

Pub Date : 2026-03-01 Epub Date: 2025-12-10 DOI: 10.1016/j.ibmed.2025.100330

Radwan Qasrawi , Suliman Thwib , Ghada Issa , Razan AbuGhoush , Hussein AlMasri , Marah Qawasmi , Nael Abu Halaweh

Background

Speckle noise, tissue deformation, low contrast, and frame inconsistencies limit the reliability of traditional breast lesion tracking approaches in ultrasound videos.

Objective

This study aims to develop a robust hybrid framework that integrates advanced image enhancement, deep learning-based detection, and spatiotemporal feature fusion for improved lesion detection and tracking in breast ultrasound video sequences.

Methods

We propose a two-phase computational framework. The first phase employs Contrast-Limited Adaptive Histogram Equalization (CLAHE) for local contrast enhancement, followed by a hybrid denoising strategy combining anisotropic diffusion and unsharp masking to suppress noise and preserve edge sharpness. In the second phase, lesion detection is performed using a YOLOv11-L model, fine-tuned on a curated dataset of annotated breast ultrasound images. For tracking, we utilize Kernelized Correlation Filtering (KCF) enhanced with a Hybrid Spatiotemporal Context (STC) representation. The system is evaluated on a dataset comprising 11,382 ultrasound images and 40 video sequences, with performance assessed using Intersection over Union (IoU), success rate, failure rate, and processing speed.

Results

The proposed framework achieved an IoU of 0.878 for benign lesions and 0.881 for malignant lesions. The integration of STC features and YOLO detection reduced tracking failure rates by over 25 % and improved success rates to 99.0 % for benign and 99.4 % for malignant lesions. The system processed 41–45 frames per second in real time.

Conclusions

Our framework provides an effective solution for real-time lesion detection and tracking in breast ultrasound videos. By enhancing both accuracy and reliability, it supports improved clinical decision-making in breast cancer diagnostics.

斑点噪声、组织变形、低对比度和帧不一致限制了超声视频中传统乳腺病变跟踪方法的可靠性。本研究旨在开发一个强大的混合框架，将先进的图像增强、基于深度学习的检测和时空特征融合相结合，以改进乳腺超声视频序列的病变检测和跟踪。方法提出了一种两阶段计算框架。第一阶段采用对比度限制自适应直方图均衡化（CLAHE）进行局部对比度增强，然后采用各向异性扩散和非锐利掩蔽相结合的混合降噪策略来抑制噪声并保持边缘清晰度。在第二阶段，使用YOLOv11-L模型进行病变检测，并在精心设计的带注释的乳腺超声图像数据集上进行微调。为了跟踪，我们使用了混合时空上下文（STC）表示增强的核化相关滤波（KCF）。该系统在包含11,382张超声图像和40个视频序列的数据集上进行了评估，并使用交汇交汇（IoU）、成功率、故障率和处理速度对性能进行了评估。结果该框架良性病变IoU为0.878，恶性病变IoU为0.881。STC特征与YOLO检测的结合使跟踪失败率降低了25%以上，良性病变的成功率提高到99.0%，恶性病变的成功率提高到99.4%。系统实时处理41-45帧/秒。结论sour框架为乳腺超声视频中病灶的实时检测和跟踪提供了有效的解决方案。通过提高准确性和可靠性，它支持改善乳腺癌诊断的临床决策。

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