Intelligent medicine最新文献

英文中文

Culturally and linguistically informed machine learning for corneal biomechanics: toward inclusive ophthalmic artificial intelligence 基于文化和语言的角膜生物力学机器学习：迈向包容性眼科人工智能

IF 6.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-11-01 DOI: 10.1016/j.imed.2025.05.006

Juhi Yasmeen, Md. Tauseef Qamar, Sayed Mohammed Zeeshan

引用次数: 0

Neuroimaging in narcolepsy 发作性睡病的神经影像学

IF 6.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-08-01 DOI: 10.1016/j.imed.2024.11.005

Yuefan Ding, Fei Zhang, Minglin Li, Jiahe Wang

Narcolepsy is a chronic neurological disorder that disrupts the sleep-wake cycle and manifests in symptoms like excessive daytime sleepiness (EDS), cataplexy, and rapid transitions into rapid eye movement (REM) sleep. Its variable prevalence, genetics, and clinical presentations pose considerable challenges in diagnosis and management. Here, we synthesized the advances in neuroimaging techniques and their substantial contributions to the narcolepsy complex pathology. We analyzed the structural magnetic resonance imaging (MRI) scan findings that highlight gray matter reductions and cortical thinning in patients with narcolepsy. Additionally, we explored findings from diffusion tensor imaging (DTI) scans that shed light on compromises in white matter integrity. Functional MRI and positron emission tomography (PET) scan studies further illuminated neurochemical deficits and altered brain connectivity. The implications of these findings extend beyond diagnosis, suggesting potential targets for neuromodulation therapies and calling for larger, more standardized studies to enhance both our understanding and treatment approaches for narcolepsy. Despite such advances, this field continues to meet challenges, including limitations in sample size and the need for comprehensive longitudinal and multimodal studies. This review highlighted the potential of neuroimaging combined with machine learning and advanced analytics, which help to discover novel biomarkers, refine the comprehension of narcolepsy and its neurochemical intricacies, and improve the therapeutic strategies.

嗜睡症是一种慢性神经系统疾病，它会扰乱睡眠-觉醒周期，表现为白天过度嗜睡（EDS）、猝厥和快速过渡到快速眼动睡眠（REM）。其不同的患病率，遗传学和临床表现构成了相当大的挑战，在诊断和管理。在这里，我们综合了神经成像技术的进展及其对发作性睡症复杂病理的重大贡献。我们分析了结构磁共振成像（MRI）扫描结果，突出了发作性睡病患者的灰质减少和皮层变薄。此外，我们探索了扩散张量成像（DTI）扫描的发现，揭示了白质完整性的妥协。功能性MRI和正电子发射断层扫描（PET）研究进一步揭示了神经化学缺陷和大脑连接的改变。这些发现的意义超出了诊断，提示了神经调节疗法的潜在目标，并呼吁进行更大规模、更标准化的研究，以增强我们对发作性睡病的理解和治疗方法。尽管取得了这些进展，但这一领域仍面临挑战，包括样本量的限制以及需要进行全面的纵向和多模式研究。这篇综述强调了神经成像与机器学习和高级分析相结合的潜力，这有助于发现新的生物标志物，完善对发作性睡病及其神经化学复杂性的理解，并改进治疗策略。

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引用次数: 0

Incident-induced attention-based deep learning model for early warning of sepsis onset 基于事件诱导注意力的脓毒症发病早期预警深度学习模型

IF 6.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-08-01 DOI: 10.1016/j.imed.2024.11.004

Mutian Yang , Jiandong Gao , Yuan Xu , Jingyuan Xie , Yihe Zhao , Jingyuan Liu , Hua Zhou , Ji Wu

Background

Accurate early warning of sepsis onset is crucial for reducing mortality. However, the inter-individual heterogeneity in clinical manifestations of sepsis leads to significant sparsity of data. The current time series analysis methods attempt to interpolate highly sparse sepsis data, yielding unsatisfactory results. In this study, we aimed to develop an efficient artificial intelligence approach for early warning of sepsis onset.

Methods

The I2former model, an incident-induced attention-based architecture, was proposed to address the challenges posed by sparse medical data. This model employs a novel increment entropy encoding strategy to extract clinically significant features from sparse data, effectively transforming the unavailable data into valuable insights. The training data were sourced from MIMIC-IV v2.2 and eICU v2.0, with external validation from Beijing Tsinghua Changgung Hospital. Five advanced models, including the Autoformer, Timesnet, Informer, Reformer, and DLinear, currently in use were used for comparison.

Results

Five metrics used for classification indicated that the I2former significantly outperformed the 5 advanced time series analysis methods, achieving area under the receiver operating characteristic (AUROC), area under the precision-recall curve (AUPRC), Matthews correlation coefficient (MCC), F1-score, and accuracy of 0.886, 0.529, 0.449, and 0.917, respectively. Furthermore, external validation using the data from Beijing Tsinghua Changgung Hospital demonstrated that the model provides accurate early warnings, on average of 15.5 h prior to sepsis onset.

Conclusion

Therefore, I2former is proposed for accurate early warning of sepsis onset. Five crucial metrics for classification underscored the substantial advantages of I2former in managing sparse data, while highlighting its potential application and value in the field of medical data analysis.

背景：脓毒症发病的准确早期预警对于降低死亡率至关重要。然而，脓毒症临床表现的个体间异质性导致数据明显稀疏。目前的时间序列分析方法试图插值高度稀疏的脓毒症数据，结果不令人满意。在这项研究中，我们旨在开发一种有效的人工智能方法来早期预警败血症的发作。方法针对医疗数据稀疏带来的挑战，提出了基于事件诱导注意力的i2模型。该模型采用一种新颖的增量熵编码策略，从稀疏数据中提取临床显著特征，有效地将不可用数据转化为有价值的见解。训练数据来源于MIMIC-IV v2.2和eICU v2.0，并由北京清华长工医院进行外部验证。采用目前使用的Autoformer、Timesnet、Informer、Reformer、DLinear五种先进的模型进行比较。结果采用5个指标进行分类，结果表明前者显著优于5种先进的时间序列分析方法，分别达到接收者工作特征下面积（AUROC）、精确召回率曲线下面积（AUPRC）、马修斯相关系数（MCC）、f1评分和准确率分别为0.886、0.529、0.449和0.917。此外，使用来自北京清华长工医院的数据进行的外部验证表明，该模型提供了准确的早期预警，平均在脓毒症发作前15.5小时。结论I2former可用于脓毒症发病的准确预警。分类的五个关键指标强调了I2former在管理稀疏数据方面的巨大优势，同时突出了其在医疗数据分析领域的潜在应用和价值。

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引用次数: 0

Advancement in blood pressure abnormality detection and interpretation using large language models 基于大语言模型的血压异常检测与解释研究进展

IF 6.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-08-01 DOI: 10.1016/j.imed.2025.04.002

Abdul Rahman , Shahab Saquib Sohail , Irfan Alam , Dag Øivind Madsen

引用次数: 0

Reframing disease prediction models: a commentary on hybrid approaches and entropic limitations 重构疾病预测模型：对混合方法和熵限制的评论

IF 6.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-08-01 DOI: 10.1016/j.imed.2025.05.001

Ashfaq Ahmad Najar, Daood Saleem

引用次数: 0

Consensus on the research and application of artificial intelligence in coronary computed tomography angiography 人工智能在冠状动脉计算机断层造影中的研究与应用共识

IF 6.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-08-01 DOI: 10.1016/j.imed.2024.11.006

Longjiang Zhang , Qian Chen , Chun Xiang Tang , Zhao Shi , Tongyuan Liu , Chunhong Hu , Bin Lu , Zhengyu Jin , Guangming Lu

Coronary computed tomography angiography (CCTA), which enables noninvasive assessment of luminal stenosis and atherosclerotic plaque components, has become the first-line technique for evaluating coronary artery disease. Artificial intelligence (AI) has the potential to revolutionize the CCTA workflow. However, it is crucial to evaluate the effectiveness and feasibility of AI algorithms before their clinical deployment. This expert consensus proposes three fundamental elements of research designs of AI in CCTA and offers corresponding recommendations. The consensus also reviews the existing evidence on AI applications in CCTA and provides recommendations on the current clinical applications of AI, including image acquisition and reconstruction, postprocessing, diagnosis, prognostic prediction, guiding prevention and treatment, and cardiovascular disease prevention.

冠状动脉ct血管造影（CCTA）可以无创地评估管腔狭窄和动脉粥样硬化斑块成分，已成为评估冠状动脉疾病的一线技术。人工智能（AI）有可能彻底改变CCTA的工作流程。然而，在临床部署人工智能算法之前，评估其有效性和可行性至关重要。这一专家共识提出了CCTA中AI研究设计的三个基本要素，并提出了相应的建议。共识还回顾了人工智能在CCTA中应用的现有证据，并就目前人工智能的临床应用提出了建议，包括图像采集与重建、后处理、诊断、预后预测、指导预防与治疗、心血管疾病预防等。

引用次数: 0

Enhancing the evaluation of large language models in healthcare: addressing methodological gaps and entropic considerations 加强医疗保健中大型语言模型的评估：解决方法差距和熵的考虑

IF 6.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-08-01 DOI: 10.1016/j.imed.2025.05.002

Daood Saleem, Mohd Rafi Lone

引用次数: 0

Artificial intelligence and digital twins: revolutionizing diabetes care for tomorrow 人工智能和数字双胞胎：革新未来的糖尿病治疗

IF 6.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-08-01 DOI: 10.1016/j.imed.2025.05.004

Shaocheng Wang , Mengyao An , Siyong Lin , SreyRam Kuy , Dong Li

Artificial intelligence (AI) and digital twin technologies exhibit significant potential in analyzing and integrating multidimensional datasets and offer novel perspectives for the management of chronic diseases including diabetes. These technologies offer opportunities for personalizing treatment and potentially reversing the conditions. This review systematically evaluated the advantages and limitations of AI applications, potential for predictive analytics in formulating personalized management strategies, and practical roles of AI and digital twin technologies in diabetes diagnosis and treatment. Special attention was given to their strengths and weaknesses in disease prediction, early detection, and development of individualized management strategies.

AI algorithms have demonstrated great efficiency in analyzing large datasets, aiding in the early identification and intervention of prediabetes. Machine learning algorithms, including deep learning neural networks, integrate lifestyle, genetic, and other influencing factors to accurately predict the progression of prediabetes to diabetes. Moreover, AI-driven wearable devices and mobile applications provide real-time monitoring and personalized guidance, thereby effectively mitigating diabetes. This study also explored the challenges of integrating AI and digital twin technologies into clinical practice for diabetes management and broader healthcare domains, focusing on data privacy, need for diverse and comprehensive datasets, and the importance of integrating AI tools into clinical workflows.

人工智能（AI）和数字孪生技术在分析和整合多维数据集方面显示出巨大的潜力，并为包括糖尿病在内的慢性疾病的管理提供了新的视角。这些技术为个性化治疗提供了机会，并有可能扭转病情。本文系统地评估了人工智能应用的优势和局限性，预测分析在制定个性化管理策略方面的潜力，以及人工智能和数字孪生技术在糖尿病诊断和治疗中的实际作用。特别注意了它们在疾病预测、早期发现和制定个性化管理策略方面的优缺点。人工智能算法在分析大型数据集、帮助早期识别和干预前驱糖尿病方面表现出极大的效率。包括深度学习神经网络在内的机器学习算法整合了生活方式、遗传和其他影响因素，以准确预测糖尿病前期向糖尿病的进展。此外，人工智能驱动的可穿戴设备和移动应用提供实时监测和个性化指导，从而有效缓解糖尿病。本研究还探讨了将人工智能和数字孪生技术集成到糖尿病管理和更广泛的医疗保健领域的临床实践中的挑战，重点关注数据隐私，对多样化和全面数据集的需求，以及将人工智能工具集成到临床工作流程中的重要性。

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引用次数: 0

Beyond decision support: large language models such as ChatGPT and DeepSeek and the future of patient empathy in artificial intelligence 除决策支持外：ChatGPT和DeepSeek等大型语言模型，以及人工智能中患者同理心的未来

IF 6.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-08-01 DOI: 10.1016/j.imed.2025.04.001

Shahab Saquib Sohail , Dag Øivind Madsen

引用次数: 0

Source independent multiple-domain adaptation for knee osteoarthritis cartilage and meniscus segmentation in clinical magnetic resonance imaging 临床磁共振成像中膝关节骨关节炎软骨和半月板分割的源独立多域自适应

IF 6.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-08-01 DOI: 10.1016/j.imed.2024.12.002

Sheheryar Khan , Siyue Li , Fan Xiao , Kevin Ho , Michael Ong , James Griffith , Weitian Chen

Background

Generalized knee tissue segmentation, such as cartilage and meniscus in magnetic resonance imaging (MRI), plays a vital role in the clinical assessment of knee osteoarthritis (OA). However, domain variability between MRI datasets poses a significant challenge for the application of robust segmentation methods in real-world clinical settings. Existing unsupervised domain adaptation (UDA) approaches, which rely on one-to-one assumptions between the source and target domains, often fail to preserve knee tissues such as cartilage and meniscus, which are critical for OA diagnosis in diverse clinical settings.

Methods

We propose a source-independent segmentation approach tailored for multi-domain knee MRI datasets. Our method emphasizes knee tissue regions to reduce domain gaps and label inconsistencies. By introducing a stepwise adaptation strategy, segmentation performance was refined progressively from intermediate domains to the final target domain. Pseudo-label attention mechanisms were integrated into the adaptation pipeline, enabling iterative fine-tuning of domain-specific segmentations while leveraging unidirectional generative adversarial networks to enhance tissue-specific adaptation. This iterative training process ensures the generation of reliable pseudo-labels, thereby improving segmentation accuracy in diverse clinical MRI datasets.

Results

We demonstrated the effectiveness of our approach on the OA initiative dataset as the source domain and self-collected, T1-weighted fast field echo (T1FFE) as the intermediate domain and three-dimensional fast spin echo (3D FSE) as the final target domain. Our method achieved an average dice scores of 0.8701 and 0.7990 for source and target domains, respectively, surpassing the typical UDA methods explored in our experiments.

Conclusion

The experiments conducted on clinical MRI data, spanning OA severity from healthy knees to KL Grades 1–4, validated the effectiveness of the proposed domain adaptation method in precise segmentation of the cartilage and meniscus.

广泛的膝关节组织分割，如磁共振成像（MRI）中的软骨和半月板，在膝关节骨关节炎（OA）的临床评估中起着至关重要的作用。然而，MRI数据集之间的区域可变性对现实世界临床环境中鲁棒分割方法的应用提出了重大挑战。现有的无监督域适应（UDA）方法依赖于源域和目标域之间的一对一假设，通常无法保护膝关节组织，如软骨和半月板，而这些组织在不同的临床环境中对OA诊断至关重要。方法针对多域膝关节MRI数据集，提出一种与源无关的分割方法。我们的方法强调膝关节组织区域，以减少区域间隙和标记不一致。通过引入逐步适应策略，从中间域到最终目标域的分割性能逐步得到改进。伪标签注意机制被集成到适应管道中，实现了特定领域分割的迭代微调，同时利用单向生成对抗网络来增强组织特异性适应。这种迭代训练过程保证了生成可靠的伪标签，从而提高了不同临床MRI数据集的分割精度。结果以OA主动数据集为源域，以自采集数据集为中间域，以t1加权快速场回波（T1FFE）为中间域，以三维快速自旋回波（3D FSE）为最终目标域，验证了该方法的有效性。我们的方法在源域和目标域的平均骰子得分分别为0.8701和0.7990，超过了我们在实验中探索的典型UDA方法。基于临床MRI数据进行的实验，涵盖了从健康膝关节到KL分级1-4级的OA严重程度，验证了所提出的区域适应方法在软骨和半月板精确分割方面的有效性。

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