Intelligent medicine最新文献

英文中文

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 Epub Date: 2025-06-09 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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引用次数: 0

Embracing the challenges of digital orthopedics in the age of artificial intelligence 迎接人工智能时代数字骨科的挑战

IF 6.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-08-01 Epub Date: 2025-06-19 DOI: 10.1016/j.imed.2025.06.001

Om Prakash Choudhary

引用次数: 0

Iteration and evaluation of digital interface for clinical surgery design from the usability perspective 可用性视角下临床外科设计数字界面的迭代与评价

IF 6.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-08-01 Epub Date: 2025-07-04 DOI: 10.1016/j.imed.2024.12.003

Bowen Sun , Saisai Li , Dijia Li , Xin Peng , Bowen Song , Yirui Wang , Zixu Zhang , Xia Wang , Xu He

Objective Based on the development background of digital medical technology, this study aimed to establish design guidelines and references in relevant fields to better serve clinical medical treatment using intelligent technology to enhance the usability of the interaction interface of robotic surgical systems and reduce potential human-factor risks during digital surgery.

Methods Considering the robotic liver cancer ablation surgery system as the research object, subjective and objective evaluation indicators were established from 3 dimensions of effectiveness, efficiency, and satisfaction based on the usability theory. Using the hierarchical task analysis method, usability experiments were conducted to collect relevant data. Feedback on issues during the experimental process was obtained through observation and interviews. Failure mode and effect analysis and fault tree analysis were used to assess risk levels and formulate design strategies.

Results The interface design of the liver cancer ablation surgery robot was iteratively optimized. The results showed that the interface after iteration improved in skilled operation time, subjective evaluation scores, risk priority number value, and risk level. The rationality of the scheme was verified, and interface design paradigm was constructed based on intelligent technology.

Conclusion After improving the design, the interface effectively reduced the frequency of problems and average skilled operation time, thereby, improving the subjective satisfaction score of users.

目的基于数字化医疗技术的发展背景，建立相关领域的设计指南和参考，利用智能技术更好地服务于临床医疗，增强机器人手术系统交互界面的可用性，降低数字化手术过程中潜在的人为因素风险。方法以机器人肝癌消融手术系统为研究对象，基于可用性理论，从有效性、效率、满意度三个维度建立主客观评价指标。采用分层任务分析法，进行可用性实验，收集相关数据。通过观察和访谈，对实验过程中出现的问题进行反馈。采用故障模式及影响分析和故障树分析来评估风险水平，制定设计策略。结果对肝癌消融手术机器人的界面设计进行了迭代优化。结果表明，迭代后的界面在熟练操作时间、主观评价分数、风险优先级数值、风险等级等方面均有提高。验证了方案的合理性，构建了基于智能技术的界面设计范式。结论改进设计后的界面有效降低了出现问题的频率和平均熟练操作时间，从而提高了用户的主观满意度。

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引用次数: 0

Artificial intelligence in the care of patients with rectal cancer undergoing neoadjuvant chemoradiation and intentional watchful waiting: a literature review 人工智能在直肠癌新辅助放化疗患者护理和有意观察等待中的应用：文献综述

IF 6.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-08-01 Epub Date: 2025-06-19 DOI: 10.1016/j.imed.2025.05.003

Boyang Qu, Aiwen Wu

Rectal cancer remains a major global health challenge, prompting ongoing efforts to optimize treatment strategies. In recent years, organ-preserving approaches—particularly the “watch-and-wait” strategy—have gained growing interest. Concurrently, the advent of artificial intelligence (AI) has opened new avenues in personalized oncology. This review explored the emerging role of AI in the individualized management of rectal cancer, with a focus on its potential to improve treatment outcomes and patient prognosis. Herein, we provide a comprehensive synthesis of recent studies investigating AI applications in predicting pathological complete response, metastasis, and disease-free survival following neoadjuvant therapy. These studies employ diverse data modalities, including radiomics (magnetic resonance imaging (MRI), computerized tomography (CT), and endoscopy), clinical parameters, and other omics-based features. The study evaluated the predictive models developed using machine learning and deep learning algorithms, discussing their performance metrics, strengths, and limitations. Despite the ongoing challenges—such as limited data availability, lack of model interpretability, and suboptimal predictive accuracy—AI has demonstrated potential to outperform conventional assessment methods in select areas. These findings may highlight the growing significance of AI in supporting personalized, evidence-based decision-making in rectal cancer care.

直肠癌仍然是一个主要的全球健康挑战，促使人们不断努力优化治疗策略。近年来，器官保存方法——尤其是“观察和等待”策略——获得了越来越多的兴趣。同时，人工智能（AI）的出现为个性化肿瘤学开辟了新的途径。本综述探讨了人工智能在直肠癌个体化治疗中的新作用，重点关注其改善治疗结果和患者预后的潜力。在此，我们综合了最近的研究，研究了人工智能在预测新辅助治疗后的病理完全缓解、转移和无病生存方面的应用。这些研究采用不同的数据模式，包括放射组学（磁共振成像（MRI）、计算机断层扫描（CT）和内窥镜检查）、临床参数和其他基于组学的特征。该研究评估了使用机器学习和深度学习算法开发的预测模型，讨论了它们的性能指标、优势和局限性。尽管存在持续的挑战，例如有限的数据可用性，缺乏模型可解释性，以及次优的预测准确性，但人工智能已经证明了在某些领域优于传统评估方法的潜力。这些发现可能突出了人工智能在支持直肠癌治疗中个性化、循证决策方面日益重要的意义。

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引用次数: 0

Guide for Authors 作者指南

IF 6.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-08-01 Epub Date: 2025-08-28 DOI: 10.1016/S2667-1026(25)00073-7

引用次数: 0

Evaluating large language models and agents in healthcare: key challenges in clinical applications 评估医疗保健中的大型语言模型和代理：临床应用中的关键挑战

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-05-01 Epub Date: 2025-03-31 DOI: 10.1016/j.imed.2025.03.002

Xiaolan Chen , Jiayang Xiang , Shanfu Lu , Yexin Liu , Mingguang He , Danli Shi

Large language models (LLMs) have emerged as transformative tools with significant potential across healthcare and medicine. In clinical settings, they hold promises for tasks ranging from clinical decision support to patient education. Advances in LLM agents further broaden their utility by enabling multimodal processing and multitask handling in complex clinical workflows. However, evaluating the performance of LLMs in medical contexts presents unique challenges due to the high-risk nature of healthcare and the complexity of medical data. This paper provides a comprehensive overview of current evaluation practices for LLMs and LLM agents in medicine. We contributed 3 main aspects: First, we summarized data sources used in evaluations, including existing medical resources and manually designed clinical questions, offering a basis for LLM evaluation in medical settings. Second, we analyzed key medical task scenarios: closed-ended tasks, open-ended tasks, image processing tasks, and real-world multitask scenarios involving LLM agents, thereby offering guidance for further research across different medical applications. Third, we compared evaluation methods and dimensions, covering both automated metrics and human expert assessments, while addressing traditional accuracy measures alongside agent-specific dimensions, such as tool usage and reasoning capabilities. Finally, we identified key challenges and opportunities in this evolving field, emphasizing the need for continued research and interdisciplinary collaboration between healthcare professionals and computer scientists to ensure safe, ethical, and effective deployment of LLMs in clinical practice.

大型语言模型（llm）已经成为在医疗保健和医学领域具有巨大潜力的变革性工具。在临床环境中，他们承担着从临床决策支持到患者教育等任务的承诺。LLM代理的进步通过在复杂的临床工作流程中实现多模式处理和多任务处理进一步扩大了它们的效用。然而，由于医疗保健的高风险性质和医疗数据的复杂性，评估法学硕士在医学背景下的表现提出了独特的挑战。本文提供了一个全面的概述当前的评估实践法学硕士和法学硕士代理人在医学。我们主要贡献了3个方面：首先，我们总结了评估中使用的数据源，包括现有的医疗资源和人工设计的临床问题，为医疗环境中的LLM评估提供了基础。其次，我们分析了关键的医疗任务场景：封闭式任务、开放式任务、图像处理任务和涉及LLM代理的真实多任务场景，从而为不同医疗应用的进一步研究提供指导。第三，我们比较了评估方法和维度，涵盖了自动化度量和人类专家评估，同时解决了传统的准确性度量以及特定于代理的维度，如工具使用和推理能力。最后，我们确定了这一不断发展的领域的关键挑战和机遇，强调了医疗保健专业人员和计算机科学家之间持续研究和跨学科合作的必要性，以确保法学硕士在临床实践中的安全、道德和有效部署。

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引用次数: 0

DeepSeek and the future of drug discovery: a correspondence on artificial intelligence integration DeepSeek与药物发现的未来：人工智能集成通信

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-05-01 Epub Date: 2025-03-20 DOI: 10.1016/j.imed.2025.03.001

Faiza Farhat

引用次数: 0

Guide for Authors 作者指南

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-05-01 Epub Date: 2025-06-03 DOI: 10.1016/S2667-1026(25)00045-2

引用次数: 0

Application of multimodal deep learning in the auxiliary diagnosis and treatment of dermatological diseases 多模态深度学习在皮肤病辅助诊断与治疗中的应用

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-05-01 Epub Date: 2025-03-01 DOI: 10.1016/j.imed.2024.10.002

Ting Li , Bowei Li , Yuying Jia , Lian Duan , Ping Sun , Xiaozhen Li , Xiaodong Yang , Hong Cai

Skin diseases are important factors affecting health and quality of life, especially in rural areas where medical resources are limited. Early and accurate diagnosis can reduce unnecessary health and economic losses. However, traditional visual diagnosis poses a high demand on both doctors’ experience and the examination equipment, and there is a risk of missed diagnosis and misdiagnosis. Recently, advances in artificial intelligence technology, particularly deep learning, have resulted in the use of unimodal computer-aided diagnosis and treatment technologies based on skin images in dermatology. However, due to the small amount of information contained in unimodality, this technology cannot fully demonstrate the advantages of multimodal data in the real-world medical environment. Multimodal data fusion can fully integrate various types of data to help doctors make more accurate clinical decisions. This review aimed to provide a comprehensive overview of multimodal data and deep learning methods that could help dermatologists diagnose and treat skin diseases.

皮肤病是影响健康和生活质量的重要因素，特别是在医疗资源有限的农村地区。早期和准确的诊断可以减少不必要的健康和经济损失。然而，传统的视觉诊断对医生的经验和检查设备的要求都很高，存在漏诊和误诊的风险。最近，人工智能技术的进步，特别是深度学习，已经导致在皮肤病学中使用基于皮肤图像的单模计算机辅助诊断和治疗技术。然而，由于单模态所包含的信息量较少，该技术无法在现实医疗环境中充分体现多模态数据的优势。多模式数据融合可以充分整合各类数据，帮助医生做出更准确的临床决策。本综述旨在提供多模态数据和深度学习方法的全面概述，这些方法可以帮助皮肤科医生诊断和治疗皮肤病。

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Digital orthopedics: the third technological wave of orthopedics 数字骨科：骨科的第三次技术浪潮

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-05-01 Epub Date: 2024-10-28 DOI: 10.1016/j.imed.2024.09.003

Jiayao Zhang , Zhewei Ye

As an emerging interdisciplinary field, digital orthopedics is hailed as the third technological wave in orthopedics, with its applications gradually expanding into various areas and continuously innovating orthopedic clinical practice. Through advanced technologies such as 3D printing, extended reality, finite-element analysis, robotic-assisted surgery, and artificial intelligence, the diagnosis, treatment, and rehabilitation of orthopedic diseases have become more convenient, precise, and personalized. This article primarily introduced the main advantages and applications of digital orthopedic technology and evaluates its clinical efficacy and development potential, providing important references for future research and clinical practice.

数字骨科作为一门新兴的交叉学科，被誉为骨科领域的第三次技术浪潮，其应用领域逐渐扩展到各个领域，不断创新骨科临床实践。通过3D打印、扩展现实、有限元分析、机器人辅助手术、人工智能等先进技术，骨科疾病的诊断、治疗和康复变得更加便捷、精准和个性化。本文主要介绍了数字骨科技术的主要优势和应用，并对其临床疗效和发展潜力进行了评价，为今后的研究和临床实践提供重要参考。

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