Intelligent medicine最新文献

{"title":"Neuroimaging in narcolepsy","authors":"Yuefan Ding,&nbsp;Fei Zhang,&nbsp;Minglin Li,&nbsp;Jiahe Wang","doi":"10.1016/j.imed.2024.11.005","DOIUrl":"10.1016/j.imed.2024.11.005","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 3","pages":"Pages 195-208"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908077","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}

{"title":"Incident-induced attention-based deep learning model for early warning of sepsis onset","authors":"Mutian Yang ,&nbsp;Jiandong Gao ,&nbsp;Yuan Xu ,&nbsp;Jingyuan Xie ,&nbsp;Yihe Zhao ,&nbsp;Jingyuan Liu ,&nbsp;Hua Zhou ,&nbsp;Ji Wu","doi":"10.1016/j.imed.2024.11.004","DOIUrl":"10.1016/j.imed.2024.11.004","url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 3","pages":"Pages 187-194"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908076","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}

{"title":"Advancement in blood pressure abnormality detection and interpretation using large language models","authors":"Abdul Rahman ,&nbsp;Shahab Saquib Sohail ,&nbsp;Irfan Alam ,&nbsp;Dag Øivind Madsen","doi":"10.1016/j.imed.2025.04.002","DOIUrl":"10.1016/j.imed.2025.04.002","url":null,"abstract":"","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 3","pages":"Pages 245-246"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908152","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}

{"title":"Reframing disease prediction models: a commentary on hybrid approaches and entropic limitations","authors":"Ashfaq Ahmad Najar,&nbsp;Daood Saleem","doi":"10.1016/j.imed.2025.05.001","DOIUrl":"10.1016/j.imed.2025.05.001","url":null,"abstract":"","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 3","pages":"Page 249"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908155","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}

{"title":"Consensus on the research and application of artificial intelligence in coronary computed tomography angiography","authors":"Longjiang Zhang ,&nbsp;Qian Chen ,&nbsp;Chun Xiang Tang ,&nbsp;Zhao Shi ,&nbsp;Tongyuan Liu ,&nbsp;Chunhong Hu ,&nbsp;Bin Lu ,&nbsp;Zhengyu Jin ,&nbsp;Guangming Lu","doi":"10.1016/j.imed.2024.11.006","DOIUrl":"10.1016/j.imed.2024.11.006","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 3","pages":"Pages 234-242"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908160","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}

{"title":"Enhancing the evaluation of large language models in healthcare: addressing methodological gaps and entropic considerations","authors":"Daood Saleem,&nbsp;Mohd Rafi Lone","doi":"10.1016/j.imed.2025.05.002","DOIUrl":"10.1016/j.imed.2025.05.002","url":null,"abstract":"","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 3","pages":"Page 244"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908162","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}

{"title":"Artificial intelligence and digital twins: revolutionizing diabetes care for tomorrow","authors":"Shaocheng Wang ,&nbsp;Mengyao An ,&nbsp;Siyong Lin ,&nbsp;SreyRam Kuy ,&nbsp;Dong Li","doi":"10.1016/j.imed.2025.05.004","DOIUrl":"10.1016/j.imed.2025.05.004","url":null,"abstract":"<div><div>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.</div><div>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.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 3","pages":"Pages 173-177"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908074","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}

{"title":"Beyond decision support: large language models such as ChatGPT and DeepSeek and the future of patient empathy in artificial intelligence","authors":"Shahab Saquib Sohail ,&nbsp;Dag Øivind Madsen","doi":"10.1016/j.imed.2025.04.001","DOIUrl":"10.1016/j.imed.2025.04.001","url":null,"abstract":"","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 3","pages":"Page 243"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908161","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}

{"title":"Source independent multiple-domain adaptation for knee osteoarthritis cartilage and meniscus segmentation in clinical magnetic resonance imaging","authors":"Sheheryar Khan ,&nbsp;Siyue Li ,&nbsp;Fan Xiao ,&nbsp;Kevin Ho ,&nbsp;Michael Ong ,&nbsp;James Griffith ,&nbsp;Weitian Chen","doi":"10.1016/j.imed.2024.12.002","DOIUrl":"10.1016/j.imed.2024.12.002","url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 3","pages":"Pages 209-221"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908158","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}

{"title":"Embracing the challenges of digital orthopedics in the age of artificial intelligence","authors":"Om Prakash Choudhary","doi":"10.1016/j.imed.2025.06.001","DOIUrl":"10.1016/j.imed.2025.06.001","url":null,"abstract":"","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 3","pages":"Pages 247-248"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908157","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}