NPJ Digital Medicine最新文献_第5页

A systematic review of digital and imaging technologies for measuring fatigue in immune mediated inflammatory diseases

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-03-07 DOI: 10.1038/s41746-025-01538-w

Haley Mozier, Philip S. Murphy, Robert L. Janiczek, Mark Morris, Jennifer G. Cox, Hung Nguyen

{"title":"A systematic review of digital and imaging technologies for measuring fatigue in immune mediated inflammatory diseases","authors":"Haley Mozier, Philip S. Murphy, Robert L. Janiczek, Mark Morris, Jennifer G. Cox, Hung Nguyen","doi":"10.1038/s41746-025-01538-w","DOIUrl":"https://doi.org/10.1038/s41746-025-01538-w","url":null,"abstract":"Chronic fatigue greatly impacts the quality of life in individuals with immune-mediated inflammatory disease (IMID). Currently, fatigue assessment relies on patient-reported outcome (PRO) questionnaires. A systematic review following PRISMA guidelines was conducted to explore how digital and imaging technologies have been used to measure fatigue. PubMed and Cochrane Library were searched from 2003 to June 2023. Study quality was assessed using the STROBE checklist for observational studies. The database search identified 1541 studies; 16 were selected for inclusion, including three clinical trial reports. Disease cohorts included in this review were rheumatoid arthritis, primary Sjögren’s syndrome, systemic lupus erythematosus, and inflammatory bowel disease. The majority of the studies found significant associations between fatigue, as assessed by PROs, and various digital and imaging endpoints. However, the studies were limited by a small sample size and short duration. This review stresses the need for additional research on fatigue using innovative digital and imaging modalities.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"7 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Systematic review and meta-analysis of artificial intelligence in classifying HER2 status in breast cancer immunohistochemistry

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-03-06 DOI: 10.1038/s41746-025-01483-8

Daniel Arruda Navarro Albuquerque, Matheus Trotta Vianna, Luana Alencar Fernandes Sampaio, Andrei Vasiliu, Eduardo Henrique Cunha Neves Filho

{"title":"Systematic review and meta-analysis of artificial intelligence in classifying HER2 status in breast cancer immunohistochemistry","authors":"Daniel Arruda Navarro Albuquerque, Matheus Trotta Vianna, Luana Alencar Fernandes Sampaio, Andrei Vasiliu, Eduardo Henrique Cunha Neves Filho","doi":"10.1038/s41746-025-01483-8","DOIUrl":"https://doi.org/10.1038/s41746-025-01483-8","url":null,"abstract":"The DESTINY-Breast04 trial has recently demonstrated survival benefits of trastuzumab-deruxtecan (T-DXd) in metastatic breast cancer patients with low Human Epidermal Growth Factor Receptor 2 (HER2) expression. Accurate differentiation of HER2 scores has now become crucial. However, visual immunohistochemistry (IHC) scoring is labour-intensive and prone to high interobserver variability, and artificial intelligence (AI) has emerged as a promising tool in diagnostic medicine. We conducted a diagnostic meta-analysis to evaluate AI’s performance in classifying HER2 IHC scores, demonstrating high accuracy in predicting T-DXd eligibility, with a pooled sensitivity of 0.97 [95% CI 0.96–0.98] and specificity of 0.82 [95% CI 0.73–0.88]. Meta-regression revealed better performance with deep learning and patch-based analysis, while performance declined in externally validated and those utilising commercially available algorithms. Our findings indicate that AI holds promising potential in accurately identifying HER2-low patients and excels in distinguishing 2+ and 3+ scores.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"123 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Addressing contemporary threats in anonymised healthcare data using privacy engineering

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-03-06 DOI: 10.1038/s41746-025-01520-6

Sanjiv M. Narayan, Nitin Kohli, Megan M. Martin

引用次数: 0

Regulatory considerations for successful implementation of digital endpoints in clinical trials for drug development

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-03-06 DOI: 10.1038/s41746-025-01513-5

Gül Erdemli, Tina Murphy, Sarah Walinsky

引用次数: 0

Implementing large language models in healthcare while balancing control, collaboration, costs and security

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-03-06 DOI: 10.1038/s41746-025-01476-7

Fabio Dennstädt, Janna Hastings, Paul Martin Putora, Max Schmerder, Nikola Cihoric

引用次数: 0

Machine learning on interictal intracranial EEG predicts surgical outcome in drug resistant epilepsy

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-03-05 DOI: 10.1038/s41746-025-01531-3

Hmayag Partamian, Saeed Jahromi, Ludovica Corona, M. Scott Perry, Eleonora Tamilia, Joseph R. Madsen, Jeffrey Bolton, Scellig S. D. Stone, Phillip L. Pearl, Christos Papadelis

{"title":"Machine learning on interictal intracranial EEG predicts surgical outcome in drug resistant epilepsy","authors":"Hmayag Partamian, Saeed Jahromi, Ludovica Corona, M. Scott Perry, Eleonora Tamilia, Joseph R. Madsen, Jeffrey Bolton, Scellig S. D. Stone, Phillip L. Pearl, Christos Papadelis","doi":"10.1038/s41746-025-01531-3","DOIUrl":"https://doi.org/10.1038/s41746-025-01531-3","url":null,"abstract":"Surgical success for patients with focal drug resistant epilepsy (DRE) relies on accurate localization of the epileptogenic zone (EZ). Currently, no exam delineates this zone unambiguously. Instead, the EZ is approximated by the area where seizures begin, which is identified manually through a tedious process that is prone to errors and biases. More importantly, resection of this area does not always predict good surgical outcome. Here, we propose an artificially intelligent, patient-specific framework that automatically identifies the EZ requiring little to no input from clinicians, without having to wait for a seizure to occur. The framework transforms interictal intracranial electroencephalography data into spatiotemporal representations of brain activity discriminating the interictal epileptogenic network from background activity. The epileptogenic network delineates the EZ with high precision and predicts surgical outcome. Our framework eliminates the need for manual data inspection, reduces prolonged monitoring, and enhances surgical planning for DRE patients.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"90 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Medical foundation large language models for comprehensive text analysis and beyond

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-03-05 DOI: 10.1038/s41746-025-01533-1

Qianqian Xie, Qingyu Chen, Aokun Chen, Cheng Peng, Yan Hu, Fongci Lin, Xueqing Peng, Jimin Huang, Jeffrey Zhang, Vipina Keloth, Xinyu Zhou, Lingfei Qian, Huan He, Dennis Shung, Lucila Ohno-Machado, Yonghui Wu, Hua Xu, Jiang Bian

{"title":"Medical foundation large language models for comprehensive text analysis and beyond","authors":"Qianqian Xie, Qingyu Chen, Aokun Chen, Cheng Peng, Yan Hu, Fongci Lin, Xueqing Peng, Jimin Huang, Jeffrey Zhang, Vipina Keloth, Xinyu Zhou, Lingfei Qian, Huan He, Dennis Shung, Lucila Ohno-Machado, Yonghui Wu, Hua Xu, Jiang Bian","doi":"10.1038/s41746-025-01533-1","DOIUrl":"https://doi.org/10.1038/s41746-025-01533-1","url":null,"abstract":"Recent advancements in large language models (LLMs) show significant potential in medical applications but are hindered by limited specialized medical knowledge. We present Me-LLaMA, a family of open-source medical LLMs integrating extensive domain-specific knowledge with robust instruction-following capabilities. Me-LLaMA is developed through continual pretraining and instruction tuning of LLaMA2 models using diverse biomedical and clinical data sources (e.g., biomedical literature and clinical notes). We evaluated Me-LLaMA on six text analysis tasks using 12 benchmarks (e.g., PubMedQA and MIMIC-CXR) and assessed its clinical utility in complex case diagnosis through automatic and human evaluations. Me-LLaMA outperforms existing open medical LLMs in zero-shot and supervised settings and surpasses ChatGPT and GPT-4 after task-specific instruction tuning for most text analysis tasks. Its performance is also comparable to ChatGPT and GPT-4 for diagnosing complex clinical cases. Our findings highlight the importance of combining domain-specific continual pretraining with instruction tuning to enhance performance in medical LLMs.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"131 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transforming healthcare through just, equitable and quality driven artificial intelligence solutions in South Asia

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-03-05 DOI: 10.1038/s41746-025-01534-0

Sushmita Adhikari, Iftikhar Ahmed, Deepak Bajracharya, Bishesh Khanal, Chandrasegarar Solomon, Kapila Jayaratne, Khondaker Abdullah Al Mamum, Muhammad Shamim Hayder Talukder, Sunila Shakya, Suresh Manandhar, Zahid Ali Memon, Moinul Haque Chowdhury, Ihtisham ul Islam, Noor Sabah Rakhshani, M. Imran Khan

引用次数: 0

Interpretable multimodal transformer for prediction of molecular subtypes and grades in adult-type diffuse gliomas

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-03-05 DOI: 10.1038/s41746-025-01530-4

Yunsu Byeon, Yae Won Park, Soohyun Lee, Doohyun Park, HyungSeob Shin, Kyunghwa Han, Jong Hee Chang, Se Hoon Kim, Seung-Koo Lee, Sung Soo Ahn, Dosik Hwang

{"title":"Interpretable multimodal transformer for prediction of molecular subtypes and grades in adult-type diffuse gliomas","authors":"Yunsu Byeon, Yae Won Park, Soohyun Lee, Doohyun Park, HyungSeob Shin, Kyunghwa Han, Jong Hee Chang, Se Hoon Kim, Seung-Koo Lee, Sung Soo Ahn, Dosik Hwang","doi":"10.1038/s41746-025-01530-4","DOIUrl":"https://doi.org/10.1038/s41746-025-01530-4","url":null,"abstract":"Molecular subtyping and grading of adult-type diffuse gliomas are essential for treatment decisions and patient prognosis. We introduce GlioMT, an interpretable multimodal transformer that integrates imaging and clinical data to predict the molecular subtype and grade of adult-type diffuse gliomas according to the 2021 WHO classification. GlioMT is trained on multiparametric MRI data from an institutional set of 1053 patients with adult-type diffuse gliomas to predict the IDH mutation status, 1p/19q codeletion status, and tumor grade. External validation on the TCGA (200 patients) and UCSF (477 patients) shows that GlioMT outperforms conventional CNNs and visual transformers, achieving AUCs of 0.915 (TCGA) and 0.981 (UCSF) for IDH mutation, 0.854 (TCGA) and 0.806 (UCSF) for 1p/19q codeletion, and 0.862 (TCGA) and 0.960 (UCSF) for grade prediction. GlioMT enhances the reliability of clinical decision-making by offering interpretability through attention maps and contributions of imaging and clinical data.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"32 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating base and retrieval augmented LLMs with document or online support for evidence based neurology

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-03-04 DOI: 10.1038/s41746-025-01536-y

Lars Masanneck, Sven G. Meuth, Marc Pawlitzki

引用次数: 0