NPJ Digital Medicine最新文献_第8页

Clinically applicable optimized periprosthetic joint infection diagnosis via AI based pathology 通过基于人工智能的病理诊断优化假体周围关节感染的临床应用

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-26 DOI: 10.1038/s41746-024-01301-7

Ye Tao, Yazhi Luo, Hanwen Hu, Wei Wang, Ying Zhao, Shuhao Wang, Qingyuan Zheng, Tianwei Zhang, Guoqiang Zhang, Jie Li, Ming Ni

{"title":"Clinically applicable optimized periprosthetic joint infection diagnosis via AI based pathology","authors":"Ye Tao, Yazhi Luo, Hanwen Hu, Wei Wang, Ying Zhao, Shuhao Wang, Qingyuan Zheng, Tianwei Zhang, Guoqiang Zhang, Jie Li, Ming Ni","doi":"10.1038/s41746-024-01301-7","DOIUrl":"10.1038/s41746-024-01301-7","url":null,"abstract":"Periprosthetic joint infection (PJI) is a severe complication after joint replacement surgery that demands precise diagnosis for effective treatment. We enhanced PJI diagnostic accuracy through three steps: (1) developing a self-supervised PJI model with DINO v2 to create a large dataset; (2) comparing multiple intelligent models to identify the best one; and (3) using the optimal model for visual analysis to refine diagnostic practices. The self-supervised model generated 27,724 training samples and achieved a perfect AUC of 1, indicating flawless case differentiation. EfficientNet v2-S outperformed CAMEL2 at the image level, while CAMEL2 was superior at the patient level. By using the weakly supervised PJI model to adjust diagnostic criteria, we reduced the required high-power field diagnoses per slide from five to three. These findings demonstrate AI’s potential to improve the accuracy and standardization of PJI pathology and have significant implications for infectious disease diagnostics.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-12"},"PeriodicalIF":12.4,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01301-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142490816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Public evidence on AI products for digital pathology 关于数字病理学人工智能产品的公开证据

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-25 DOI: 10.1038/s41746-024-01294-3

Gillian A. Matthews, Clare McGenity, Daljeet Bansal, Darren Treanor

引用次数: 0

Transmission line model as a digital twin for abdominal aortic aneurysm patients 作为腹主动脉瘤患者数字双胞胎的传输线模型

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-25 DOI: 10.1038/s41746-024-01303-5

Donghyeon Kim, Divyesh Narayanan, Shih-Hsien Sung, Hao-Min Cheng, Chen-Huan Chen, Chang-Sei Kim, Ramakrishna Mukkamala, Jin-Oh Hahn

引用次数: 0

Prediction of total and regional body composition from 3D body shape 根据三维体形预测身体总成分和区域成分

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-23 DOI: 10.1038/s41746-024-01289-0

Chexuan Qiao, Emanuella De Lucia Rolfe, Ethan Mak, Akash Sengupta, Richard Powell, Laura P. E. Watson, Steven B. Heymsfield, John A. Shepherd, Nicholas Wareham, Soren Brage, Roberto Cipolla

{"title":"Prediction of total and regional body composition from 3D body shape","authors":"Chexuan Qiao, Emanuella De Lucia Rolfe, Ethan Mak, Akash Sengupta, Richard Powell, Laura P. E. Watson, Steven B. Heymsfield, John A. Shepherd, Nicholas Wareham, Soren Brage, Roberto Cipolla","doi":"10.1038/s41746-024-01289-0","DOIUrl":"10.1038/s41746-024-01289-0","url":null,"abstract":"Accurate assessment of body composition is essential for evaluating the risk of chronic disease. 3D body shape, obtainable using smartphones, correlates strongly with body composition. We present a novel method that fits a 3D body mesh to a dual-energy X-ray absorptiometry (DXA) silhouette (emulating a single photograph) paired with anthropometric traits, and apply it to the multi-phase Fenland study comprising 12,435 adults. Using baseline data, we derive models predicting total and regional body composition metrics from these meshes. In Fenland follow-up data, all metrics were predicted with high correlations (r > 0.86). We also evaluate a smartphone app which reconstructs a 3D mesh from phone images to predict body composition metrics; this analysis also showed strong correlations (r > 0.84) for all metrics. The 3D body shape approach is a valid alternative to medical imaging that could offer accessible health parameters for monitoring the efficacy of lifestyle intervention programmes.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-12"},"PeriodicalIF":12.4,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01289-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142488789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Clinical usefulness of digital twin guided virtual amiodarone test in patients with atrial fibrillation ablation 数字孪生引导的虚拟胺碘酮测试在心房颤动消融患者中的临床实用性

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-23 DOI: 10.1038/s41746-024-01298-z

Taehyun Hwang, Byounghyun Lim, Oh-Seok Kwon, Moon-Hyun Kim, Daehoon Kim, Je-Wook Park, Hee Tae Yu, Tae-Hoon Kim, Jae-Sun Uhm, Boyoung Joung, Moon-Hyoung Lee, Chun Hwang, Hui-Nam Pak

{"title":"Clinical usefulness of digital twin guided virtual amiodarone test in patients with atrial fibrillation ablation","authors":"Taehyun Hwang, Byounghyun Lim, Oh-Seok Kwon, Moon-Hyun Kim, Daehoon Kim, Je-Wook Park, Hee Tae Yu, Tae-Hoon Kim, Jae-Sun Uhm, Boyoung Joung, Moon-Hyoung Lee, Chun Hwang, Hui-Nam Pak","doi":"10.1038/s41746-024-01298-z","DOIUrl":"10.1038/s41746-024-01298-z","url":null,"abstract":"It would be clinically valuable if the efficacy of antiarrhythmic drugs could be simulated in advance. We developed a digital twin to predict amiodarone efficacy in high-risk atrial fibrillation (AF) patients post-ablation. Virtual left atrium models were created from computed tomography and electroanatomical maps to simulate AF and evaluate its response to varying amiodarone concentrations. As the amiodarone concentration increased in the virtual setting, action potential duration lengthened, peak upstroke velocities decreased, and virtual AF termination became more frequent. Patients were classified into effective (those with virtually terminated AF at therapeutic doses) and ineffective groups. The one-year clinical outcomes after AF ablation showed significantly better results in the effective group compared to the ineffective group, with AF recurrence rates of 20.8% vs. 45.1% (log-rank p = 0.031, adjusted hazard ratio, 0.37 [0.14-0.98]; p = 0.046). This study highlights the potential of a digital twin-guided approach in predicting amiodarone’s effectiveness and improving personalized AF management. Clinical Trial Registration Name: The Evaluation for Prognostic Factors After Catheter Ablation of Atrial Fibrillation: Cohort Study, Registration number: NCT02138695. The date of registration: 2014-05. URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02138695.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-11"},"PeriodicalIF":12.4,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01298-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142487417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Process mining in mHealth data analysis 移动医疗数据分析中的流程挖掘

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-23 DOI: 10.1038/s41746-024-01297-0

Michael Winter, Berthold Langguth, Winfried Schlee, Rüdiger Pryss

引用次数: 0

Medical large language models are susceptible to targeted misinformation attacks 医学大型语言模型容易受到有针对性的错误信息攻击

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-23 DOI: 10.1038/s41746-024-01282-7

Tianyu Han, Sven Nebelung, Firas Khader, Tianci Wang, Gustav Müller-Franzes, Christiane Kuhl, Sebastian Försch, Jens Kleesiek, Christoph Haarburger, Keno K. Bressem, Jakob Nikolas Kather, Daniel Truhn

{"title":"Medical large language models are susceptible to targeted misinformation attacks","authors":"Tianyu Han, Sven Nebelung, Firas Khader, Tianci Wang, Gustav Müller-Franzes, Christiane Kuhl, Sebastian Försch, Jens Kleesiek, Christoph Haarburger, Keno K. Bressem, Jakob Nikolas Kather, Daniel Truhn","doi":"10.1038/s41746-024-01282-7","DOIUrl":"10.1038/s41746-024-01282-7","url":null,"abstract":"Large language models (LLMs) have broad medical knowledge and can reason about medical information across many domains, holding promising potential for diverse medical applications in the near future. In this study, we demonstrate a concerning vulnerability of LLMs in medicine. Through targeted manipulation of just 1.1% of the weights of the LLM, we can deliberately inject incorrect biomedical facts. The erroneous information is then propagated in the model’s output while maintaining performance on other biomedical tasks. We validate our findings in a set of 1025 incorrect biomedical facts. This peculiar susceptibility raises serious security and trustworthiness concerns for the application of LLMs in healthcare settings. It accentuates the need for robust protective measures, thorough verification mechanisms, and stringent management of access to these models, ensuring their reliable and safe use in medical practice.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-9"},"PeriodicalIF":12.4,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01282-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142487418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development and assessment of a machine learning tool for predicting emergency admission in Scotland 开发和评估用于预测苏格兰急诊入院情况的机器学习工具

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-23 DOI: 10.1038/s41746-024-01250-1

James Liley, Gergo Bohner, Samuel R. Emerson, Bilal A. Mateen, Katie Borland, David Carr, Scott Heald, Samuel D. Oduro, Jill Ireland, Keith Moffat, Rachel Porteous, Stephen Riddell, Simon Rogers, Ioanna Thoma, Nathan Cunningham, Chris Holmes, Katrina Payne, Sebastian J. Vollmer, Catalina A. Vallejos, Louis J. M. Aslett

{"title":"Development and assessment of a machine learning tool for predicting emergency admission in Scotland","authors":"James Liley, Gergo Bohner, Samuel R. Emerson, Bilal A. Mateen, Katie Borland, David Carr, Scott Heald, Samuel D. Oduro, Jill Ireland, Keith Moffat, Rachel Porteous, Stephen Riddell, Simon Rogers, Ioanna Thoma, Nathan Cunningham, Chris Holmes, Katrina Payne, Sebastian J. Vollmer, Catalina A. Vallejos, Louis J. M. Aslett","doi":"10.1038/s41746-024-01250-1","DOIUrl":"10.1038/s41746-024-01250-1","url":null,"abstract":"Emergency admissions (EA), where a patient requires urgent in-hospital care, are a major challenge for healthcare systems. The development of risk prediction models can partly alleviate this problem by supporting primary care interventions and public health planning. Here, we introduce SPARRAv4, a predictive score for EA risk that will be deployed nationwide in Scotland. SPARRAv4 was derived using supervised and unsupervised machine-learning methods applied to routinely collected electronic health records from approximately 4.8M Scottish residents (2013-18). We demonstrate improvements in discrimination and calibration with respect to previous scores deployed in Scotland, as well as stability over a 3-year timeframe. Our analysis also provides insights about the epidemiology of EA risk in Scotland, by studying predictive performance across different population sub-groups and reasons for admission, as well as by quantifying the effect of individual input features. Finally, we discuss broader challenges including reproducibility and how to safely update risk prediction models that are already deployed at population level.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-13"},"PeriodicalIF":12.4,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01250-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142487419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detecting clinical medication errors with AI enabled wearable cameras 利用人工智能可穿戴相机检测临床用药错误

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-22 DOI: 10.1038/s41746-024-01295-2

Justin Chan, Solomon Nsumba, Mitchell Wortsman, Achal Dave, Ludwig Schmidt, Shyamnath Gollakota, Kelly Michaelsen

引用次数: 0

Evaluation and mitigation of cognitive biases in medical language models 评估和减轻医学语言模型中的认知偏差。

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-21 DOI: 10.1038/s41746-024-01283-6

Samuel Schmidgall, Carl Harris, Ime Essien, Daniel Olshvang, Tawsifur Rahman, Ji Woong Kim, Rojin Ziaei, Jason Eshraghian, Peter Abadir, Rama Chellappa

{"title":"Evaluation and mitigation of cognitive biases in medical language models","authors":"Samuel Schmidgall, Carl Harris, Ime Essien, Daniel Olshvang, Tawsifur Rahman, Ji Woong Kim, Rojin Ziaei, Jason Eshraghian, Peter Abadir, Rama Chellappa","doi":"10.1038/s41746-024-01283-6","DOIUrl":"10.1038/s41746-024-01283-6","url":null,"abstract":"Increasing interest in applying large language models (LLMs) to medicine is due in part to their impressive performance on medical exam questions. However, these exams do not capture the complexity of real patient–doctor interactions because of factors like patient compliance, experience, and cognitive bias. We hypothesized that LLMs would produce less accurate responses when faced with clinically biased questions as compared to unbiased ones. To test this, we developed the BiasMedQA dataset, which consists of 1273 USMLE questions modified to replicate common clinically relevant cognitive biases. We assessed six LLMs on BiasMedQA and found that GPT-4 stood out for its resilience to bias, in contrast to Llama 2 70B-chat and PMC Llama 13B, which showed large drops in performance. Additionally, we introduced three bias mitigation strategies, which improved but did not fully restore accuracy. Our findings highlight the need to improve LLMs’ robustness to cognitive biases, in order to achieve more reliable applications of LLMs in healthcare.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-9"},"PeriodicalIF":12.4,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11494053/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142471109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0