NPJ Digital Medicine最新文献

Deep learning CT model for stratified diagnosis of pancreatic cystic neoplasms: multicenter development, validation, and real-world clinical impact. 胰腺囊性肿瘤分层诊断的深度学习CT模型：多中心发展、验证和现实世界的临床影响。

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-10-13 DOI: 10.1038/s41746-025-01970-y

Xiaohan Yuan,Chengwei Chen,Zhang Shi,Wenbin Liu,Xinyue Zhang,Ming Yang,Mengmeng Zhu,Jieyu Yu,Fang Liu,Jing Li,Yunshuo Zhang,Hui Jiang,Bozhu Chen,Jianping Lu,Chengwei Shao,Yun Bian

{"title":"Deep learning CT model for stratified diagnosis of pancreatic cystic neoplasms: multicenter development, validation, and real-world clinical impact.","authors":"Xiaohan Yuan,Chengwei Chen,Zhang Shi,Wenbin Liu,Xinyue Zhang,Ming Yang,Mengmeng Zhu,Jieyu Yu,Fang Liu,Jing Li,Yunshuo Zhang,Hui Jiang,Bozhu Chen,Jianping Lu,Chengwei Shao,Yun Bian","doi":"10.1038/s41746-025-01970-y","DOIUrl":"https://doi.org/10.1038/s41746-025-01970-y","url":null,"abstract":"Pancreatic cystic neoplasms (PCN) are critical precursors for early pancreatic cancer detection, yet current diagnostic methods lack accuracy and consistency. This multicenter study developed and validated an artificial intelligence (AI)-powered CT model (PCN-AI) for improved assessment. Using contrast-enhanced CT images from 1835 patients, PCN-AI extracted 63 quantitative features to classify PCN subtypes through four hierarchical tasks. A multi-reader, multi-case (MRMC) study demonstrated that AI assistance significantly improved radiologists' diagnostic accuracy (AUC: 0.786 to 0.845; p < 0.05) and reduced interpretation time by 23.7% (5.28 vs. 4.03 minutes/case). Radiologists accepted AI recommendations in 87.14% of cases. In a prospective real-world cohort, PCN-AI outperformed radiologist double-reading, providing actionable diagnostic benefits to 45.45% of patients (5/11) by correctly identifying missed malignant PCN cases, enabling timely intervention, and simultaneously reducing clinical workload by 39.3%. PCN-AI achieved robust performance across tasks (AUCs: 0.845-0.988), demonstrating its potential to enhance early detection, precision management, and diagnostic efficiency in clinical practice.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"117 1","pages":"609"},"PeriodicalIF":15.2,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145283940","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

Automated AI based identification of autism spectrum disorder from home videos. 从家庭视频中自动识别基于AI的自闭症谱系障碍。

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-10-10 DOI: 10.1038/s41746-025-01993-5

Dong Yeong Kim,Ryemi Do,Youmin Shin,Hewoen Sim,Hanna Kim,Sungchul Cho,Geonhee Lee,Seyeon Park,Boa Jang,Hyojeong Lim,Sungji Ha,Jaeeun Yu,Hangnyoung Choi,Junghan Lee,Min-Hyeon Park,Ayeong Cho,Chan-Mo Yang,Dongho Lee,Heejeong Yoo,Yoojeong Lee,Guiyoung Bong,Johanna Inhyang Kim,Haneul Sung,Hyo-Won Kim,Eunji Jung,Seungwon Chung,Jung-Woo Son,Jae Hyun Yoo,Sekye Jeon,Jinseong Jang,You Bin Lim,Jeeyoung Chun,Wooseok Choi,Sooyeon Lee,Sohyun Park,Jisung Ahn,Chae Rim Lee,Keun-Ah Cheon,Young-Gon Kim,Bung-Nyun Kim

{"title":"Automated AI based identification of autism spectrum disorder from home videos.","authors":"Dong Yeong Kim,Ryemi Do,Youmin Shin,Hewoen Sim,Hanna Kim,Sungchul Cho,Geonhee Lee,Seyeon Park,Boa Jang,Hyojeong Lim,Sungji Ha,Jaeeun Yu,Hangnyoung Choi,Junghan Lee,Min-Hyeon Park,Ayeong Cho,Chan-Mo Yang,Dongho Lee,Heejeong Yoo,Yoojeong Lee,Guiyoung Bong,Johanna Inhyang Kim,Haneul Sung,Hyo-Won Kim,Eunji Jung,Seungwon Chung,Jung-Woo Son,Jae Hyun Yoo,Sekye Jeon,Jinseong Jang,You Bin Lim,Jeeyoung Chun,Wooseok Choi,Sooyeon Lee,Sohyun Park,Jisung Ahn,Chae Rim Lee,Keun-Ah Cheon,Young-Gon Kim,Bung-Nyun Kim","doi":"10.1038/s41746-025-01993-5","DOIUrl":"https://doi.org/10.1038/s41746-025-01993-5","url":null,"abstract":"Autism spectrum disorder (ASD) is a prevalent childhood-onset neurodevelopmental condition. Early diagnosis remains challenging by the time, cost, and expertise required for traditional assessments, creating barriers to timely identification. We developed an AI-based screening system leveraging home-recorded videos to improve early ASD detection. Three task-based video protocols under 1 min each-name-response, imitation, and ball-playing-were developed, and home videos following these protocols were collected from 510 children (253 ASD, 257 typically developing), aged 18-48 months, across 9 hospitals in South Korea. Task-specific features were extracted using deep learning models and combined with demographic data through machine learning classifiers. The ensemble model achieved an area under the receiver operating characteristic curve of 0.83 and an accuracy of 0.75. This fully automated approach, based on short home-video protocols that elicit children's natural behaviors, complements clinical evaluation and may aid in prioritizing referrals and enabling earlier intervention in resource-limited settings.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"36 1","pages":"607"},"PeriodicalIF":15.2,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261163","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

Predicting recovery after stressors using step count data derived from activity monitors. 使用来自活动监视器的步数数据预测压力后的恢复情况。

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-10-09 DOI: 10.1038/s41746-025-01998-0

Dario Baretta,Sarah Koch,Joren Buekers,Judith Garcia-Aymerich,Lenka Knapova,Steriani Elavsky,Job Godino,Merlijn Olthof,Anna Lichtwarck-Aschoff,Ruud den Hartigh,Guillaume Chevance

引用次数: 0

Planet-wide performance of a skin disease AI algorithm validated in Korea. 在韩国验证的皮肤病AI算法的全球性能。

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-10-08 DOI: 10.1038/s41746-025-01980-w

Seung Seog Han,Soo Ick Cho,Gröger Fabian,Alexander A Navarini,Myoung Shin Kim,Dong Hun Lee,Ju Hee Lee,Jihee Kim,Chong Hyun Won,Kyung-Nam Bae,Jee-Bum Lee,Hyun-Sun Yoon,Sung Eun Chang,Seong Hwan Kim,Jung Im Na,Cristian Navarrete-Dechent

{"title":"Planet-wide performance of a skin disease AI algorithm validated in Korea.","authors":"Seung Seog Han,Soo Ick Cho,Gröger Fabian,Alexander A Navarini,Myoung Shin Kim,Dong Hun Lee,Ju Hee Lee,Jihee Kim,Chong Hyun Won,Kyung-Nam Bae,Jee-Bum Lee,Hyun-Sun Yoon,Sung Eun Chang,Seong Hwan Kim,Jung Im Na,Cristian Navarrete-Dechent","doi":"10.1038/s41746-025-01980-w","DOIUrl":"https://doi.org/10.1038/s41746-025-01980-w","url":null,"abstract":"To address the diversity of skin conditions and the low prevalence of skin cancers, we curated a large hospital dataset (National Information Society Agency, Seoul, Korea [NIA] dataset; 70 diseases, 152,443 images) and collected real-world webapp data ( https://modelderm.com ; 1,691,032 requests). We propose a conservative evaluation method by assessing sensitivity in hospitals and specificity in real-world use, assuming all malignancy predictions were false positives. Based on three differential diagnoses, skin cancer sensitivity in Korea was 78.2% (NIA) and specificity was 88.0% (webapp). Top-1 and Top-3 accuracies for 70 diseases (NIA) were 43.3% and 66.6%, respectively. Analysis of webapp data provides insights into disease prevalence and public interest across 228 countries. Malignancy predictions were highest in North America (2.6%) and lowest in Africa (0.9%), while benign tumors were most common in Asia (55.5%), and infectious diseases were most prevalent in Africa (17.1%). These findings suggest that AI can aid global dermatologic surveillance.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"34 1","pages":"603"},"PeriodicalIF":15.2,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246762","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: digital biomarkers of fatigue in chronic diseases. 系统综述：慢性疾病中疲劳的数字生物标志物。

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-10-08 DOI: 10.1038/s41746-025-01939-x

Nana Yaw Aboagye,Chloe Hinchliffe,Silvia Del Din,Wan-Fai Ng,Kenneth F Baker,Mark R Baker

{"title":"Systematic review: digital biomarkers of fatigue in chronic diseases.","authors":"Nana Yaw Aboagye,Chloe Hinchliffe,Silvia Del Din,Wan-Fai Ng,Kenneth F Baker,Mark R Baker","doi":"10.1038/s41746-025-01939-x","DOIUrl":"https://doi.org/10.1038/s41746-025-01939-x","url":null,"abstract":"This systematic review explores the relationship between digital biomarkers, measured using wearable devices, and fatigue in patients with chronic diseases. Studies included in this review focused on individuals with diseases or conditions in 13 broad categories: multiple sclerosis (MS); rheumatoid arthritis (RA); chronic obstructive pulmonary disease (COPD); long COVID; cancer; chronic fatigue syndrome (CFS); pulmonary sarcoidosis; Parkinson's disease; chronic stroke; chronic inflammatory rheumatic disease (CIRD); Inflammatory Bowel Diseases (IBD), Primary Sjogren's Syndrome (PSS), and Systemic Lupus Erythematosus (SLE). The review synthesizes findings on the correlation between objective digital biomarkers and self-reported fatigue, highlighting the potential for disease-specific digital biomarkers to inform personalized fatigue management. The results suggest that reduced physical activity, increased sedentary behavior and autonomic dysfunction are associated with fatigue levels across multiple disease conditions included in this review, though the strength of this association and the specific biomarkers involved vary across diseases.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"62 1","pages":"602"},"PeriodicalIF":15.2,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246724","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

Publisher Correction: Graph retrieval augmented large language models for facial phenotype associated rare genetic disease. 出版者更正：图形检索增强了面部表型相关罕见遗传疾病的大型语言模型。

IF 15.1 1区医学

NPJ Digital Medicine Pub Date : 2025-10-08 DOI: 10.1038/s41746-025-02017-y

Jie Song, Zhichuan Xu, Mengqiao He, Jinhua Feng, Bairong Shen

引用次数: 0

The evaluation illusion of large language models in medicine. 医学中大型语言模型的评价错觉。

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-10-07 DOI: 10.1038/s41746-025-01963-x

Monica Agrawal,Irene Y Chen,Freya Gulamali,Shalmali Joshi

引用次数: 0

Using a fine-tuned large language model for symptom-based depression evaluation. 使用微调的大型语言模型进行基于症状的抑郁症评估。

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-10-07 DOI: 10.1038/s41746-025-01982-8

Samantha Weber,Nicolas Deperrois,Robert Heun,Laura Frühschütz,Anna Monn,Stephanie Homan,Andrea Häfliger,Erich Seifritz,Tobias Kowatsch, ,Birgit Kleim,Sebastian Olbrich

引用次数: 0

Representation is power: traditional, hybrid, and digital recruitment results from a non-randomized clinical trial engaging adolescents. 代表性就是力量：一项涉及青少年的非随机临床试验的传统、混合和数字招募结果。

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-10-07 DOI: 10.1038/s41746-025-01947-x

Taylor B Harrison,Jessica A Sinclair,Lisa J Martin,Kristin Childers-Buschle,Holly Elder,Sunyang Fu,Hongfang Liu,William B Brinkman,Melanie F Myers,Michelle L McGowan

{"title":"Representation is power: traditional, hybrid, and digital recruitment results from a non-randomized clinical trial engaging adolescents.","authors":"Taylor B Harrison,Jessica A Sinclair,Lisa J Martin,Kristin Childers-Buschle,Holly Elder,Sunyang Fu,Hongfang Liu,William B Brinkman,Melanie F Myers,Michelle L McGowan","doi":"10.1038/s41746-025-01947-x","DOIUrl":"https://doi.org/10.1038/s41746-025-01947-x","url":null,"abstract":"Clinical trials support the iterative advancement of modern medicine. However, challenges in achieving population-representativeness or participant sampling commensurate with the burden of disease can limit the generalizability and reproducibility of trial results. Here, we present the recruitment strategies and cohort profile of the Engaging Adolescents in Decisions about Return of Genomic Research Results non-randomized clinical trial (NCT0448106), where traditional, targeted hybrid, and digital recruitment methods were implemented with quota sampling to enroll diverse adolescents (ages 13-17) and young adults (ages 18-21). The largest proportion of participants enrolled through digital strategies (39.1%), followed by traditional (34.2%), and targeted hybrid strategies (23.2%). Despite lower enrollment, targeted hybrid recruitment, involving letters and text messages, had the largest proportion of participants from groups historically underrepresented in research (87.5%), compared to traditional (48.5%) and digital (32.3%) methods (p < 0.001). Our findings demonstrate a model for achieving both recruitment targets and inclusive trial participation to counteract overrepresentation of participants of European descent in clinical research.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"33 1","pages":"601"},"PeriodicalIF":15.2,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240893","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

Metagenomic fingerprints in bronchoalveolar lavage differentiate pulmonary diseases. 支气管肺泡灌洗的宏基因组指纹图谱鉴别肺部疾病。

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-10-07 DOI: 10.1038/s41746-025-01977-5

Dongsheng Han,Chang Liu,Bin Yang,Fei Yu,Huifang Liu,Bin Lou,Yifei Shen,Hui Tang,Hua Zhou,Shufa Zheng,Yu Chen

{"title":"Metagenomic fingerprints in bronchoalveolar lavage differentiate pulmonary diseases.","authors":"Dongsheng Han,Chang Liu,Bin Yang,Fei Yu,Huifang Liu,Bin Lou,Yifei Shen,Hui Tang,Hua Zhou,Shufa Zheng,Yu Chen","doi":"10.1038/s41746-025-01977-5","DOIUrl":"https://doi.org/10.1038/s41746-025-01977-5","url":null,"abstract":"Recent advances in unbiased metagenomic next-generation sequencing (mNGS) enable simultaneous examination of microbial and host genetic material. We developed a multimodal machine learning-based diagnostic approach to differentiate lung cancer and pulmonary infections by analyzing 402 bronchoalveolar lavage fluid (BALF) mNGS datasets, including lung cancer (n = 123), bacterial infections (n = 114), fungal infections (n = 79), and pulmonary tuberculosis (n = 86). The training cohort revealed differences in microbial profiles, bacteriophage abundance, host gene and transposable element expression, immune cell composition, and tumor fraction derived from copy number variation (CNV). The integrated model (Model VI) achieved an AUC of 0.937 (95% CI, 0.910-0.964) in the training cohort and 0.847 (95% CI, 0.776-0.918) in the test cohort. A rule-in/rule-out strategy further improved accuracy in differentiating lung cancer from tuberculosis (accuracy = 0.896), fungal (accuracy = 0.915), and bacterial (accuracy = 0.907) infections. These findings highlight the potential of mNGS-based multimodal analysis as a cost-effective tool for early and accurate differential diagnosis.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"21 1","pages":"599"},"PeriodicalIF":15.2,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240900","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