NPJ Digital Medicine最新文献_第6页

Predictive modeling for step II therapy response in periodontitis - model development and validation. 牙周炎第二阶段治疗反应的预测模型——模型的开发和验证。

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-15 DOI: 10.1038/s41746-025-01828-3

Elias Walter,Tobias Brock,Pierre Lahoud,Nils Werner,Felix Czaja,Antonin Tichy,Caspar Bumm,Andreas Bender,Ana Castro,Wim Teughels,Falk Schwendicke,Matthias Folwaczny

{"title":"Predictive modeling for step II therapy response in periodontitis - model development and validation.","authors":"Elias Walter,Tobias Brock,Pierre Lahoud,Nils Werner,Felix Czaja,Antonin Tichy,Caspar Bumm,Andreas Bender,Ana Castro,Wim Teughels,Falk Schwendicke,Matthias Folwaczny","doi":"10.1038/s41746-025-01828-3","DOIUrl":"https://doi.org/10.1038/s41746-025-01828-3","url":null,"abstract":"Steps I and II periodontal therapy is the first-line treatment for periodontal disease, but has varying success. This study aimed to develop machine learning models to predict changes in periodontal probing depth (PPD) after step II therapy using patient-, tooth-, and site-specific clinical covariates. Models accurately predicted that healthy sites stay healthy, but performed suboptimally for diseased sites. Tuning improved performance, with PPD, tooth-site, and tooth-type identified as key predictors. Pocket closure was predicted with fair accuracy, with baseline PPD as the most relevant covariate. Models predicted improving pockets well but underperformed for non-responding sites, with antibiotic treatment and tooth type being the most influential features. While predictive performance for step II periodontal therapy based on routine clinical data remains limited, models can stratify periodontal sites into meaningful categories and estimate the probability of pocket improvement. They provide a foundation for site-specific outcome prediction and may support patient communication and expectations.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"8 1","pages":"445"},"PeriodicalIF":15.2,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144640014","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

Deep adaptive learning predicts and diagnoses CSVD-related cognitive decline using radiomics from T2-FLAIR: a multi-centre study. 深度自适应学习使用T2-FLAIR放射组学预测和诊断csvd相关的认知衰退：一项多中心研究。

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-15 DOI: 10.1038/s41746-025-01813-w

Lili Huang,Zhuoyuan Li,Xiaolei Zhu,Hui Zhao,Chenglu Mao,Zhihong Ke,Yuting Mo,Dan Yang,Yue Cheng,Ruomeng Qin,Zheqi Hu,Pengfei Shao,Ying Chen,Min Lou,Kelei He,Yun Xu

{"title":"Deep adaptive learning predicts and diagnoses CSVD-related cognitive decline using radiomics from T2-FLAIR: a multi-centre study.","authors":"Lili Huang,Zhuoyuan Li,Xiaolei Zhu,Hui Zhao,Chenglu Mao,Zhihong Ke,Yuting Mo,Dan Yang,Yue Cheng,Ruomeng Qin,Zheqi Hu,Pengfei Shao,Ying Chen,Min Lou,Kelei He,Yun Xu","doi":"10.1038/s41746-025-01813-w","DOIUrl":"https://doi.org/10.1038/s41746-025-01813-w","url":null,"abstract":"Early identification of cerebral small vessel disease related cognitive impairment (CSVD-CI) is crucial for timely clinical intervention. We developed a Transformer-based deep learning model using white matter hyperintensity (WMH) radiomics features from T2-fluid-attenuated inversion recovery images to detect CSVD-CI. A total of 783 subjects (161 longitudinally followed) were enrolled from three centres for model development and external validation, using a domain adaptation strategy. The model achieved AUCs of 0.841 (training) and 0.859/0.749 (validation cohorts), outperforming conventional machine learning models. The gradient-weighted class activation mapping approach highlighted WMH textural features, particularly the logarithm-transformed gray level size zone matrix features, as key contributors. These features were significantly correlated with CSVD macro- and microstructural changes, mediated age-cognition relationships and predicted longitudinal cognitive decline. Our findings indicate that WMH radiomics features, reflecting CI-related biological changes in CSVD, combined with a Transformer-based deep learning model, constitute a feasible, automated, and non-invasive tool for CSVD-CI detection.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"2 1","pages":"444"},"PeriodicalIF":15.2,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144640016","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

Transdiagnostic-focused apps for depression and anxiety: a meta-analysis. 针对抑郁和焦虑的跨诊断应用程序：一项荟萃分析。

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-15 DOI: 10.1038/s41746-025-01860-3

Jake Linardon,Cleo Anderson,Mariel Messer,Claudia Liu,John Torous

{"title":"Transdiagnostic-focused apps for depression and anxiety: a meta-analysis.","authors":"Jake Linardon,Cleo Anderson,Mariel Messer,Claudia Liu,John Torous","doi":"10.1038/s41746-025-01860-3","DOIUrl":"https://doi.org/10.1038/s41746-025-01860-3","url":null,"abstract":"Mental health apps that adopt a transdiagnostic approach to addressing depression and anxiety are emerging, yet a synthesis of their evidence-base is missing. This meta-analysis evaluated the efficacy of transdiagnostic-focused apps for depression and anxiety, and aimed to understand how they compare to diagnostic-specific apps. Nineteen randomized controlled trials (N = 5165) were included. Transdiagnostic-focused apps produced small post-intervention effects relative to controls on pooled outcomes related depression, anxiety and distress (N = 23 comparisons; g = 0.29; 95% CI = 0.17-0.40). Effects remained significant across various sensitivity analyses. CBT apps and apps that were compared with a waitlist produced larger effects. Significant effects were found at follow-up (g = 0.25; 95% CI = 0.10, 0.41). Effects were comparable to disorder-specific app estimates. Findings highlight the potential of transdiagnostic apps to provide accessible support for managing depression and anxiety. Their broad applicability highlights their public health relevance, especially when combined with in-person transdiagnostic therapies to create new hybrid care models.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"52 1","pages":"443"},"PeriodicalIF":15.2,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144640015","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 standalone digital behavior change interventions on physical activity 独立的数字行为改变干预对身体活动的系统回顾和meta分析

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-14 DOI: 10.1038/s41746-025-01827-4

Si-An Lee, Jin-Hyuck Park

引用次数: 0

Will AI become our Co-PI? AI会成为我们的Co-PI吗？

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-14 DOI: 10.1038/s41746-025-01859-w

Dillan Prasad, Aditya Khandeshi, Spencer Sartin, Rishi Jain, Nader Dahdaleh, Maciej Lesniak, Yuan Luo, Christopher Ahuja

引用次数: 0

Phenotypic screening and genetic insights for predicting major vascular-related diseases using retinal imaging 使用视网膜成像预测主要血管相关疾病的表型筛选和遗传见解

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-14 DOI: 10.1038/s41746-025-01850-5

Menglin Lu, Yiheng Mao, Hui Zhu, Yesheng Xu, Yu-Feng Yao, Fei Wu, Zhengxing Huang

{"title":"Phenotypic screening and genetic insights for predicting major vascular-related diseases using retinal imaging","authors":"Menglin Lu, Yiheng Mao, Hui Zhu, Yesheng Xu, Yu-Feng Yao, Fei Wu, Zhengxing Huang","doi":"10.1038/s41746-025-01850-5","DOIUrl":"https://doi.org/10.1038/s41746-025-01850-5","url":null,"abstract":"Retinal photography is a valuable non-invasive tool for assessing vascular health, but genetic evidence linking retinal microcirculation to major vascular-related diseases (e.g., myocardial infarction [MI], stroke, and chronic kidney disease [CKD]) remains scarce. This study investigates their relationships from both phenotypic and genetic perspectives. Phenotypically, we developed a retinal imaging-based screening model to evaluate 10-year risk of these conditions, incorporating quantitative analyses to pinpoint specific vascular abnormalities. Genetically, we analyzed retinal image-derived traits to explore their genetic and causal relationships with vascular-related diseases. Internal validation with 25,840 UK Biobank participants and external temporal validation with 4558 participants confirmed the model’s superiority over traditional risk models. Mendelian randomization suggested causal relationships between retinal traits and stroke and MI, as well as the impact of CKD on retinal microcirculation. These findings reinforce the connection between retinal microcirculation and major vascular-related events, highlighting the potential of retinal imaging for early detection in clinical settings.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"38 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144622431","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

Physical activity and diet just-in-time adaptive intervention to reduce blood pressure: a randomized controlled trial 体育活动和饮食及时的适应性干预降低血压：一项随机对照试验

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-14 DOI: 10.1038/s41746-025-01844-3

Michael P. Dorsch, Jessica R. Golbus, Rachel Stevens, Brad Trumpower, Tanima Basu, Evan Luff, Kimberly Warden, Michael Giacalone, Sarah Bailey, Gabriella V. Rubick, Sonali Mishra, Predrag Klasnja, Mark W. Newman, Lesli E. Skolarus, Brahmajee K. Nallamothu

{"title":"Physical activity and diet just-in-time adaptive intervention to reduce blood pressure: a randomized controlled trial","authors":"Michael P. Dorsch, Jessica R. Golbus, Rachel Stevens, Brad Trumpower, Tanima Basu, Evan Luff, Kimberly Warden, Michael Giacalone, Sarah Bailey, Gabriella V. Rubick, Sonali Mishra, Predrag Klasnja, Mark W. Newman, Lesli E. Skolarus, Brahmajee K. Nallamothu","doi":"10.1038/s41746-025-01844-3","DOIUrl":"https://doi.org/10.1038/s41746-025-01844-3","url":null,"abstract":"Mobile health interventions for behavioral change require large-scale studies to ensure their clinical benefits. We conducted a randomized controlled trial of patients with hypertension to assess the myBPmyLife application in promoting physical activity and lower-sodium foods to lower systolic blood pressure (SBP). 602 participants were randomly assigned to either a control group or an intervention group that received the myBPmyLife application. For the primary outcome, change in SBP over 6 months was −5.2 mmHg in the intervention and −5.7 mmHg in the control group (p = 0.76). For secondary outcomes, the intervention group increased their daily step count by 170 steps, while the control group decreased by 319 steps (p = 0.040). Sodium intake decreased by 1145 mg in the intervention and 860 mg in the control group (p = 0.002). The myBPmyLife application did not reduce SBP over 6 months in hypertension patients despite increasing step counts and lowering sodium intake. ClinicalTrials.gov registration: NCT05154929, 12/2021","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"23 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144622428","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

X-ray2CTPA: leveraging diffusion models to enhance pulmonary embolism classification x射线2ctpa：利用扩散模型增强肺栓塞分级

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-14 DOI: 10.1038/s41746-025-01857-y

Noa Cahan, Eyal Klang, Galit Aviram, Yiftach Barash, Eli Konen, Raja Giryes, Hayit Greenspan

{"title":"X-ray2CTPA: leveraging diffusion models to enhance pulmonary embolism classification","authors":"Noa Cahan, Eyal Klang, Galit Aviram, Yiftach Barash, Eli Konen, Raja Giryes, Hayit Greenspan","doi":"10.1038/s41746-025-01857-y","DOIUrl":"https://doi.org/10.1038/s41746-025-01857-y","url":null,"abstract":"Chest X-rays or chest radiography (CXR), commonly used for medical diagnostics, typically enables limited imaging compared to computed tomography (CT) scans, which offer more detailed and accurate three-dimensional data, particularly contrast-enhanced scans like CT Pulmonary Angiography (CTPA). However, CT scans entail higher costs, greater radiation exposure, and are less accessible than CXRs. In this work, we explore cross-modal translation from a 2D low contrast-resolution X-ray input to a 3D high contrast and spatial-resolution CTPA scan. Driven by recent advances in generative AI, we introduce a novel diffusion-based approach to this task. We employ the synthesized 3D images in a classification framework and show improved AUC in a Pulmonary Embolism (PE) categorization task, using the initial CXR input. Furthermore, we evaluate the model’s performance using quantitative metrics, ensuring diagnostic relevance of the generated images. The proposed method is generalizable and capable of performing additional cross-modality translations in medical imaging. It may pave the way for more accessible and cost-effective advanced diagnostic tools. The code for this project is available: https://github.com/NoaCahan/X-ray2CTPA.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"20 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144622455","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

Diagnosing pathologic myopia by identifying morphologic patterns using ultra widefield images with deep learning 利用深度学习的超广角图像识别形态模式诊断病理性近视

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-13 DOI: 10.1038/s41746-025-01849-y

Yang Liu, Keming Zhao, Lihui Luo, Ziheng Zhang, Zhenghang Qian, Cenk Jiang, Zhicheng Du, Simin Deng, Chengming Yang, Duanpo Wu, Shuai Wang, Xingru Huang, Chenggang Yan, Yingting Zhu, Yehong Zhuo, Chunsheng Qu, Jiaqi Chen, Zhenqiang Huang, Chenying Lu, Minjiang Chen, Dongmei Yu, Jiantao Wang, Peiwu Qin, Jiansong Ji

{"title":"Diagnosing pathologic myopia by identifying morphologic patterns using ultra widefield images with deep learning","authors":"Yang Liu, Keming Zhao, Lihui Luo, Ziheng Zhang, Zhenghang Qian, Cenk Jiang, Zhicheng Du, Simin Deng, Chengming Yang, Duanpo Wu, Shuai Wang, Xingru Huang, Chenggang Yan, Yingting Zhu, Yehong Zhuo, Chunsheng Qu, Jiaqi Chen, Zhenqiang Huang, Chenying Lu, Minjiang Chen, Dongmei Yu, Jiantao Wang, Peiwu Qin, Jiansong Ji","doi":"10.1038/s41746-025-01849-y","DOIUrl":"https://doi.org/10.1038/s41746-025-01849-y","url":null,"abstract":"Pathologic myopia is a leading cause of visual impairment and blindness. While deep learning-based approaches aid in recognizing pathologic myopia using color fundus photography, they often rely on implicit patterns that lack clinical interpretability. This study aims to diagnose pathologic myopia by identifying clinically significant morphologic patterns, specifically posterior staphyloma and myopic maculopathy, by leveraging ultra-widefield (UWF) images that provide a broad retinal field of view. We curate a large-scale, multi-source UWF myopia dataset called PSMM and introduce RealMNet, an end-to-end lightweight framework designed to identify these challenging patterns. Benefiting from the fast pretraining distillation backbone, RealMNet comprises only 21 million parameters, which facilitates deployment for medical devices. Extensive experiments conducted across three different protocols demonstrate the robustness and generalizability of RealMNet. RealMNet achieves an F1 Score of 0.7970 (95% CI 0.7612–0.8328), mAP of 0.8497 (95% CI 0.8058–0.8937), and AUROC of 0.9745 (95% CI 0.9690–0.9801), showcasing promise in clinical applications.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"13 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612899","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

Vision-language model for report generation and outcome prediction in CT pulmonary angiogram 用于CT肺血管造影报告生成和结果预测的视觉语言模型

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-07-12 DOI: 10.1038/s41746-025-01807-8

Zhusi Zhong, Yuli Wang, Jing Wu, Wen-Chi Hsu, Vin Somasundaram, Lulu Bi, Shreyas Kulkarni, Zhuoqi Ma, Scott Collins, Grayson Baird, Sun Ho Ahn, Xue Feng, Ihab Kamel, Cheng Ting Lin, Colin Greineder, Michael Atalay, Zhicheng Jiao, Harrison Bai

{"title":"Vision-language model for report generation and outcome prediction in CT pulmonary angiogram","authors":"Zhusi Zhong, Yuli Wang, Jing Wu, Wen-Chi Hsu, Vin Somasundaram, Lulu Bi, Shreyas Kulkarni, Zhuoqi Ma, Scott Collins, Grayson Baird, Sun Ho Ahn, Xue Feng, Ihab Kamel, Cheng Ting Lin, Colin Greineder, Michael Atalay, Zhicheng Jiao, Harrison Bai","doi":"10.1038/s41746-025-01807-8","DOIUrl":"https://doi.org/10.1038/s41746-025-01807-8","url":null,"abstract":"Accurate and comprehensive interpretation of pulmonary embolism (PE) from Computed Tomography Pulmonary Angiography (CTPA) scans remains a clinical challenge due to the limited specificity and structure of existing AI tools. We propose an agent-based framework that integrates Vision-Language Models (VLMs) for detecting 32 PE-related abnormalities and Large Language Models (LLMs) for structured report generation. Trained on over 69,000 CTPA studies from 24,890 patients across Brown University Health (BUH), Johns Hopkins University (JHU), and the INSPECT dataset from Stanford, the model demonstrates strong performance in abnormality classification and report generation. For abnormality classification, it achieved AUROC scores of 0.788 (BUH), 0.754 (INSPECT), and 0.710 (JHU), with corresponding BERT-F1 scores of 0.891, 0.829, and 0.842. The abnormality-guided reporting strategy consistently outperformed the organ-based and holistic captioning baselines. For survival prediction, a multimodal fusion model that incorporates imaging, clinical variables, diagnostic outputs, and generated reports achieved concordance indices of 0.863 (BUH) and 0.731 (JHU), outperforming traditional PESI scores. This framework provides a clinically meaningful and interpretable solution for end-to-end PE diagnosis, structured reporting, and outcome prediction.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"14 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612838","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