NPJ Digital Medicine最新文献

{"title":"Personalized home based neurostimulation via AI optimization augments sustained attention","authors":"Roi Cohen Kadosh, Delia Ciobotaru, Malin I. Karstens, Vu Nguyen","doi":"10.1038/s41746-025-01744-6","DOIUrl":"https://doi.org/10.1038/s41746-025-01744-6","url":null,"abstract":"<p>Brain-based technologies for human augmentation face challenges in personalization and real-world translation. We present an AI-driven personalized Bayesian optimization algorithm that remotely adjusts neurostimulation parameters based on baseline ability and head anatomy to enhance sustained attention at home. Validated through in silico modeling and a double-blind, sham-controlled study, our approach aligns with MRI-based models and neurobiological theories, maximizing efficacy and enabling scalable, personalized cognitive enhancement and therapy in real-world settings.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"28 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144719685","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}

{"title":"Evaluation of performance of generative large language models for stroke care","authors":"John Tayu Lee, Vincent Cheng-Sheng Li, Jia-Jyun Wu, Hsiao-Hui Chen, Sophia Sin-Yu Su, Brian Pin-Hsuan Chang, Richard Lee Lai, Chi-Hung Liu, Chung-Ting Chen, Valis Tanapima, Toby Kai-Bo Shen, Rifat Atun","doi":"10.1038/s41746-025-01830-9","DOIUrl":"https://doi.org/10.1038/s41746-025-01830-9","url":null,"abstract":"<p>Stroke is a leading cause of global morbidity and mortality, disproportionately impacting lower socioeconomic groups. In this study, we evaluated three generative LLMs—GPT, Claude, and Gemini—across four stages of stroke care: prevention, diagnosis, treatment, and rehabilitation. Using three prompt engineering techniques—Zero-Shot Learning (ZSL), Chain of Thought (COT), and Talking Out Your Thoughts (TOT)—we applied each to realistic stroke scenarios. Clinical experts assessed the outputs across five domains: (1) accuracy; (2) hallucinations; (3) specificity; (4) empathy; and (5) actionability, based on clinical competency benchmarks. Overall, the LLMs demonstrated suboptimal performance with inconsistent scores across domains. Each prompt engineering method showed strengths in specific areas: TOT does well in empathy and actionability, COT was strong in structured reasoning during diagnosis, and ZSL provided concise, accurate responses with fewer hallucinations, especially in the Treatment stage. However, none consistently met high clinical standards across all stroke care stages.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"90 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144719385","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}

{"title":"Rapid vessel segmentation and reconstruction of head and neck angiograms from MR vessel wall images","authors":"Jin Zhang, Wen Wang, Jinhua Dong, Xiong Yang, Shuwei Bai, Jiaqi Tian, Bo Li, Xiao Li, Jianjian Zhang, Hangyu Wu, Xiaoxi Zeng, Yongqiang Ye, Shenghao Ding, Jieqing Wan, Ke Wu, Yufei Mao, Cheng Li, Na Zhang, Jianrong Xu, Yongming Dai, Feng Shi, Beibei Sun, Yan Zhou, Huilin Zhao","doi":"10.1038/s41746-025-01866-x","DOIUrl":"https://doi.org/10.1038/s41746-025-01866-x","url":null,"abstract":"<p>Three-dimensional magnetic resonance vessel wall imaging (3D MR-VWI) is critical for characterizing cerebrovascular pathologies, yet its clinical adoption is hindered by labor-intensive postprocessing. We developed VWI Assistant, a multi-sequence integrated deep learning platform trained on multicenter data (study cohorts 1981 patients and imaging datasets) to automate artery segmentation and reconstruction. The framework demonstrated robust performance across diverse patient populations, imaging protocols, and scanner manufacturers, achieving 92.9% qualified rate comparable to expert manual delineation. VWI Assistant reduced processing time by over 90% (10–12 min per case) compared to manual methods (<i>p</i> &lt; 0.001) and improved inter-/intra-reader agreement. Real-world deployment (<i>n</i> = 1099 patients) demonstrated rapid clinical adoption, with utilization rates increasing from 10.8% to 100.0% within 12 months. By streamlining 3D MR-VWI workflows, VWI Assistant addresses scalability challenges in vascular imaging, offering a practical tool for routine use and large-scale research, significantly improving workflow efficiency while reducing labor and time costs.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"4 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144715284","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}

{"title":"A consensus statement on the use of digital twins in medicine","authors":"Jeffrey David Iqbal, Michael Krauthammer, Claudia M. Witt, Nikola Biller-Andorno, Markus Christen","doi":"10.1038/s41746-025-01897-4","DOIUrl":"https://doi.org/10.1038/s41746-025-01897-4","url":null,"abstract":"<p>Digital Health Technologies represent a marked shift from current medical technologies in use, the approach to health and healthcare and stakeholders engaged in healthcare delivery. What the digitalized future of medicine will look like and how it should be governed is unclear. A participatory process with interdisciplinary expert groups developed scenarios of Artificial Intelligence use in medicine and recommendations on their governance. The process included a patient-consumer focus group and the recommendations were validated by a representative population survey in Switzerland. Digital twins were identified as a pivotal innovation for personalized healthcare, with 62% of the Swiss population expressing interest, though 87% oppose mandatory use. Additionally, 75% view the state as responsible for ensuring necessary infrastructure. Digital twins are seen as an opportunity to support both the healthcare provider as well as patient-consumer directly in different modes of use and along functions, prevention, diagnosis, prognosis, and therapy.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"59 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144719686","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}

{"title":"Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson’s disease","authors":"Rebecca Ting Jiin Loo, Lukas Pavelka, Graziella Mangone, Fouad Khoury, Marie Vidailhet, Jean-Christophe Corvol, Enrico Glaab","doi":"10.1038/s41746-025-01862-1","DOIUrl":"https://doi.org/10.1038/s41746-025-01862-1","url":null,"abstract":"<p>Cognitive impairment is a frequent complication of Parkinson’s disease (PD), affecting up to half of newly diagnosed patients. To improve early detection and risk assessment, we developed machine learning models using clinical data from three independent PD cohorts, which are (LuxPARK, PPMI, ICEBERG). Models were trained to predict mild cognitive impairment (<i>PD-MCI</i>) and subjective cognitive decline (<i>SCD</i>) using Explainable Artificial Intelligence (XAI) for classification and time-to-event analysis. Multi-cohort models showed greater performance stability over single-cohort models, while retaining competitive average performance. Age at diagnosis and visuospatial ability were identified as key predictors. Significant sex differences observed highlight the importance of considering sex-specific factors in cognitive assessment. Men were more likely to report <i>SCD</i>. Our findings highlight the potential of multi-cohort machine learning for early identification and personalized management of cognitive decline in PD.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"214 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144712173","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}

{"title":"AI-driven preclinical disease risk assessment using imaging in UK biobank","authors":"Dmitrii Seletkov, Sophie Starck, Tamara T. Mueller, Yundi Zhang, Lisa Steinhelfer, Daniel Rueckert, Rickmer Braren","doi":"10.1038/s41746-025-01771-3","DOIUrl":"https://doi.org/10.1038/s41746-025-01771-3","url":null,"abstract":"<p>Identifying disease risk and detecting disease before clinical symptoms appear are essential for early intervention and improving patient outcomes. In this context, the integration of medical imaging in a clinical workflow offers a unique advantage by capturing detailed structural and functional information. Unlike non-image data, such as lifestyle, sociodemographic, or prior medical conditions, which often rely on self-reported information susceptible to recall biases and subjective perceptions, imaging offers more objective and reliable insights. Although the use of medical imaging in artificial intelligence (AI)-driven risk assessment is growing, its full potential remains underutilized. In this work, we demonstrate how imaging can be integrated into routine screening workflows, in particular by taking advantage of neck-to-knee whole-body magnetic resonance imaging (MRI) data available in the large prospective study UK Biobank. Our analysis focuses on three-year risk assessment for a broad spectrum of diseases, including cardiovascular, digestive, metabolic, inflammatory, degenerative, and oncologic conditions. We evaluate AI-based pipelines for processing whole-body MRI and demonstrate that using image-derived radiomics features provides the best prediction performance, interpretability, and integration capability with non-image data.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"29 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144712316","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}

{"title":"The role of face regions in remote photoplethysmography for contactless heart rate monitoring","authors":"Maksym Bondarenko, Carlo Menon, Mohamed Elgendi","doi":"10.1038/s41746-025-01814-9","DOIUrl":"https://doi.org/10.1038/s41746-025-01814-9","url":null,"abstract":"<p>Heart rate (HR) estimation is crucial for early cardiovascular diagnosis, continuous monitoring, and various health applications. While electrocardiography (ECG) remains the gold standard, its discomfort and impracticality for continuous use have spurred the development of non-contact methods like remote photoplethysmography (rPPG). This systematic review (PROSPERO: CRD 42024592157) examines 70 studies to assess the impact of Region of Interest (ROI) selection on HR estimation accuracy. Most methods (36.8%) use the holistic face, while forehead and cheek areas (24.5% and 21.7%) show superior accuracy. Machine learning-based approaches outperform traditional methods under motion artifacts and poor lighting, achieving Mean Absolute Error and Root Mean Square Error below 1.0 for some datasets. Combining multiple patches improves performance, though increasing ROIs beyond 60 patches results in diminishing returns and higher computational complexity. These findings highlight the significance of ROI optimization for robust rPPG-based HR estimation.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"21 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710632","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}

{"title":"Personalized federated learning for predicting disability progression in multiple sclerosis using real-world routine clinical data.","authors":"Ashkan Pirmani,Edward De Brouwer,Ádám Arany,Martijn Oldenhof,Antoine Passemiers,Axel Faes,Tomas Kalincik,Serkan Ozakbas,Riadh Gouider,Barbara Willekens,Dana Horakova,Eva Kubala Havrdova,Francesco Patti,Alexandre Prat,Alessandra Lugaresi,Valentina Tomassini,Pierre Grammond,Elisabetta Cartechini,Izanne Roos,Cavit Boz,Raed Alroughani,Maria Pia Amato,Katherine Buzzard,Jeannette Lechner-Scott,Joana Guimarães,Claudio Solaro,Oliver Gerlach,Aysun Soysal,Jens Kuhle,Jose Luis Sanchez-Menoyo,Daniele Spitaleri,Tunde Csepany,Bart Van Wijmeersch,Radek Ampapa,Julie Prevost,Samia J Khoury,Vincent Van Pesch,Nevin John,Davide Maimone,Bianca Weinstock-Guttman,Guy Laureys,Pamela McCombe,Yolanda Blanco,Ayse Altintas,Abdullah Al-Asmi,Justin Garber,Anneke Van der Walt,Helmut Butzkueven,Koen de Gans,Csilla Rozsa,Bruce Taylor,Talal Al-Harbi,Attila Sas,Cecilia Rajda,Orla Gray,Danny Decoo,William M Carroll,Allan G Kermode,Marzena Fabis-Pedrini,Deborah Mason,Angel Perez-Sempere,Mihaela Simu,Neil Shuey,Bhim Singhal,Marija Cauchi,Todd A Hardy,Sudarshini Ramanathan,Patrice Lalive,Carmen-Adella Sirbu,Stella Hughes,Tamara Castillo Trivino,Liesbet M Peeters,Yves Moreau","doi":"10.1038/s41746-025-01788-8","DOIUrl":"https://doi.org/10.1038/s41746-025-01788-8","url":null,"abstract":"Early prediction of disability progression in multiple sclerosis (MS) remains challenging despite its critical importance for therapeutic decision-making. We present the first systematic evaluation of personalized federated learning (PFL) for 2-year MS disability progression prediction, leveraging multi-center real-world data from over 26,000 patients. While conventional federated learning (FL) enables privacy-aware collaborative modeling, it remains vulnerable to institutional data heterogeneity. PFL overcomes this challenge by adapting shared models to local data distributions without compromising privacy. We evaluated two personalization strategies: a novel AdaptiveDualBranchNet architecture with selective parameter sharing, and personalized fine-tuning of global models, benchmarked against centralized and client-specific approaches. Baseline FL underperformed relative to personalized methods, whereas personalization significantly improved performance, with personalized FedProx and FedAVG achieving ROC-AUC scores of 0.8398 ± 0.0019 and 0.8384 ± 0.0014, respectively. These findings establish personalization as critical for scalable, privacy-aware clinical prediction models and highlight its potential to inform earlier intervention strategies in MS and beyond.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"114 1","pages":"478"},"PeriodicalIF":15.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144701079","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}

{"title":"Equity-enhanced glaucoma progression prediction from OCT with knowledge distillation","authors":"Sulaiman O. Afolabi, Leila Gheisi, Jing Shan, Lucy Q. Shen, Mengyu Wang, Min Shi","doi":"10.1038/s41746-025-01884-9","DOIUrl":"https://doi.org/10.1038/s41746-025-01884-9","url":null,"abstract":"<p>Glaucoma is a progressive disease that can lead to permanent vision loss, making progression prediction vital for guiding effective treatment. Deep learning aids progression prediction but may yield unequal outcomes across demographic groups. We proposed a model called FairDist, which utilized baseline optical coherence tomography scans to predict glaucoma progression. An equity-aware EfficientNet was trained for glaucoma detection, which was then adapted for progression prediction with knowledge distillation. Model accuracy was measured by the AUC, Sensitivity, Specificity, and equity was assessed using equity-scaled AUC, which adjusts AUC by accounting for subgroup disparities. The mean deviation, fast progression, and total deviation pointwise progression were explored in this work. For both progression types, FairDist achieved the highest AUC and equity-scaled AUC for gender and racial groups, compared to methods with and without unfairness mitigation strategies. FairDist can be generalized to other disease progression prediction tasks to potentially achieve improved performance and fairness.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"47 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144701903","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}

{"title":"Predicting outcomes following endovascular aortoiliac revascularization using machine learning","authors":"Ben Li, Badr Aljabri, Derek Beaton, Leen Al-Omran, Mohamad A. Hussain, Douglas S. Lee, Duminda N. Wijeysundera, Ori D. Rotstein, Charles de Mestral, Muhammad Mamdani, Mohammed Al-Omran","doi":"10.1038/s41746-025-01865-y","DOIUrl":"https://doi.org/10.1038/s41746-025-01865-y","url":null,"abstract":"<p>Endovascular aortoiliac revascularization is a common treatment option for peripheral artery disease that carries non-negligible risks. Outcome prediction tools may support clinical decision-making but remain limited. We developed machine learning algorithms that predict 30-day post-procedural outcomes. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent endovascular aortoiliac revascularization between 2011–2021. Input features included 37 pre-operative demographic/clinical variables. The primary outcome was 30-day post-procedural major adverse limb event (MALE) or death. Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using pre-operative features. Overall, 6601 patients were included, and 30-day MALE/death occurred in 470 (7.1%) individuals. The best-performing model was XGBoost, achieving an AUROC (95% CI) of 0.94 (0.93–0.95). In comparison, logistic regression had an AUROC (95% CI) of 0.74 (0.73–0.76). The XGBoost model accurately predicted 30-day post-procedural outcomes, performing better than logistic regression.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"14 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144693972","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}