NPJ Digital Medicine最新文献

{"title":"Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs","authors":"Shawn T. O’Neil, Charisse Madlock-Brown, Kenneth J. Wilkins, Brenda M. McGrath, Hannah E. Davis, Gina S. Assaf, Hannah Wei, Parya Zareie, Evan T. French, Johanna Loomba, Julie A. McMurry, Andrea Zhou, Christopher G. Chute, Richard A. Moffitt, Emily R. Pfaff, Yun Jae Yoo, Peter Leese, Robert F. Chew, Michael Lieberman, Melissa A. Haendel, the N3C and RECOVER Consortia","doi":"10.1038/s41746-024-01286-3","DOIUrl":"10.1038/s41746-024-01286-3","url":null,"abstract":"Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to associate patients with clusters over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-13"},"PeriodicalIF":12.4,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11494196/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142471110","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}

{"title":"Privacy enhancing and generalizable deep learning with synthetic data for mediastinal neoplasm diagnosis","authors":"Zhanping Zhou, Yuchen Guo, Ruijie Tang, Hengrui Liang, Jianxing He, Feng Xu","doi":"10.1038/s41746-024-01290-7","DOIUrl":"10.1038/s41746-024-01290-7","url":null,"abstract":"The success of deep learning (DL) relies heavily on training data from which DL models encapsulate information. Consequently, the development and deployment of DL models expose data to potential privacy breaches, which are particularly critical in data-sensitive contexts like medicine. We propose a new technique named DiffGuard that generates realistic and diverse synthetic medical images with annotations, even indistinguishable for experts, to replace real data for DL model training, which cuts off their direct connection and enhances privacy safety. We demonstrate that DiffGuard enhances privacy safety with much less data leakage and better resistance against privacy attacks on data and model. It also improves the accuracy and generalizability of DL models for segmentation and classification of mediastinal neoplasms in multi-center evaluation. We expect that our solution would enlighten the road to privacy-preserving DL for precision medicine, promote data and model sharing, and inspire more innovation on artificial-intelligence-generated-content technologies for medicine.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-15"},"PeriodicalIF":12.4,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01290-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451281","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}

{"title":"Early detection of dementia through retinal imaging and trustworthy AI","authors":"Jinkui Hao, William R. Kwapong, Ting Shen, Huazhu Fu, Yanwu Xu, Qinkang Lu, Shouyue Liu, Jiong Zhang, Yonghuai Liu, Yifan Zhao, Yalin Zheng, Alejandro F. Frangi, Shuting Zhang, Hong Qi, Yitian Zhao","doi":"10.1038/s41746-024-01292-5","DOIUrl":"10.1038/s41746-024-01292-5","url":null,"abstract":"Alzheimer’s disease (AD) is a global healthcare challenge lacking a simple and affordable detection method. We propose a novel deep learning framework, Eye-AD, to detect Early-onset Alzheimer’s Disease (EOAD) and Mild Cognitive Impairment (MCI) using OCTA images of retinal microvasculature and choriocapillaris. Eye-AD employs a multilevel graph representation to analyze intra- and inter-instance relationships in retinal layers. Using 5751 OCTA images from 1671 participants in a multi-center study, our model demonstrated superior performance in EOAD (internal data: AUC = 0.9355, external data: AUC = 0.9007) and MCI detection (internal data: AUC = 0.8630, external data: AUC = 0.8037). Furthermore, we explored the associations between retinal structural biomarkers in OCTA images and EOAD/MCI, and the results align well with the conclusions drawn from our deep learning interpretability analysis. Our findings provide further evidence that retinal OCTA imaging, coupled with artificial intelligence, will serve as a rapid, noninvasive, and affordable dementia detection.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-15"},"PeriodicalIF":12.4,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01292-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451306","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}

{"title":"Feasibility of snapshot testing using wearable sensors to detect cardiorespiratory illness (COVID infection in India)","authors":"Olivia K. Botonis, Jonathan Mendley, Shreya Aalla, Nicole C. Veit, Michael Fanton, JongYoon Lee, Vikrant Tripathi, Venkatesh Pandi, Akash Khobragade, Sunil Chaudhary, Amitav Chaudhuri, Vaidyanathan Narayanan, Shuai Xu, Hyoyoung Jeong, John A. Rogers, Arun Jayaraman","doi":"10.1038/s41746-024-01287-2","DOIUrl":"10.1038/s41746-024-01287-2","url":null,"abstract":"The COVID-19 pandemic has challenged the current paradigm of clinical and community-based disease detection. We present a multimodal wearable sensor system paired with a two-minute, movement-based activity sequence that successfully captures a snapshot of physiological data (including cardiac, respiratory, temperature, and percent oxygen saturation). We conducted a large, multi-site trial of this technology across India from June 2021 to April 2022 amidst the COVID-19 pandemic (Clinical trial registry name: International Validation of Wearable Sensor to Monitor COVID-19 Like Signs and Symptoms; NCT05334680; initial release: 04/15/2022). An Extreme Gradient Boosting algorithm was trained to discriminate between COVID-19 infected individuals (n = 295) and COVID-19 negative healthy controls (n = 172) and achieved an F1-Score of 0.80 (95% CI = [0.79, 0.81]). SHAP values were mapped to visualize feature importance and directionality, yielding engineered features from core temperature, cough, and lung sounds as highly important. The results demonstrated potential for data-driven wearable sensor technology for remote preliminary screening, highlighting a fundamental pivot from continuous to snapshot monitoring of cardiorespiratory illnesses.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-12"},"PeriodicalIF":12.4,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01287-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449441","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}

{"title":"Author Correction: Games Wide Open to athlete partnership in building artificial intelligence systems","authors":"Yosra Magdi Mekki, Osman Hassan Ahmed, Dylan Powell, Amy Price, H. Paul Dijkstra","doi":"10.1038/s41746-024-01284-5","DOIUrl":"10.1038/s41746-024-01284-5","url":null,"abstract":"","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-1"},"PeriodicalIF":12.4,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01284-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451307","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}

{"title":"Biologically informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post treatment glioblastoma","authors":"Hairong Wang, Michael G. Argenziano, Hyunsoo Yoon, Deborah Boyett, Akshay Save, Petros Petridis, William Savage, Pamela Jackson, Andrea Hawkins-Daarud, Nhan Tran, Leland Hu, Kyle W. Singleton, Lisa Paulson, Osama Al Dalahmah, Jeffrey N. Bruce, Jack Grinband, Kristin R. Swanson, Peter Canoll, Jing Li","doi":"10.1038/s41746-024-01277-4","DOIUrl":"10.1038/s41746-024-01277-4","url":null,"abstract":"Intratumoral heterogeneity poses a significant challenge to the diagnosis and treatment of recurrent glioblastoma. This study addresses the need for non-invasive approaches to map heterogeneous landscape of histopathological alterations throughout the entire lesion for each patient. We developed BioNet, a biologically-informed neural network, to predict regional distributions of two primary tissue-specific gene modules: proliferating tumor (Pro) and reactive/inflammatory cells (Inf). BioNet significantly outperforms existing methods (p < 2e-26). In cross-validation, BioNet achieved AUCs of 0.80 (Pro) and 0.81 (Inf), with accuracies of 80% and 75%, respectively. In blind tests, BioNet achieved AUCs of 0.80 (Pro) and 0.76 (Inf), with accuracies of 81% and 74%. Competing methods had AUCs lower or around 0.6 and accuracies lower or around 70%. BioNet’s voxel-level prediction maps reveal intratumoral heterogeneity, potentially improving biopsy targeting and treatment evaluation. This non-invasive approach facilitates regular monitoring and timely therapeutic adjustments, highlighting the role of ML in precision medicine.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-14"},"PeriodicalIF":12.4,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01277-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451305","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}

{"title":"Guidance for unbiased predictive information for healthcare decision-making and equity (GUIDE): considerations when race may be a prognostic factor","authors":"Keren Ladin, John Cuddeback, O. Kenrik Duru, Sharad Goel, William Harvey, Jinny G. Park, Jessica K. Paulus, Joyce Sackey, Richard Sharp, Ewout Steyerberg, Berk Ustun, David van Klaveren, Saul N. Weingart, David M. Kent","doi":"10.1038/s41746-024-01245-y","DOIUrl":"10.1038/s41746-024-01245-y","url":null,"abstract":"Clinical prediction models (CPMs) are tools that compute the risk of an outcome given a set of patient characteristics and are routinely used to inform patients, guide treatment decision-making, and resource allocation. Although much hope has been placed on CPMs to mitigate human biases, CPMs may potentially contribute to racial disparities in decision-making and resource allocation. While some policymakers, professional organizations, and scholars have called for eliminating race as a variable from CPMs, others raise concerns that excluding race may exacerbate healthcare disparities and this controversy remains unresolved. The Guidance for Unbiased predictive Information for healthcare Decision-making and Equity (GUIDE) provides expert guidelines for model developers and health system administrators on the transparent use of race in CPMs and mitigation of algorithmic bias across contexts developed through a 5-round, modified Delphi process from a diverse 14-person technical expert panel (TEP). Deliberations affirmed that race is a social construct and that the goals of prediction are distinct from those of causal inference, and emphasized: the importance of decisional context (e.g., shared decision-making versus healthcare rationing); the conflicting nature of different anti-discrimination principles (e.g., anticlassification versus antisubordination principles); and the importance of identifying and balancing trade-offs in achieving equity-related goals with race-aware versus race-unaware CPMs for conditions where racial identity is prognostically informative. The GUIDE, comprising 31 key items in the development and use of CPMs in healthcare, outlines foundational principles, distinguishes between bias and fairness, and offers guidance for examining subgroup invalidity and using race as a variable in CPMs. This GUIDE presents a living document that supports appraisal and reporting of bias in CPMs to support best practice in CPM development and use.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-12"},"PeriodicalIF":12.4,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01245-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449461","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}

{"title":"A randomized clinical trial testing digital mindset intervention for knee osteoarthritis pain and activity improvement","authors":"Melissa A. Boswell, Kris M. Evans, Disha Ghandwani, Trevor Hastie, Sean R. Zion, Paula L. Moya, Nicholas J. Giori, Jennifer L. Hicks, Alia J. Crum, Scott L. Delp","doi":"10.1038/s41746-024-01281-8","DOIUrl":"10.1038/s41746-024-01281-8","url":null,"abstract":"This randomized clinical trial evaluated the effectiveness of short, digital interventions in improving physical activity and pain for individuals with knee osteoarthritis. We compared a digital mindset intervention, focusing on adaptive mindsets (e.g., osteoarthritis is manageable), to a digital education intervention and a no-intervention group. 408 participants with knee osteoarthritis completed the study online in the US. The mindset intervention significantly improved mindsets compared to both other groups (P < 0.001) and increased physical activity levels more than the no-intervention group (mean = 28.6 points, P = 0.001), but pain reduction was not significant. The mindset group also showed significantly greater improvements in the perceived need for surgery, self-imposed physical limitations, fear of movement, and self-efficacy than the no-intervention and education groups. This trial demonstrates the effectiveness of brief digital interventions in educating about osteoarthritis and further highlights the additional benefits of improving mindsets to transform patients’ approach to disease management. The study was prospectively registered (ClinicalTrials.gov: NCT05698368, 2023-01-26).","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-11"},"PeriodicalIF":12.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01281-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443797","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}

{"title":"Addressing fairness issues in deep learning-based medical image analysis: a systematic review","authors":"Zikang Xu, Jun Li, Qingsong Yao, Han Li, Mingyue Zhao, S. Kevin Zhou","doi":"10.1038/s41746-024-01276-5","DOIUrl":"10.1038/s41746-024-01276-5","url":null,"abstract":"Deep learning algorithms have demonstrated remarkable efficacy in various medical image analysis (MedIA) applications. However, recent research highlights a performance disparity in these algorithms when applied to specific subgroups, such as exhibiting poorer predictive performance in elderly females. Addressing this fairness issue has become a collaborative effort involving AI scientists and clinicians seeking to understand its origins and develop solutions for mitigation within MedIA. In this survey, we thoroughly examine the current advancements in addressing fairness issues in MedIA, focusing on methodological approaches. We introduce the basics of group fairness and subsequently categorize studies on fair MedIA into fairness evaluation and unfairness mitigation. Detailed methods employed in these studies are presented too. Our survey concludes with a discussion of existing challenges and opportunities in establishing a fair MedIA and healthcare system. By offering this comprehensive review, we aim to foster a shared understanding of fairness among AI researchers and clinicians, enhance the development of unfairness mitigation methods, and contribute to the creation of an equitable MedIA society.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-16"},"PeriodicalIF":12.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01276-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443798","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}

{"title":"India’s evolving digital health strategy","authors":"Aditya Narayan, Indu Bhushan, Kevin Schulman","doi":"10.1038/s41746-024-01279-2","DOIUrl":"10.1038/s41746-024-01279-2","url":null,"abstract":"India’s evolving digital health strategy leverages innovative technologies to enhance access to healthcare services. This paper explores the key components of India’s digital health transformation, including the Ayushman Bharat Digital Mission (ABDM) and India’s integration of biometric identification and digital infrastructure to improve healthcare delivery. The lessons learned from India’s large-scale implementation of digital health provide valuable insights for global health markets and digital transformations in healthcare systems.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-3"},"PeriodicalIF":12.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01279-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440371","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}