NPJ Digital Medicine最新文献_第5页

A scoping review on advancements in noninvasive wearable technology for heart failure management 无创可穿戴技术在心力衰竭管理方面的进展范围综述

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-12 DOI: 10.1038/s41746-024-01268-5

Niels T. B. Scholte, Annemiek. E. van Ravensberg, Abdul Shakoor, Eric Boersma, Eelko Ronner, Rudolf A. de Boer, Jasper J. Brugts, Nico Bruining, Robert M. A. van der Boon

{"title":"A scoping review on advancements in noninvasive wearable technology for heart failure management","authors":"Niels T. B. Scholte, Annemiek. E. van Ravensberg, Abdul Shakoor, Eric Boersma, Eelko Ronner, Rudolf A. de Boer, Jasper J. Brugts, Nico Bruining, Robert M. A. van der Boon","doi":"10.1038/s41746-024-01268-5","DOIUrl":"10.1038/s41746-024-01268-5","url":null,"abstract":"Wearables offer a promising solution for enhancing remote monitoring (RM) of heart failure (HF) patients by tracking key physiological parameters. Despite their potential, their clinical integration faces challenges due to the lack of rigorous evaluations. This review aims to summarize the current evidence and assess the readiness of wearables for clinical practice using the Medical Device Readiness Level (MDRL). A systematic search identified 99 studies from 3112 found articles, with only eight being randomized controlled trials. Accelerometery was the most used measurement technique. Consumer-grade wearables, repurposed for HF monitoring, dominated the studies with most of them in the feasibility testing stage (MDRL 6). Only two of the described wearables were specifically designed for HF RM, and received FDA approval. Consequently, the actual impact of wearables on HF management remains uncertain due to limited robust evidence, posing a significant barrier to their integration into HF care.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01268-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142415369","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

Systematic review and meta-analysis of standalone digital interventions for cognitive symptoms in people without dementia 针对无痴呆症患者认知症状的独立数字干预措施的系统回顾和荟萃分析

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-10 DOI: 10.1038/s41746-024-01280-9

Veronica Cabreira, Tim Wilkinson, Lisbeth Frostholm, Jon Stone, Alan Carson

{"title":"Systematic review and meta-analysis of standalone digital interventions for cognitive symptoms in people without dementia","authors":"Veronica Cabreira, Tim Wilkinson, Lisbeth Frostholm, Jon Stone, Alan Carson","doi":"10.1038/s41746-024-01280-9","DOIUrl":"10.1038/s41746-024-01280-9","url":null,"abstract":"Cognitive symptoms are prevalent across neuropsychiatric disorders, increase distress and impair quality of life. Self-guided digital interventions offer accessibility, scalability, and may overcome the research-to-practice treatment gap. Seventy-six trials with 5214 participants were identified. A random-effects meta-analysis investigated the effects of all digital self-guided interventions, compared to controls, at post-treatment. We found a small-to-moderate positive pooled effect on cognition (k = 71; g = −0.51, 95%CI −0.64 to −0.37; p < 0.00001) and mental health (k = 30; g = −0.41, 95%CI −0.60 to −0.22; p < 0.0001). Positive treatment effects on fatigue (k = 8; g = −0.27, 95%CI −0.53 to −0.02; p = 0.03) and quality of life (k = 22; g = −0.17, 95%CI −0.34 to −0.00; p = 0.04) were only marginally significant. No significant benefit was found for performance on activities of daily living. Results were independent of control groups, treatment duration, risk of bias and delivery format. Self-guided digital transdiagnostic interventions may benefit at least a subset of patients in the short run, yet their impact on non-cognitive outcomes remains uncertain.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01280-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397857","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

Individualized decision making in on-scene resuscitation time for out-of-hospital cardiac arrest using reinforcement learning 利用强化学习在院外心脏骤停的现场复苏时间内做出个性化决策

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-09 DOI: 10.1038/s41746-024-01278-3

Dong Hyun Choi, Min Hyuk Lim, Ki Jeong Hong, Young Gyun Kim, Jeong Ho Park, Kyoung Jun Song, Sang Do Shin, Sungwan Kim

{"title":"Individualized decision making in on-scene resuscitation time for out-of-hospital cardiac arrest using reinforcement learning","authors":"Dong Hyun Choi, Min Hyuk Lim, Ki Jeong Hong, Young Gyun Kim, Jeong Ho Park, Kyoung Jun Song, Sang Do Shin, Sungwan Kim","doi":"10.1038/s41746-024-01278-3","DOIUrl":"10.1038/s41746-024-01278-3","url":null,"abstract":"On-scene resuscitation time is associated with out-of-hospital cardiac arrest (OHCA) outcomes. We developed and validated reinforcement learning models for individualized on-scene resuscitation times, leveraging nationwide Korean data. Adult OHCA patients with a medical cause of arrest were included (N = 73,905). The optimal policy was derived from conservative Q-learning to maximize survival. The on-scene return of spontaneous circulation hazard rates estimated from the Random Survival Forest were used as intermediate rewards to handle sparse rewards, while patients’ historical survival was reflected in the terminal rewards. The optimal policy increased the survival to hospital discharge rate from 9.6% to 12.5% (95% CI: 12.2–12.8) and the good neurological recovery rate from 5.4% to 7.5% (95% CI: 7.3–7.7). The recommended maximum on-scene resuscitation times for patients demonstrated a bimodal distribution, varying with patient, emergency medical services, and OHCA characteristics. Our survival analysis-based approach generates explainable rewards, reducing subjectivity in reinforcement learning.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01278-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142385476","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

Screening chronic kidney disease through deep learning utilizing ultra-wide-field fundus images 通过深度学习利用超宽视场眼底图像筛查慢性肾病

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-07 DOI: 10.1038/s41746-024-01271-w

Xinyu Zhao, Xingwang Gu, Lihui Meng, Yongwei Chen, Qing Zhao, Shiyu Cheng, Wenfei Zhang, Tiantian Cheng, Chuting Wang, Zhengming Shi, Shengyin Jiao, Changlong Jiang, Guofang Jiao, Da Teng, Xiaolei Sun, Bilei Zhang, Yakun Li, Huiqin Lu, Changzheng Chen, Hao Zhang, Ling Yuan, Chang Su, Han Zhang, Song Xia, Anyi Liang, Mengda Li, Dan Zhu, Meirong Xue, Dawei Sun, Qiuming Li, Ziwu Zhang, Donglei Zhang, Hongbin Lv, Rishet Ahmat, Zilong Wang, Charumathi Sabanayagam, Xiaowei Ding, Tien Yin Wong, Youxin Chen

{"title":"Screening chronic kidney disease through deep learning utilizing ultra-wide-field fundus images","authors":"Xinyu Zhao, Xingwang Gu, Lihui Meng, Yongwei Chen, Qing Zhao, Shiyu Cheng, Wenfei Zhang, Tiantian Cheng, Chuting Wang, Zhengming Shi, Shengyin Jiao, Changlong Jiang, Guofang Jiao, Da Teng, Xiaolei Sun, Bilei Zhang, Yakun Li, Huiqin Lu, Changzheng Chen, Hao Zhang, Ling Yuan, Chang Su, Han Zhang, Song Xia, Anyi Liang, Mengda Li, Dan Zhu, Meirong Xue, Dawei Sun, Qiuming Li, Ziwu Zhang, Donglei Zhang, Hongbin Lv, Rishet Ahmat, Zilong Wang, Charumathi Sabanayagam, Xiaowei Ding, Tien Yin Wong, Youxin Chen","doi":"10.1038/s41746-024-01271-w","DOIUrl":"10.1038/s41746-024-01271-w","url":null,"abstract":"To address challenges in screening for chronic kidney disease (CKD), we devised a deep learning-based CKD screening model named UWF-CKDS. It utilizes ultra-wide-field (UWF) fundus images to predict the presence of CKD. We validated the model with data from 23 tertiary hospitals across China. Retinal vessels and retinal microvascular parameters (RMPs) were extracted to enhance model interpretability, which revealed a significant correlation between renal function and RMPs. UWF-CKDS, utilizing UWF images, RMPs, and relevant medical history, can accurately determine CKD status. Importantly, UWF-CKDS exhibited superior performance compared to CTR-CKDS, a model developed using the central region (CTR) cropped from UWF images, underscoring the contribution of the peripheral retina in predicting renal function. The study presents UWF-CKDS as a highly implementable method for large-scale and accurate CKD screening at the population level.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01271-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142383835","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

Enabling data linkages for rare diseases in a resilient environment with the SERDIF framework 利用 SERDIF 框架，在弹性环境中实现罕见疾病的数据链接。

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-04 DOI: 10.1038/s41746-024-01267-6

Albert Navarro-Gallinad, Fabrizio Orlandi, Jennifer Scott, Enock Havyarimana, Neil Basu, Mark A. Little, Declan O’Sullivan

{"title":"Enabling data linkages for rare diseases in a resilient environment with the SERDIF framework","authors":"Albert Navarro-Gallinad, Fabrizio Orlandi, Jennifer Scott, Enock Havyarimana, Neil Basu, Mark A. Little, Declan O’Sullivan","doi":"10.1038/s41746-024-01267-6","DOIUrl":"10.1038/s41746-024-01267-6","url":null,"abstract":"Environmental factors amplified by climate change contribute significantly to the global burden of disease, disproportionately impacting vulnerable populations, such as individuals with rare diseases. Researchers require innovative, dynamic data linkage methods to enable the development of risk prediction models, particularly for diseases like vasculitis with unknown aetiology but potential environmental triggers. In response, we present the Semantic Environmental and Rare Disease Data Integration Framework (SERDIF). SERDIF was evaluated with researchers studying climate-related health hazards of vasculitis disease activity across European countries (NP1 = 10, NP2 = 17, NP3 = 23). Usability metrics consistently improved, indicating SERDIF’s effectiveness in linking complex environmental and health datasets. Furthermore, SERDIF-enabled epidemiologists to study environmental factors in a pregnancy cohort in Lombardy, showcasing its versatility beyond rare diseases. This framework offers for the first time a user-friendly, FAIR-compliant design for environment-health data linkage with export capabilities enabling data analysis to mitigate health risks posed by climate change.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11452697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142375762","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

A scoping review of reporting gaps in FDA-approved AI medical devices 对 FDA 批准的人工智能医疗器械的报告差距进行范围审查

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-03 DOI: 10.1038/s41746-024-01270-x

Vijaytha Muralidharan, Boluwatife Adeleye Adewale, Caroline J. Huang, Mfon Thelma Nta, Peter Oluwaduyilemi Ademiju, Pirunthan Pathmarajah, Man Kien Hang, Oluwafolajimi Adesanya, Ridwanullah Olamide Abdullateef, Abdulhammed Opeyemi Babatunde, Abdulquddus Ajibade, Sonia Onyeka, Zhou Ran Cai, Roxana Daneshjou, Tobi Olatunji

{"title":"A scoping review of reporting gaps in FDA-approved AI medical devices","authors":"Vijaytha Muralidharan, Boluwatife Adeleye Adewale, Caroline J. Huang, Mfon Thelma Nta, Peter Oluwaduyilemi Ademiju, Pirunthan Pathmarajah, Man Kien Hang, Oluwafolajimi Adesanya, Ridwanullah Olamide Abdullateef, Abdulhammed Opeyemi Babatunde, Abdulquddus Ajibade, Sonia Onyeka, Zhou Ran Cai, Roxana Daneshjou, Tobi Olatunji","doi":"10.1038/s41746-024-01270-x","DOIUrl":"10.1038/s41746-024-01270-x","url":null,"abstract":"Machine learning and artificial intelligence (AI/ML) models in healthcare may exacerbate health biases. Regulatory oversight is critical in evaluating the safety and effectiveness of AI/ML devices in clinical settings. We conducted a scoping review on the 692 FDA-approved AI/ML-enabled medical devices approved from 1995-2023 to examine transparency, safety reporting, and sociodemographic representation. Only 3.6% of approvals reported race/ethnicity, 99.1% provided no socioeconomic data. 81.6% did not report the age of study subjects. Only 46.1% provided comprehensive detailed results of performance studies; only 1.9% included a link to a scientific publication with safety and efficacy data. Only 9.0% contained a prospective study for post-market surveillance. Despite the growing number of market-approved medical devices, our data shows that FDA reporting data remains inconsistent. Demographic and socioeconomic characteristics are underreported, exacerbating the risk of algorithmic bias and health disparity.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01270-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368727","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

Improving prognostic accuracy in lung transplantation using unique features of isolated human lung radiographs 利用离体人肺X光片的独特特征提高肺移植预后的准确性

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-03 DOI: 10.1038/s41746-024-01260-z

Bonnie T. Chao, Andrew T. Sage, Micheal C. McInnis, Jun Ma, Micah Grubert Van Iderstine, Xuanzi Zhou, Jerome Valero, Marcelo Cypel, Mingyao Liu, Bo Wang, Shaf Keshavjee

{"title":"Improving prognostic accuracy in lung transplantation using unique features of isolated human lung radiographs","authors":"Bonnie T. Chao, Andrew T. Sage, Micheal C. McInnis, Jun Ma, Micah Grubert Van Iderstine, Xuanzi Zhou, Jerome Valero, Marcelo Cypel, Mingyao Liu, Bo Wang, Shaf Keshavjee","doi":"10.1038/s41746-024-01260-z","DOIUrl":"10.1038/s41746-024-01260-z","url":null,"abstract":"Ex vivo lung perfusion (EVLP) enables advanced assessment of human lungs for transplant suitability. We developed a convolutional neural network (CNN)-based approach to analyze the largest cohort of isolated lung radiographs to date. CNNs were trained to process 1300 longitudinal radiographs from n = 650 clinical EVLP cases. Latent features were transformed into principal components (PC) and correlated with known radiographic findings. PCs were combined with physiological data to classify clinical outcomes: (1) recipient time to extubation of <72 h, (2) ≥ 72 h, and (3) lungs unsuitable for transplantation. The top PC was significantly correlated with infiltration (Spearman R: 0·72, p < 0·0001), and adding radiographic PCs significantly improved the discrimination for clinical outcomes (Accuracy: 73 vs 78%, p = 0·014). CNN-derived radiographic lung features therefore add substantial value to the current assessments. This approach can be adopted by EVLP centers worldwide to harness radiographic information without requiring real-time radiological expertise.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01260-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368731","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

Algorithmovigilance, lessons from pharmacovigilance 算法警戒，从药物警戒中汲取经验教训

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-02 DOI: 10.1038/s41746-024-01237-y

Alan Balendran, Mehdi Benchoufi, Theodoros Evgeniou, Philippe Ravaud

引用次数: 0

Machine learning explains response variability of deep brain stimulation on Parkinson’s disease quality of life 机器学习解释了深部脑刺激对帕金森病患者生活质量的影响。

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-02 DOI: 10.1038/s41746-024-01253-y

Enrico Ferrea, Farzin Negahbani, Idil Cebi, Daniel Weiss, Alireza Gharabaghi

{"title":"Machine learning explains response variability of deep brain stimulation on Parkinson’s disease quality of life","authors":"Enrico Ferrea, Farzin Negahbani, Idil Cebi, Daniel Weiss, Alireza Gharabaghi","doi":"10.1038/s41746-024-01253-y","DOIUrl":"10.1038/s41746-024-01253-y","url":null,"abstract":"Improving health-related quality of life (QoL) is crucial for managing Parkinson’s disease. However, QoL outcomes after deep brain stimulation (DBS) of the subthalamic nucleus (STN) vary considerably. Current approaches lack integration of demographic, patient-reported, neuroimaging, and neurophysiological data to understand this variability. This study used explainable machine learning to analyze multimodal factors affecting QoL changes, measured by the Parkinson’s Disease Questionnaire (PDQ-39) in 63 patients, and quantified each variable’s contribution. Results showed that preoperative PDQ-39 scores and upper beta band activity (>20 Hz) in the left STN were key predictors of QoL changes. Lower initial QoL burden predicted worsening, while improvement was associated with higher beta activity. Additionally, electrode positions along the superior-inferior axis, especially relative to the z = −7 coordinate in standard space, influenced outcomes, with improved and worsened QoL above and below this marker. This study emphasizes a tailored, data-informed approach to optimize DBS treatment and improve patient QoL.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01253-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142361792","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

Ethical guidance for reporting and evaluating claims of AI outperforming human doctors 报告和评估人工智能优于人类医生的道德指南

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-02 DOI: 10.1038/s41746-024-01255-w

Jojanneke Drogt, Megan Milota, Anne van den Brink, Karin Jongsma

引用次数: 0