Mayo Clinic Proceedings. Digital health最新文献

{"title":"Remote, Smart Device-Based Cardiac Rehabilitation After Myocardial Infarction: A Pilot, Randomized Cross-Over SmartRehab Study","authors":"Peter Wohlfahrt MD, PhD ,&nbsp;Dominik Jenča MD ,&nbsp;Vojtěch Melenovský MD, PhD ,&nbsp;Jolana Mrázková Mgr ,&nbsp;Marek Šramko MD, PhD ,&nbsp;Martin Kotrč MD ,&nbsp;Michael Želízko MD ,&nbsp;Věra Adámková MD, PhD ,&nbsp;Francisco Lopez-Jimenez MD, MSc, MBA ,&nbsp;Jan Piťha MD, PhD ,&nbsp;Josef Kautzner MD, PhD","doi":"10.1016/j.mcpdig.2024.06.001","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.06.001","url":null,"abstract":"<div><h3>Objective</h3><p>To evaluate the effect of smart device-based telerehabilitation on V<span>o</span>_2peak in patients after myocardial infarction.</p></div><div><h3>Patients and Methods</h3><p>This was a pilot, single-center, randomized, cross-over study with a 3-month intervention. One month after myocardial infarction, patients had cardiopulmonary exercise testing and a 6-minute walking test (6MWT) and were randomly assigned 1:1. In the intervention group, patients received a smartwatch to track the recommended number of steps, which was individualized and derived from the 6MWT. A study nurse telemonitored adherence to the recommended number of steps a day. In the control group, 150 minutes a week of moderate-intensity physical activity was recommended. After 3 months study arms were crossed over, and study procedures were repeated after 3 months.</p></div><div><h3>Results</h3><p>Between June 1, 2019, and February 28, 2023, 64 patients were randomized, of which 61 (aged 51±10 years, 10% women) completed the study. Overall, the smart device-based telerehabilitation led to 2.31 mL/kg/min (95% CI, 1.25-3.37; <em>P</em>&lt;.001) V<span>o</span>_2peak increase compared with the control treatment. Furthermore, there was a significant effect on weight (−1.50 kg; 95% CI, −0.39 to −2.70), whereas the effect on the 6MWT distance (4.7 m; 95% CI, −11.8 to 21.1) or Kansas City Quality of Life questionnaire score (0.98; 95% CI, −1.38 to 3.35) was not significant.</p></div><div><h3>Conclusion</h3><p>Smart device-based cardiac rehabilitation may be a promising alternative for patients unable or unwilling to attend in-person cardiac rehabilitation.</p></div><div><h3>Trial Registration</h3><p><span>clinicaltrials.gov</span><svg><path></path></svg> Identifier: <span>NCT03926312</span><svg><path></path></svg></p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 3","pages":"Pages 352-360"},"PeriodicalIF":0.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000622/pdfft?md5=525707d1f0d92a2cc407d45c17140fef&pid=1-s2.0-S2949761224000622-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141595891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Future of Evidence Synthesis: Automated, Living, and Interactive Systematic Reviews and Meta-analyses","authors":"Irbaz Bin Riaz MD, MS, MBI, PhD ,&nbsp;Syed Arsalan Ahmed Naqvi MD ,&nbsp;Bashar Hasan MD ,&nbsp;Mohammad Hassan Murad MD, MPH","doi":"10.1016/j.mcpdig.2024.05.023","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.05.023","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 3","pages":"Pages 361-365"},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000580/pdfft?md5=394feae87eb958f7b2342fa783623323&pid=1-s2.0-S2949761224000580-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141605114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of 5 Prominent Large Language Models in Surgical Knowledge Evaluation: A Comparative Analysis","authors":"Adam M. Ostrovsky BS,&nbsp;Joshua R. Chen BS,&nbsp;Vishal N. Shah DO,&nbsp;Babak Abai MD","doi":"10.1016/j.mcpdig.2024.05.022","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.05.022","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 3","pages":"Pages 348-350"},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000579/pdfft?md5=4ab21ee50f30d05ffdd5242c3c0f5bb9&pid=1-s2.0-S2949761224000579-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Skin and Digital–The 2024 Narrative","authors":"Dominique du Crest MBE ,&nbsp;Monisha Madhumita MD ,&nbsp;Wendemagegn Enbiale MD, MPH, PhD ,&nbsp;Alexander Zink MD, MPH, PhD ,&nbsp;Art Papier MD ,&nbsp;Gaone Matewa BBA ,&nbsp;Harvey Castro MD, MBA ,&nbsp;Hector Perandones MD ,&nbsp;Josef De Guzman OD-OPS ,&nbsp;Misha Rosenbach MD ,&nbsp;Tu-Anh Duong MD, PhD ,&nbsp;Yu-Chuan Jack Li MD, PhD ,&nbsp;Hugues Cartier MD ,&nbsp;Benjamin Ascher MD ,&nbsp;Sebastien Garson MD ,&nbsp;Alessandra Haddad MD, PhD ,&nbsp;Daniel Z. Liu MD ,&nbsp;Diala Haykal MD ,&nbsp;Jane Yoo MD, MPP ,&nbsp;Nav Paul MBBS, MRCP ,&nbsp;Lilit Garibyan MD, PhD","doi":"10.1016/j.mcpdig.2024.05.008","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.05.008","url":null,"abstract":"<div><p>The global burden of skin diseases affects over 3 billion individuals, posing important public health challenges worldwide, with profound impacts in both high-income and low-income and middle-income countries. These challenges are exacerbated by widespread disparities in access to dermatologic care and the prevalence of misinformation. This article, derived from the Skin and Digital Summit at the International Master Course on Aging Science critically evaluates how digital technologies such as artificial intelligence, teledermatology, and large language models can bridge these access gaps. It explores practical applications and case studies demonstrating the impact of these technologies in various settings, with a particular focus on adapting solutions to meet the diverse needs of low-income and middle-income countries. In addition, the narrative highlights the ongoing conversation within the dermatologic community about the role of digital advances in health care, emphasizing that this discussion is dynamic and the one that is continuously evolving. Dermatologists play an essential role in this transition, integrating digital tools into mainstream care to complement a patient-centered, culturally sensitive approach. The article advocates for a globally coordinated digital response that not only addresses current disparities in skin health care but also promotes equitable access to digital health resources, making dermatologic care more representative of all skin types and accessible worldwide.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 3","pages":"Pages 322-330"},"PeriodicalIF":0.0,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000439/pdfft?md5=ef5fd4a5427d12cc5a17c3fd6d54330b&pid=1-s2.0-S2949761224000439-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrocardiogram Signal Analysis With a Machine Learning Model Predicts the Presence of Pulmonary Embolism With Accuracy Dependent on Embolism Burden","authors":"Waldemar E. Wysokinski MD, PhD,&nbsp;Ryan A. Meverden PA-C,&nbsp;Francisco Lopez-Jimenez MD, MBA,&nbsp;David M. Harmon MD,&nbsp;Betsy J. Medina Inojosa MD,&nbsp;Abraham Baez Suarez PhD, MS,&nbsp;Kan Liu PhD,&nbsp;Jose R. Medina Inojosa MD,&nbsp;Ana I. Casanegra MD,&nbsp;Robert D. McBane MD,&nbsp;Damon E. Houghton MD, MS","doi":"10.1016/j.mcpdig.2024.03.009","DOIUrl":"10.1016/j.mcpdig.2024.03.009","url":null,"abstract":"<div><h3>Objective</h3><p>To develop an artificial intelligence deep neural network (AI-DNN) algorithm to analyze 12-lead electrocardiogram (ECG) for detection of acute pulmonary embolism (PE) and PE categories.</p></div><div><h3>Patients and Methods</h3><p>A cohort of patients seen between January 1, 1999, and December 31, 2020, from across the Mayo Clinic Enterprise with computed tomography pulmonary angiogram (CTPA) and ECG performed ±6 hours was identified. Natural language processing algorithms were applied to radiology reports to determine the diagnosis of acute PE, acute right ventricular strain pulmonary embolism (RVSPE), saddle pulmonary embolism (SADPE), or no PE. Diagnostic performance parameters of the AI-DNN reported were area under the receiver operating characteristics curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).</p></div><div><h3>Results</h3><p>A cohort of patients with CTPA report and ECG consisted of 79,894 patients including 7423 (9.3%) with acute PE, among whom 1138 patients had RVSPE or SADPE. Artificial intelligence deep neural network predicted acute PE with a modest accuracy of AUROC of 0.69 (95% CI, 0.68-0.71), sensitivity of 63.5%, specificity of 64.7%, PPV of 15.6%, and NPV of 94.5%. The AI-DNN prediction using the same algorithm for RVSPE or SADPE was higher (AUROC, 0.84; 95% CI, 0.81-0.86) with a sensitivity of 80.8%, specificity of 64.7.8%, PPV of 3.5%, and NPV of 99.5%.</p></div><div><h3>Conclusion</h3><p>An AI-based analysis of 12-lead ECG shows modest detection power for acute PE in patients who underwent CTPA, with higher accuracy for high-risk PE. Moreover, with the high NPV, it has the clinical potential to exclude high-risk PE quickly and correctly.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 3","pages":"Pages 453-462"},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000336/pdfft?md5=37397978693133143ba8101acf52268a&pid=1-s2.0-S2949761224000336-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142050371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review","authors":"Amy Bucher PhD,&nbsp;E. Susanne Blazek PhD,&nbsp;Christopher T. Symons PhD","doi":"10.1016/j.mcpdig.2024.05.007","DOIUrl":"10.1016/j.mcpdig.2024.05.007","url":null,"abstract":"<div><p>To assess the current real-world applications of machine learning (ML) and artificial intelligence (AI) as functionality of digital behavior change interventions (DBCIs) that influence patient or consumer health behaviors. A scoping review was done across the EMBASE, PsycInfo, PsycNet, PubMed, and Web of Science databases using search terms related to ML/AI, behavioral science, and digital health to find live DBCIs using ML or AI to influence real-world health behaviors in patients or consumers. A total of 32 articles met inclusion criteria. Evidence regarding behavioral domains, target real-world behaviors, and type and purpose of ML and AI used were extracted. The types and quality of research evaluations done on the DBCIs and limitations of the research were also reviewed. Research occurred between October 9, 2023, and January 20, 2024. Twenty-three DBCIs used AI to influence real-world health behaviors. Most common domains were cardiometabolic health (n=5, 21.7%) and lifestyle interventions (n=4, 17.4%). The most common types of ML and AI used were classical ML algorithms (n=10, 43.5%), reinforcement learning (n=8, 34.8%), natural language understanding (n=8, 34.8%), and conversational AI (n=5, 21.7%). Evidence was generally positive, but had limitations such as inability to detect causation, low generalizability, or insufficient study duration to understand long-term outcomes. Despite evidence gaps related to the novelty of the technology, research supports the promise of using AI in DBCIs to manage complex input data and offer personalized, contextualized support for people changing real-world behaviors. Key opportunities are standardizing terminology and improving understanding of what ML and AI are.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 3","pages":"Pages 375-404"},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000427/pdfft?md5=6c9780a76948435fb6c91a05b2e3b023&pid=1-s2.0-S2949761224000427-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141951089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transforming Health Care With Artificial Intelligence: Redefining Medical Documentation","authors":"Archana Reddy Bongurala MD ,&nbsp;Dhaval Save MD ,&nbsp;Ankit Virmani MSc ,&nbsp;Rahul Kashyap MBBS","doi":"10.1016/j.mcpdig.2024.05.006","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.05.006","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 3","pages":"Pages 342-347"},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000415/pdfft?md5=65848adacb29206aec465218a9902c5c&pid=1-s2.0-S2949761224000415-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141429145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy and Consent in Mobile Health: Solutions for Balancing Benefits and Risks","authors":"Austin T. Gregg BS ,&nbsp;Lisa Soleymani Lehmann MD, PhD","doi":"10.1016/j.mcpdig.2024.05.005","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.05.005","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 3","pages":"Pages 331-334"},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000403/pdfft?md5=93292ffce6526ded18455eb6f74ff7ad&pid=1-s2.0-S2949761224000403-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141429146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Systematic Review of Natural Language Processing Methods and Applications in Thyroidology","authors":"Ricardo Loor-Torres MD ,&nbsp;Mayra Duran MD ,&nbsp;David Toro-Tobon MD ,&nbsp;Maria Mateo Chavez MD ,&nbsp;Oscar Ponce MD ,&nbsp;Cristian Soto Jacome MD ,&nbsp;Danny Segura Torres MD ,&nbsp;Sandra Algarin Perneth MD ,&nbsp;Victor Montori BA ,&nbsp;Elizabeth Golembiewski PhD, MPH ,&nbsp;Mariana Borras Osorio MD ,&nbsp;Jungwei W. Fan PhD ,&nbsp;Naykky Singh Ospina MD ,&nbsp;Yonghui Wu PhD ,&nbsp;Juan P. Brito MD, MS","doi":"10.1016/j.mcpdig.2024.03.007","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.03.007","url":null,"abstract":"<div><p>This study aimed to review the application of natural language processing (NLP) in thyroid-related conditions and to summarize current challenges and potential future directions. We performed a systematic search of databases for studies describing NLP applications in thyroid conditions published in English between January 1, 2012 and November 4, 2022. In addition, we used a snowballing technique to identify studies missed in the initial search or published after our search timeline until April 1, 2023. For included studies, we extracted the NLP method (eg, rule-based, machine learning, deep learning, or hybrid), NLP application (eg, identification, classification, and automation), thyroid condition (eg, thyroid cancer, thyroid nodule, and functional or autoimmune disease), data source (eg, electronic health records, health forums, medical literature databases, or genomic databases), performance metrics, and stages of development. We identified 24 eligible NLP studies focusing on thyroid-related conditions. Deep learning-based methods were the most common (38%), followed by rule-based (21%), and traditional machine learning (21%) methods. Thyroid nodules (54%) and thyroid cancer (29%) were the primary conditions under investigation. Electronic health records were the dominant data source (17/24, 71%), with imaging reports being the most frequently used (15/17, 88%). There is increasing interest in NLP applications for thyroid-related studies, mostly addressing thyroid nodules and using deep learning-based methodologies with limited external validation. However, none of the reviewed NLP applications have reached clinical practice. Several limitations, including inconsistent clinical documentation and model portability, need to be addressed to promote the evaluation and implementation of NLP applications to support patient care in thyroidology.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 270-279"},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000270/pdfft?md5=a263fb1467469ab6d8333b257365a8ec&pid=1-s2.0-S2949761224000270-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141077738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Analysis of Facial Photographs for Predicting Bicuspid Aortic Valve","authors":"Atefeh Ghorbanzadeh MD ,&nbsp;Naresh Prodduturi MS ,&nbsp;Ana I. Casanegra MD, MS ,&nbsp;Robert McBane MD ,&nbsp;Paul Wennberg MD ,&nbsp;Thom Rooke MD ,&nbsp;David Liedl RN ,&nbsp;Dennis Murphree PhD ,&nbsp;Damon E. Houghton MD, MS","doi":"10.1016/j.mcpdig.2024.05.002","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.05.002","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 3","pages":"Pages 319-321"},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000373/pdfft?md5=323ed4c1e00b694f865a70ffa47c077d&pid=1-s2.0-S2949761224000373-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}