Mayo Clinic Proceedings. Digital health最新文献

{"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}

{"title":"Reinforcing Stereotypes in Health Care Through Artificial Intelligence–Generated Images: A Call for Regulation","authors":"Hannah van Kolfschooten LLM ,&nbsp;Astrid Pilottin LLM","doi":"10.1016/j.mcpdig.2024.05.004","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.05.004","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 3","pages":"Pages 335-341"},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000397/pdfft?md5=6c14744f6113830d0aee54966003b0f0&pid=1-s2.0-S2949761224000397-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141429163","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":"Economic Perspective of the Use of Wearables in Health Care: A Systematic Review","authors":"Gioacchino D. De Sario Velasquez MD ,&nbsp;Sahar Borna MD ,&nbsp;Michael J. Maniaci MD ,&nbsp;Jordan D. Coffey MBA ,&nbsp;Clifton R. Haider PhD ,&nbsp;Bart M. Demaerschalk MSc, MD ,&nbsp;Antonio Jorge Forte MD, PhD","doi":"10.1016/j.mcpdig.2024.05.003","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.05.003","url":null,"abstract":"<div><p>The objective of this study is to explore the current state of research concerning the cost-effectiveness of wearable health technologies, excluding hearing aids, owing to extensive previous investigation. A systematic review was performed using PubMed, EMBASE/MEDLINE, Google Scholar, and Cumulated Index to Nursing and Allied Health Literature to search studies evaluating the cost-effectiveness of wearable health devices in terms of quality-adjusted life years and incremental cost-effectiveness ratio. The search was conducted on March 28, 2023, and the date of publication did not limit the search. The search yielded 10 studies eligible for inclusion. These studies, published between 2012 and 2023, spanned various locations globally. The studies used data from hypothetical cohorts, existing research, randomized controlled trials, and meta-analyses. They covered a diverse range of wearable technologies applied in different health care settings, including respiratory rate monitors, pedometers, fall-prediction devices, hospital-acquired pressure injury prevention monitors, seizure detection devices, heart rate monitors, insulin therapy sensors, and wearable cardioverter defibrillators. The time horizons in the cost-effectiveness analyses ranged from less than a year to a lifetime. The studies indicate that wearable technologies can increase quality-adjusted life years and be cost-effective and potentially cost-saving. However, the cost-effectiveness depends on various factors, such as the type of device, the health condition being addressed, the specific perspective of the health economic analysis, local cost and payment structure, and willingness-to-pay thresholds. The use of wearables in health care promises improving outcomes and resource allocation. However, more research is needed to fully understand the long-term benefits and to strengthen the evidence base for health care providers, policymakers, and patients.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 3","pages":"Pages 299-317"},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000385/pdfft?md5=dcc6804bc580088be603b0023cca6ac3&pid=1-s2.0-S2949761224000385-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424582","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":"Staff Experiences Transitioning to Digital Dermatopathology in a Tertiary Academic Medical Center: Lessons Learned From Implementation Science","authors":"Celia C. Kamath PhD ,&nbsp;Erin O. Wissler Gerdes MA ,&nbsp;Barbara A. Barry PhD ,&nbsp;Sarah A. Minteer PhD ,&nbsp;Nneka I. Comfere MD ,&nbsp;Margot S. Peters MD ,&nbsp;Carilyn N. Wieland MD ,&nbsp;Elizabeth B. Habermann PhD ,&nbsp;Jennifer L. Ridgeway PhD","doi":"10.1016/j.mcpdig.2024.05.001","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.05.001","url":null,"abstract":"<div><p>Digital pathology (DP) transforms practice by replacing traditional glass slide review with digital whole slide images and workflows. Although digitization may improve accuracy and efficiency, transitioning to digital practice requires staff to learn new skills and adopt new ways of working and collaborating. In this study, we aimed to evaluate the experiences and perceptions of individuals involved in the day-to-day work of implementing DP in a tertiary academic medical center using Normalization Process Theory, a social theory that explains the processes by which innovations are operationalized and sustained in practice. Between September 2021 and June 2022, dermatopathologists, referring clinicians, and support staff at Mayo Clinic (Minnesota, Florida, and Arizona) participated in interviews (n=22) and completed surveys (n=34) concerning the transition. Normalization Process Theory informed the selection of validated survey items (Normalization Measure Development Questionnaire) and guided qualitative analysis. Participants reported high agreement with statements related to shared understanding and potential value of DP for workflow integration and working relationships. Qualitative themes reflecting the way organization and social context enable these processes were mapped onto implementation stages and related key activities. We found that earlier processes of implementation (understanding and working out participation) were better supported than later stages (doing it and reflecting on it). Our analysis helps identify targets for further intervention to hasten and help sustain implementation, including additional support in software and technological integration, workflows and work redesign, and regular monitoring and feedback systems. The use of implementation theory, such as Normalization Process Theory, may provide useful pointers to enable other similar digital system transition efforts.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 3","pages":"Pages 289-298"},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000361/pdfft?md5=029727f4e2c849485b54c16b291dce70&pid=1-s2.0-S2949761224000361-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424583","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":"The Development and Performance of a Machine-Learning Based Mobile Platform for Visually Determining the Etiology of 5 Penile Diseases","authors":"Lao-Tzu Allan-Blitz MD, MPH ,&nbsp;Sithira Ambepitiya MD ,&nbsp;Raghavendra Tirupathi MD ,&nbsp;Jeffrey D. Klausner MD, MPH","doi":"10.1016/j.mcpdig.2024.04.006","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.04.006","url":null,"abstract":"<div><h3>Objective</h3><p>To develop a machine-learning visual classification algorithm for penile diseases in order to address disparities in access to sexual health services.</p></div><div><h3>Patients and Methods</h3><p>We developed an image data set using original and augmented images for 5 penile diseases: herpes lesions, syphilitic chancres, balanitis, penile cancer, and genital warts. We used a U-Net architecture model for semantic pixel segmentation into background or subject image, an Inception-ResNet version 2 neural architecture to classify each pixel as diseased or nondiseased, and a salience map using GradCAM++. We trained the model on a random 91% sample of the images and evaluated the model on the remaining 9%, assessing recall (or sensitivity), precision, specificity, and F1-score. As of July 1st 2022, the model has been in use via a mobile application platform; we assessed application usage between July and October 1, 2023.</p></div><div><h3>Results</h3><p>Of 239 images in the validation data set, 45 (18.8%) were of genital warts, 43 (18%) were of herpes simplex virus infection (ranging from early vesicles to ulcers), 29 (12.1%) were of penile cancer, 40 (16.7%) were of balanitis, 37 (15.5%) were of syphilitic chancres, and 45 (18.8%) were nondiseased images. The overall accuracy of the model for correctly classifying images was 0.944. There were 2640 unique submissions to the mobile platform; among a random sample (n=437), 271 (62%) were from the United States, 64 (14.6%) from Singapore, 41 (9.4%) from Canada, 40 (9.2%) from the United Kingdom, and 21 (4.8%) from Vietnam.</p></div><div><h3>Conclusion</h3><p>We report on the development of a machine-learning model for classifying 5 penile diseases, which exhibited excellent performance.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 280-288"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294976122400035X/pdfft?md5=82980c3f1cb70a53329b1e3241d7722b&pid=1-s2.0-S294976122400035X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141077698","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}