Mayo Clinic Proceedings. Digital health最新文献

{"title":"Reply to: Artificial Intelligence Face Swapping: Promise and Peril in Health Care","authors":"Jen-Cheng Hou BSc, MSc, PhD, Aileen McGonigal MD, PhD, Hsiang-Yu Yu MD, PhD","doi":"10.1016/j.mcpdig.2024.01.010","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.01.010","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 1","pages":"Page 163"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000130/pdfft?md5=65f75efa4b12bbac8ca4d180a36c9d18&pid=1-s2.0-S2949761224000130-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139993282","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":"Artificial Intelligence, Medical Knowledge, and Empowering Patients","authors":"Allen O. Eghrari MD, MPH","doi":"10.1016/j.mcpdig.2024.01.008","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.01.008","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 1","pages":"Pages 160-162"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000099/pdfft?md5=b6bae2bc4e228b8a516229c4e659c2d7&pid=1-s2.0-S2949761224000099-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139993283","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":"Exercise Testing and Artificial Intelligence as Allies in Improving the Detection and Diagnosis of Long QT Syndrome","authors":"Audrey Harvey PhD Candidate, Daniel Curnier PhD, Maxime Caru PhD, PhD","doi":"10.1016/j.mcpdig.2024.01.011","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.01.011","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 1","pages":"Pages 175-176"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000154/pdfft?md5=7c396341fdede66f6a5c8c2652205ad7&pid=1-s2.0-S2949761224000154-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140113244","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":"Accuracy of Smartwatch Pulse Oximetry Measurements in Hospitalized Patients With Coronavirus Disease 2019","authors":"Kevin Rajakariar MBBS ,&nbsp;Paul Buntine MBBS ,&nbsp;Andrew Ghaly MBBS ,&nbsp;Zheng Cheng Zhu MBBS ,&nbsp;Vihangi Abeygunawardana MD ,&nbsp;Sarah Visakhamoorthy MBBS ,&nbsp;Patrick J. Owen PhD ,&nbsp;Shaun Tham MD ,&nbsp;Liam Hackett MPH ,&nbsp;Louise Roberts PhD ,&nbsp;Jithin K. Sajeev MBBS, PhD ,&nbsp;Nicholas Jones MBBS ,&nbsp;Andrew W. Teh MBBS, PhD","doi":"10.1016/j.mcpdig.2024.02.001","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.02.001","url":null,"abstract":"<div><h3>Objective</h3><p>To assess the ability of 2 commercially available smartwatches to accurately detect clinically significant hypoxia in patients hospitalized with coronavirus-19 (COVID-19).</p></div><div><h3>Patients and Methods</h3><p>A prospective multicenter validation study was performed from November 1, 2021, to August 31, 2022, assessing the Apple Watch Series 7 and Withings ScanWatch inbuilt pulse oximetry, against simultaneous ward-based oximetry as the reference standard. Patients hospitalized with active COVID-19 infection not requiring intensive care admission were recruited.</p></div><div><h3>Results</h3><p>A total of 750 smartwatch pulse oximetry measurements and 400 ward oximetry readings were successfully obtained from 200 patients (male 54%, age 66±18 years). For the detection of clinically significant hypoxia, the Apple Watch had a sensitivity and specificity of 34.8% and 97.5%, respectively with a positive predictive value of 78.1% and negative predictive value of 85.6%. The Withings ScanWatch had a sensitivity and specificity of 68.5% and 80.8%, respectively with a positive predictive value of 44.7% and negative predictive value of 91.9%. The overall accuracy was 84.9% for the Apple Watch and 78.5% for the Withings ScanWatch. The Spearman rank correlation coefficients reported a moderate correlation to ward-based photoplethysmography (Apple: r_s=0.61; Withings: r_s=0.51, both <em>P</em>&lt;.01).</p></div><div><h3>Conclusion</h3><p>Although smartwatches are able to provide SpO₂ readings, their overall accuracy may not be sufficient to replace the standard photoplethysmography technology in detecting hypoxia in patients with COVID-19.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 1","pages":"Pages 152-158"},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000105/pdfft?md5=dbf3bf07a6737561ec1ad6f4adb7fdcd&pid=1-s2.0-S2949761224000105-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139985368","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":"Leveraging the Metaverse for Enhanced Longevity as a Component of Health 4.0","authors":"Srinivasan S. Pillay MD ,&nbsp;Patrick Candela BA ,&nbsp;Ivana T. Croghan PhD ,&nbsp;Ryan T. Hurt MD, PhD ,&nbsp;Sara L. Bonnes MD, MS ,&nbsp;Ravindra Ganesh MBBS, MD ,&nbsp;Brent A. Bauer MD","doi":"10.1016/j.mcpdig.2024.01.007","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.01.007","url":null,"abstract":"<div><p>In this review, we describe evidence that supports building a metaverse to promote healthy longevity. We propose that the metaverse offers several physical advantages (architecture, music, and nature), social (accessibility, affordability, community-building, and relief of social anxiety), and therapeutic (immersive, anti-inflammatory, and adjunctive use in complementary and integrative medicine). Lifelogging by patients may help clinicians personalize interventions by matching data to therapeutic outcomes. Although the metaverse cannot entirely replace our current model of care, a strategic approach will ensure adequate resource allocation and value assessment. In a collaborative effort between Reulay, Inc and Mayo Clinic, we are building a platform for the delivery of personalized and idiographic interventions to promote healthy longevity. To this end, we are using specific science-informed art design to reduce stress and anxiety for patients, with the progressive addition of integrated care elements that connect to this framework and connect treatment response to biomarkers that are relevant to healthy longevity. This review is a commentary on the thought process behind this effort.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 1","pages":"Pages 139-151"},"PeriodicalIF":0.0,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000087/pdfft?md5=fe65aacfc7a505e1acf93b8a7a7b844e&pid=1-s2.0-S2949761224000087-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139944992","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":"An Automated Approach for Diagnosing Allergic Contact Dermatitis Using Deep Learning to Support Democratization of Patch Testing","authors":"Matthew R. Hall MD ,&nbsp;Alexander D. Weston PhD ,&nbsp;Mikolaj A. Wieczorek BA ,&nbsp;Misty M. Hobbs MD ,&nbsp;Maria A. Caruso BA ,&nbsp;Habeeba Siddiqui BA ,&nbsp;Laura M. Pacheco-Spann MS ,&nbsp;Johanny L. Lopez-Dominguez MD ,&nbsp;Coralle Escoda-Diaz BA ,&nbsp;Rickey E. Carter PhD ,&nbsp;Charles J. Bruce MB, ChB","doi":"10.1016/j.mcpdig.2024.01.006","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.01.006","url":null,"abstract":"<div><h3>Objective</h3><p>To develop a deep learning algorithm for the analysis of patch testing.</p></div><div><h3>Patients and Methods</h3><p>A retrospective case series between January 1, 2010, and December 31, 2020, was constructed to develop a deep learning model for the classification of patch test results from photographs. The performance of human expert readers reviewing the same photographs blinded to the original clinical physical examination findings was measured to benchmark model performance.</p></div><div><h3>Results</h3><p>Model performance on the independent test set (n=5070 test site locations from 37 patients) achieved an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86-0.91) and an F1 score of 37.1. The optimal cutoff had a sensitivity of 70.1% (136/194; 95% CI, 63.1%-76.5%) and a specificity of 91.7% (4472/4876; 95% CI, 90.9%-92.5%).</p></div><div><h3>Conclusion</h3><p>We demonstrated proof-of-concept utility for detecting allergic contact dermatitis using an automated deep learning approach.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 1","pages":"Pages 131-138"},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000075/pdfft?md5=ec00a6cd5a159970ea4a21e054923ea6&pid=1-s2.0-S2949761224000075-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139942544","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 Transformative Future for Health Care: On the First Year of Mayo Clinic Proceedings: Digital Health","authors":"Gianrico Farrugia MD","doi":"10.1016/j.mcpdig.2024.02.002","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.02.002","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 1","pages":"Pages 129-130"},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000117/pdfft?md5=cbd982a82d4ca62307430904aba1007a&pid=1-s2.0-S2949761224000117-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139748980","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":"Differences Between Patient and Clinician-Taken Images: Implications for Virtual Care of Skin Conditions","authors":"Rajeev V. Rikhye PhD ,&nbsp;Grace Eunhae Hong BA ,&nbsp;Preeti Singh MS ,&nbsp;Margaret Ann Smith MBA ,&nbsp;Aaron Loh MS ,&nbsp;Vijaytha Muralidharan MD ,&nbsp;Doris Wong BS ,&nbsp;Rory Sayres PhD ,&nbsp;Michelle Phung MS ,&nbsp;Nicolas Betancourt MD ,&nbsp;Bradley Fong BS ,&nbsp;Rachna Sahasrabudhe BA ,&nbsp;Khoban Nasim BS ,&nbsp;Alec Eschholz BA ,&nbsp;Yossi Matias PhD ,&nbsp;Greg S. Corrado PhD ,&nbsp;Katherine Chou MS ,&nbsp;Dale R. Webster PhD ,&nbsp;Peggy Bui MD, MBA ,&nbsp;Yuan Liu PhD ,&nbsp;Steven Lin MD","doi":"10.1016/j.mcpdig.2024.01.005","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.01.005","url":null,"abstract":"<div><h3>Objective</h3><p>To understand and highlight the differences in clinical, demographic, and image quality characteristics between patient-taken (PAT) and clinic-taken (CLIN) photographs of skin conditions.</p></div><div><h3>Patients and Methods</h3><p>This retrospective study applied logistic regression to data from 2500 deidentified cases in Stanford Health Care’s eConsult system, from November 2015 to January 2021. Cases with undiagnosable or multiple conditions or cases with both patient and clinician image sources were excluded, leaving 628 PAT cases and 1719 CLIN cases. Demographic characteristic factors, such as age and sex were self-reported, whereas anatomic location, estimated skin type, clinical signs and symptoms, condition duration, and condition frequency were summarized from patient health records. Image quality variables such as blur, lighting issues and whether the image contained skin, hair, or nails were estimated through a deep learning model.</p></div><div><h3>Results</h3><p>Factors that were positively associated with CLIN photographs, post-2020 were as follows: age 60 years or older, darker skin types (eFST V/VI), and presence of skin growths. By contrast, factors that were positively associated with PAT photographs include conditions appearing intermittently, cases with blurry photographs, photographs with substantial nonskin (or nail/hair) regions and cases with more than 3 photographs. Within the PAT cohort, older age was associated with blurry photographs.</p></div><div><h3>Conclusion</h3><p>There are various demographic, clinical, and image quality characteristic differences between PAT and CLIN photographs of skin concerns. The demographic characteristic differences present important considerations for improving digital literacy or access, whereas the image quality differences point to the need for improved patient education and better image capture workflows, particularly among elderly patients.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 1","pages":"Pages 107-118"},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000063/pdfft?md5=b6821d4312bb7e3ec9c3c66208aec937&pid=1-s2.0-S2949761224000063-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139738259","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":"Untapped Potential of Artificial Intelligence for Analysis of Epileptic Seizure Videos: A Clinician’s Expectation","authors":"Naotaka Usui MD, PhD","doi":"10.1016/j.mcpdig.2024.01.004","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.01.004","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 1","pages":"Pages 104-106"},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000051/pdfft?md5=c551603f1e01d547a79eec0bbf642cfc&pid=1-s2.0-S2949761224000051-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139738298","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":"Beyond Atrial Fibrillation: Machine Learning Algorithm Predicts Stroke in Adult Patients With Congenital Heart Disease","authors":"Anca Chiriac MD, PhD ,&nbsp;Che Ngufor PhD ,&nbsp;Holly K. van Houten BA ,&nbsp;Raphael Mwangi MS ,&nbsp;Malini Madhavan MBBS ,&nbsp;Peter A. Noseworthy MD ,&nbsp;Samuel J. Asirvatham MD ,&nbsp;Sabrina D. Phillips MD ,&nbsp;Christopher J. McLeod MB ChB, PhD","doi":"10.1016/j.mcpdig.2023.12.002","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2023.12.002","url":null,"abstract":"<div><h3>Objective</h3><p>To develop and validate a robust risk prediction model for stroke and systemic embolism (SSE) in adult patients with congenital heart disease (ACHD), using artificial intelligence.</p></div><div><h3>Patients and Methods</h3><p>Deidentified insurance claims from the Optum Labs Data Warehouse, including enrollment records and medical and pharmacy claims for commercial and Medicare Advantage enrollees, were used to identify 49,276 patients with ACHD, followed between January 1, 2009, and December 31, 2014. The group was randomly divided into development (70%) and validation (30%) cohorts. The development cohort was used to train 2 machine learning (ML) algorithms, regularized Cox regression (RegCox), and extreme gradient boosting (XGBoost) to predict SSE at 1, 2, and 5 years. The Shapley additive explanations (SHAP) model was used to identify the variables particularly driving the SSE risk.</p></div><div><h3>Results</h3><p>Within this large and diverse cohort of patients with ACHD (mean age, 59 ± 19 years; 25,390 (51.5%) female, 35,766 [77.6%]) white), 1756 (3.6%) patients experienced SSE during follow-up. In the Validation cohort, CHA₂DS₂-VASC had an area under the receiver operating characteristics curve (AUC) of 0.66 for predicting SSE at 1-,2, and 5-years. RegCox had the best predictive performance, with AUCs of 0.82,.81, and.80 at 1-, 2, and 5-years. XGBoost had AUCs of 0.81, 0.80, and 0.79 respectively. Atrial septal defect (ASD) emerged as an important predictor for SSE uncovered by the unbiased ML algorithms. A new clinical risk score, the CHA₂DS₂-VASC-ASD₂ score, provides improved SSE prediction in ACHD. Yet, the ML models still outperformed this.</p></div><div><h3>Conclusion</h3><p>ML models significantly outperformed the clinical risk scores in patients with ACHD.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 1","pages":"Pages 92-103"},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000026/pdfft?md5=c34fed3977be03552486d0740a93fe5f&pid=1-s2.0-S2949761224000026-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139738326","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}