Mayo Clinic Proceedings. Digital health最新文献

{"title":"Barriers to Radiomics Adoption for Urological Cancer Diagnosis in Low-Income and Middle-Income Countries: A Perspective from Pakistan","authors":"Awais Ayub MBBS, Hanan Mudassar MBBS, Maida Rizwan MBBS","doi":"10.1016/j.mcpdig.2025.100262","DOIUrl":"10.1016/j.mcpdig.2025.100262","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100262"},"PeriodicalIF":0.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In Reply: Barriers to Radiomics Adoption for Urological Cancer Diagnosis in Low-Income and Middle-Income Countries: A Perspective from Pakistan","authors":"Isaiah Z. Yao, Min Dong, William Y.K. Hwang MBBS, FRCP, FAMS, MBA","doi":"10.1016/j.mcpdig.2025.100263","DOIUrl":"10.1016/j.mcpdig.2025.100263","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100263"},"PeriodicalIF":0.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Increasing Retention in a Large-Scale Decentralized Clinical Trial: Learnings From the COVID-RED Trial","authors":"Laura C. Zwiers MPhil ,&nbsp;Duco Veen PhD ,&nbsp;Marianna Mitratza PhD ,&nbsp;Timo B. Brakenhoff PhD ,&nbsp;Brianna M. Goodale PhD ,&nbsp;Paul Klaver MSc ,&nbsp;Kay Y. Hage MSc ,&nbsp;Marcel van Willigen PhD ,&nbsp;George S. Downward PhD ,&nbsp;Peter Lugtig PhD ,&nbsp;Leendert van Maanen PhD ,&nbsp;Stefan Van der Stigchel PhD ,&nbsp;Peter van der Heijden PhD ,&nbsp;Maureen Cronin PhD ,&nbsp;Diederick E. Grobbee PhD ,&nbsp;COVID-RED Consortium","doi":"10.1016/j.mcpdig.2025.100264","DOIUrl":"10.1016/j.mcpdig.2025.100264","url":null,"abstract":"<div><h3>Objective</h3><div>To present retention strategies implemented in the coronavirus disease 2019 (COVID-19) rapid early detection trial, a decentralized trial investigating the use of a wearable device for severe acute respiratory syndrome coronavirus 2 detection, and to provide insights into study retention and investigate determinants of discontinuation.</div></div><div><h3>Patients and Methods</h3><div>The COVID-2019 rapid early detection trial collected data from 17,825 participants from February 22, 2021 to November 18, 2021. Participants wore a wearable device overnight and synchronized it with a mobile application on waking. Retention strategies included common and personalized activities. Multivariable logistic regression was used to identify participants at high risk of discontinuation after 6 months in the trial. Results were combined with insights from behavioral theory to target participants with additional telephone calls.</div></div><div><h3>Results</h3><div>Total of 14,326 (80.4%) participants remained in the trial after 6 months and 12,208 (68.5%) until the end of the trial. Multivariable logistic regression identified age, employment situation, living situation, and COVID-19 vaccination status as predictors of discontinuation. Subgroups at high risk of discontinuation were identified, and behavioral assessments indicated that the subgroup of vaccinated pensioners would receive additional telephone calls. Their dropout rate was 11.4% after telephone calls.</div></div><div><h3>Conclusion</h3><div>This study describes how innovative and targeted data-driven retention strategies can be applied in a large decentralized clinical trial and presents the implemented retention strategies and discontinuation rates. Results can serve as a starting point for designing retention strategies in future decentralized trials.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100264"},"PeriodicalIF":0.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reimagining Pediatrics in a World of Artificial Intelligence: Will We Be Empowered or Imperiled?","authors":"Shelby Kutty MD, PhD, MHCM ,&nbsp;Yiu-fai Cheung MD ,&nbsp;Sowmya Viswanathan MD ,&nbsp;David A. Danford MD, MPH","doi":"10.1016/j.mcpdig.2025.100258","DOIUrl":"10.1016/j.mcpdig.2025.100258","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100258"},"PeriodicalIF":0.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Byline or Botline? The Dilemma of Artificial Intelligence in Medical Scholarship","authors":"James Connor BSc, MB BCh, BAO","doi":"10.1016/j.mcpdig.2025.100259","DOIUrl":"10.1016/j.mcpdig.2025.100259","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100259"},"PeriodicalIF":0.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Standardized Temporal Segmentation Framework and Annotation Resource Library in Robotic Surgery","authors":"Busisiwe Mlambo MD ,&nbsp;Mallory Shields PhD ,&nbsp;Simon Bach MD ,&nbsp;Armin Bauer PhD ,&nbsp;Andrew Hung MD ,&nbsp;Omar Yusef Kudsi MD ,&nbsp;Felix Neis MD ,&nbsp;John Lazar MD ,&nbsp;Daniel Oh MD ,&nbsp;Robert Perez MD ,&nbsp;Seth Rosen MD ,&nbsp;Naeem Soomro MD ,&nbsp;Michael Stany MD ,&nbsp;Mark Tousignant MD ,&nbsp;Christian Wagner MD ,&nbsp;Ken Whaler MS ,&nbsp;Lilia Purvis MS ,&nbsp;Benjamin Mueller BS ,&nbsp;Sadia Yousaf MD ,&nbsp;Casey Troxler BS ,&nbsp;Anthony Jarc PhD","doi":"10.1016/j.mcpdig.2025.100257","DOIUrl":"10.1016/j.mcpdig.2025.100257","url":null,"abstract":"<div><h3>Objective</h3><div>To develop and share the first clinical temporal annotation guide library for 10 robotic procedures accompanied with a standardized ontology framework for surgical video annotation.</div></div><div><h3>Patients and Methods</h3><div>A standardized temporal annotation framework of surgical videos paired with consistent, procedure-specific annotation guides is critical to enable comparisons of surgical insights and facilitate large-scale insights for exceptional surgical practice. Existing ontologies and guidance not only provide foundational frameworks but also provide limited scalability in clinical settings. Building on these, we developed a temporal annotation framework with nested surgical phases, steps, tasks, and subtasks. Procedure-specific annotation resource guides consistent with this framework that define each surgical segment with formulaic start and stop parameters and surgical objectives were iteratively created across 7 years (January 1, 2018, to January 1, 2025) through global research collaborations with surgeon researchers and industry scientists.</div></div><div><h3>Results</h3><div>We provide the first resource library of annotation guides for 10 common robotic procedures consistent with our proposed temporal annotation framework, enabling consistent annotations for clinicians and large-scale data comparisons with computer-readable examples. These have been used in over 13,000 annotated surgical cases globally, demonstrating reproducibility and broad applicability.</div></div><div><h3>Conclusion</h3><div>This resource library and accompanying ontology framework provide critical structure for standardized temporal segmentation in robotic surgery. This framework has been applied globally in private studies examining surgical objective performance metrics, surgical education, workflow characterization, outcome prediction, algorithms for surgical activity recognition, and more. Adoption of these resources will unify clinical, academic, and industry efforts, ultimately catalyzing transformational advancements in surgical practice.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100257"},"PeriodicalIF":0.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How is Engagement Defined Across Health Care Services and Technology Companies? A Systematic Review","authors":"Sanjay Basu MD, PhD ,&nbsp;Ariela Simerman BA ,&nbsp;Ari Hoffman MD","doi":"10.1016/j.mcpdig.2025.100256","DOIUrl":"10.1016/j.mcpdig.2025.100256","url":null,"abstract":"<div><h3>Objective</h3><div>To systematically examine how digital health startups define and operationalize engagement in the post- coronavirus disease environment (2020-2025).</div></div><div><h3>Patients and Methods</h3><div>Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines adapted for web-based literature, we systematically reviewed publicly available information from digital health startups founded or significantly operating between 2020-2025. We extracted engagement definitions from company websites, white papers, blog posts, and press releases. Definitions were coded by type (explicit, implicit, or nondefinition) and dimensional focus (behavioral, cognitive, affective, and social). Inter-rater reliability was assessed using Cohen’s κ (κ=0.82). We conducted this systematic review from April 20, 2025, to May 21, 2025.</div></div><div><h3>Results</h3><div>We analyzed 64 engagement definitions from 30 digital health startups. Only 18.8% (n=12) were explicit definitions with clear measurement criteria, whereas 45.3% (n=29) were implicit definitions and 35.9% (n=23) were nondefinitions that mentioned engagement without defining it. The behavioral dimension dominated (64.1%, n=41), followed by social (28.1%, n=18), cognitive (21.9%, n=14), and affective dimensions (17.2%, n=11). Statistical analysis revealed significant associations between definition type and dimensional focus (<em>P</em>&lt;.05). Based on our findings, we developed a taxonomy of engagement definitions and a 5-level engagement definition maturity model.</div></div><div><h3>Conclusion</h3><div>Digital health startups predominantly use implicit or undefined engagement concepts with a strong behavioral focus. The proposed taxonomy and maturity model provide frameworks for standardizing engagement definitions across the digital health ecosystem, potentially improving measurement consistency, facilitating more meaningful comparisons between solutions, and establishing a baseline for evaluating effectiveness.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100256"},"PeriodicalIF":0.0,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"iCardio: The Brazilian Population-Based Real-World Data Platform for Cardiovascular Disease","authors":"Miriam Allein Zago Marcolino PhD ,&nbsp;Ana Paula Beck da Silva Etges PhD ,&nbsp;Luciana Rodrigues de Lara MBA ,&nbsp;Nayê Balzan Schneider Msc ,&nbsp;Yohan Casiraghi MD ,&nbsp;Wanderson Maia Da Silva MD ,&nbsp;Carisi Anne Polanczyk ScD","doi":"10.1016/j.mcpdig.2025.100255","DOIUrl":"10.1016/j.mcpdig.2025.100255","url":null,"abstract":"<div><div>Technological advances that contribute to improving organizations and systems’ capability to manage care services and pathways are impactful in improving efficiency and reducing waste in health care. This narrative paper presents the implementation of iCardio, a dashboard of population real-world data-based analytical online open-access solution for the cardiovascular field in Brazil. The platform was developed using hospitalization data from patients who underwent cardiovascular operation or interventional procedures, identified by procedure codes reimbursed by the public health system. Patient-level data from hospital and mortality systems were provided by the Brazilian Ministry of Health, cleaned, and organized into individual-level and hospitalization-level datasets to enable parameter calculation. A web-based solution was developed to provide user-friendly, interactive access to 17 indicators relevant to evaluating cardiovascular service efficiency, quality, and equity. Data from 291,490 patients with 317,338 index hospitalizations and 375,809 procedures (172,874 of cardiovascular operations and 202,935 of interventional cardiology) performed in 558 health care centers in Brazil compose the dataset behind the platform. The platform offers 4 analytical views: “patients,’ profile,’’ “by location,’’ “procedure rates,’’ and “detailed exploration,’’ displaying data by year (2019-2020) with multiple stratification options (eg, patient characteristics, procedures, health care centers, and geography). The iCardio is an online open-access platform based on real-world data that provides ready-to-use information about cardiovascular care in Brazil, which can be used as a transformative tool to sustain data-driven health policies and research in the cardiovascular field in Brazil.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100255"},"PeriodicalIF":0.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Health Rights, Adult Systems: Are We Leaving Adolescents Behind?","authors":"Louisa Peralta PhD ,&nbsp;Cristyn Davies PhD ,&nbsp;Kellie Burns PhD ,&nbsp;Leena Paakkari PhD","doi":"10.1016/j.mcpdig.2025.100254","DOIUrl":"10.1016/j.mcpdig.2025.100254","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100254"},"PeriodicalIF":0.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144813863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Applications in Clinical Cancer Detection: A Review of Implementation Challenges and Solutions.","authors":"Isaiah Z Yao, Min Dong, William Y K Hwang","doi":"10.1016/j.mcpdig.2025.100253","DOIUrl":"10.1016/j.mcpdig.2025.100253","url":null,"abstract":"<p><p>Deep learning (DL) has revolutionized cancer detection accuracy, speed, and accessibility. Leveraging sophisticated algorithms, DL has demonstrated transformative potential across diverse applications, including imaging-based diagnostics and genomic analysis, ultimately leading to better detection, improved patient treatment outcomes, and decreased overall mortality rates. Despite its promise, integrating DL into clinical practice presents substantial challenges, including limitations in data quality and standardization, as well as ethical and regulatory concerns, and the need for model interpretability and transparency. This review provides a comprehensive analysis of recent research (2018-2024) retrieved from PubMed and IEEE Xplore databases, encompassing 1304 studies from PubMed and 115 from IEEE, to highlight the current applications, opportunities, and challenges of DL in oncology. Additionally, this paper explores emerging solutions, including federated learning, explainable artificial intelligence, and synthetic data generation, to address these barriers. The review also emphasizes the importance of interdisciplinary collaboration, the integration of next-generation artificial intelligence techniques, and the adoption of multimodal data approaches to improve diagnostic precision and support personalized cancer treatment. By systematically analyzing key developments and challenges, this review aims to guide future research and DL technologies in oncology, promoting equitable and impactful advancements in cancer care.</p>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"100253"},"PeriodicalIF":0.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12351333/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144877120","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}