{"title":"Digital twins and artificial intelligence in metabolic disease research.","authors":"Clara Mosquera-Lopez, Peter G Jacobs","doi":"10.1016/j.tem.2024.04.019","DOIUrl":null,"url":null,"abstract":"<p><p>Digital twin technology is emerging as a transformative paradigm for personalized medicine in the management of chronic conditions. In this article, we explore the concept and key characteristics of a digital twin and its applications in chronic non-communicable metabolic disease management, with a focus on diabetes case studies. We cover various types of digital twin models, including mechanistic models based on ODEs, data-driven ML algorithms, and hybrid modeling strategies that combine the strengths of both approaches. We present successful case studies demonstrating the potential of digital twins in improving glucose outcomes for individuals with T1D and T2D, and discuss the benefits and challenges of translating digital twin research applications to clinical practice.</p>","PeriodicalId":54415,"journal":{"name":"Trends in Endocrinology and Metabolism","volume":" ","pages":"549-557"},"PeriodicalIF":11.4000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in Endocrinology and Metabolism","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.tem.2024.04.019","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/13 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
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Abstract
Digital twin technology is emerging as a transformative paradigm for personalized medicine in the management of chronic conditions. In this article, we explore the concept and key characteristics of a digital twin and its applications in chronic non-communicable metabolic disease management, with a focus on diabetes case studies. We cover various types of digital twin models, including mechanistic models based on ODEs, data-driven ML algorithms, and hybrid modeling strategies that combine the strengths of both approaches. We present successful case studies demonstrating the potential of digital twins in improving glucose outcomes for individuals with T1D and T2D, and discuss the benefits and challenges of translating digital twin research applications to clinical practice.
期刊介绍：
Trends in Endocrinology and Metabolism (TEM) stands as a premier Reviews journal in the realms of metabolism and endocrinology. Our commitment is reflected in the publication of refined, concise, and highly impactful articles that delve into cutting-edge topics, encompassing basic, translational, and clinical aspects. From state-of-the-art treatments for endocrine diseases to groundbreaking developments in molecular biology, TEM provides comprehensive coverage.
Explore recent advancements in diabetes, endocrine diseases, obesity, neuroendocrinology, immunometabolism, molecular and cellular biology, and a myriad of other areas through our journal.
TEM serves as an invaluable resource for researchers, clinicians, lecturers, teachers, and students. Each monthly issue is anchored by Reviews and Opinion articles, with Reviews meticulously chronicling recent and significant developments, often contributed by leading researchers in specific fields. Opinion articles foster debate and hypotheses. Our shorter pieces include Science & Society, shedding light on issues at the intersection of science, society, and policy; Spotlights, which focus on exciting recent developments in the literature, and single-point hypotheses as Forum articles. We wholeheartedly welcome and encourage responses to previously published TEM content in the form of Letters.
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