Data Harmonization, Standardization, and Collaboration for Diabetic Retinal Disease (DRD) Research: Report From the 2024 Mary Tyler Moore Vision Initiative Workshop on Data.

IF 2.6 3区 医学 Q2 OPHTHALMOLOGY Translational Vision Science & Technology Pub Date : 2024-10-01 DOI:10.1167/tvst.13.10.4
Amitha Domalpally, Ward Fickweiler, S Robert Levine, Kerry E Goetz, Brian L VanderBeek, Aaron Lee, Jeffrey M Sundstrom, Dorene Markel, Jennifer K Sun
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Abstract

The 2024 Mary Tyler Moore Vision Initiative (MTM Vision) Workshop on Data convened to discuss best practices and specific considerations for building a comprehensive, shareable MTM Vision data lake. The workshop aimed to accelerate the development of new indications, therapies, and regulatory pathways for diabetic retinal disease (DRD) by standardizing and harmonizing clinical data and ocular 'omics analyses. Standardization of data collection, the use of common data elements, and data interoperability were emphasized, alongside federated learning approaches to promote data sharing and collaboration while maintaining data privacy and security. The integration of molecular data with other multimodal data types was recognized as a promising strategy for leveraging machine learning and AI approaches to advancing therapeutics development and improving treatment outcomes for DRD patients. Partnerships with entities such as the National Eye Institute, part of the National Institutes of Health, foundations, and industry were deemed vital for the successful implementation of these initiatives.

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糖尿病视网膜疾病(DRD)研究的数据协调、标准化与合作:2024 玛丽-泰勒-摩尔视觉计划数据研讨会报告》。
2024 玛丽-泰勒-摩尔视觉计划(MTM Vision)数据研讨会召开,讨论建立全面、可共享的 MTM Vision 数据湖的最佳实践和具体注意事项。该研讨会旨在通过标准化和统一临床数据和眼部 "omics "分析,加快糖尿病视网膜疾病(DRD)新适应症、新疗法和新监管途径的开发。会议强调了数据收集的标准化、通用数据元素的使用和数据的互操作性,以及在维护数据隐私和安全的同时促进数据共享和协作的联合学习方法。分子数据与其他多模态数据类型的整合被认为是利用机器学习和人工智能方法推进治疗方法开发和改善 DRD 患者治疗效果的一项大有可为的战略。与美国国立卫生研究院下属的国家眼科研究所、基金会和行业等实体的合作被认为是成功实施这些计划的关键。
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来源期刊
Translational Vision Science & Technology
Translational Vision Science & Technology Engineering-Biomedical Engineering
CiteScore
5.70
自引率
3.30%
发文量
346
审稿时长
25 weeks
期刊介绍: Translational Vision Science & Technology (TVST), an official journal of the Association for Research in Vision and Ophthalmology (ARVO), an international organization whose purpose is to advance research worldwide into understanding the visual system and preventing, treating and curing its disorders, is an online, open access, peer-reviewed journal emphasizing multidisciplinary research that bridges the gap between basic research and clinical care. A highly qualified and diverse group of Associate Editors and Editorial Board Members is led by Editor-in-Chief Marco Zarbin, MD, PhD, FARVO. The journal covers a broad spectrum of work, including but not limited to: Applications of stem cell technology for regenerative medicine, Development of new animal models of human diseases, Tissue bioengineering, Chemical engineering to improve virus-based gene delivery, Nanotechnology for drug delivery, Design and synthesis of artificial extracellular matrices, Development of a true microsurgical operating environment, Refining data analysis algorithms to improve in vivo imaging technology, Results of Phase 1 clinical trials, Reverse translational ("bedside to bench") research. TVST seeks manuscripts from scientists and clinicians with diverse backgrounds ranging from basic chemistry to ophthalmic surgery that will advance or change the way we understand and/or treat vision-threatening diseases. TVST encourages the use of color, multimedia, hyperlinks, program code and other digital enhancements.
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