TY - JOUR
T1 - Data Harmonization, Standardization, and Collaboration for Diabetic Retinal Disease (DRD) Research
T2 - Report From the 2024 Mary Tyler Moore Vision Initiative Workshop on Data
AU - Domalpally, Amitha
AU - Fickweiler, Ward
AU - Levine, S. Robert
AU - Goetz, Kerry E.
AU - Vanderbeek, Brian L.
AU - Lee, Aaron
AU - Sundstrom, Jeffrey M.
AU - Markel, Dorene
AU - Sun, Jennifer K.
N1 - Publisher Copyright:
© 2024 The Authors.
PY - 2024/10
Y1 - 2024/10
N2 - 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.
AB - 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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U2 - 10.1167/tvst.13.10.4
DO - 10.1167/tvst.13.10.4
M3 - Article
C2 - 39361314
AN - SCOPUS:85205527703
SN - 2164-2591
VL - 13
JO - Translational Vision Science and Technology
JF - Translational Vision Science and Technology
IS - 10
M1 - 4
ER -