Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025
Initiation of a global consortium to study the progression of age-related macular degeneration: RIMR AMD consortium report # 1.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Sadda Srinivas R, Wykoff Charles C, Chowers Itay, Korobelnik Jean-Francois, Narayana Raja, Pappuru Rajeev R, Holz Frank G, Guymer Robyn, Cheung Chui Ming Gemmy, Boyer David S, Ip Michael, Niessen Heiko G, Holenkamp Nancy, Durbin Mary, Magazzeni Stephanie, Cairns Anne-Marie, Ciller Carlos, Jovic Natasa, Blair Joseph, De Zanet Sandro, Lee Aaron Y
Аннотация
PURPOSE: To describe the design and organizational structure of a global collaborative consortium aimed at aggregating longitudinal multimodal imaging data to better understand the progression of age-related macular degeneration (AMD) and facilitate therapeutic development.
METHODS: The Ryan Initiative for Macular Research (RIMR) AMD Consortium was established as a nonprofit organization, bringing together academic institutions, biopharmaceutical companies, and imaging technology providers. The consortium collects, de-identifies, and harmonizes longitudinal optical coherence tomography (OCT) data, as well as associated clinical metadata, from multiple international clinical centers using a cloud-based infrastructure. Imaging data is converted and stored in DICOM format, and associated clinical data is mapped to the OMOP Common Data Model. All analyses are conducted within a secure cloud environment, supporting both built-in and member-contributed artificial intelligence (AI) tools.
RESULTS: As of the time of reporting, the Consortium has ingested over 100,000 OCT volumes from more than 5,000 subjects across 7 global cohorts spanning 4 continents and 3 major OCT platforms. Based on information provided by the data providers, the dataset encompasses a wide range of AMD stages, from normal aging to late-stage neovascular or atrophic AMD, with longitudinal follow-up extending beyond 15 years for some subjects. A data harmonization pipeline has been established to convert all ingested OCT data to the DICOM standard and is thus ready for automated analysis to gain disease-related insights.
CONCLUSIONS: The RIMR AMD Consortium represents a novel model for global collaboration in AMD research, enabling the pooling and analysis of heterogeneous imaging data while addressing privacy, regulatory, and interoperability challenges. This framework may serve as a model for similar initiatives in other ocular diseases.
METHODS: The Ryan Initiative for Macular Research (RIMR) AMD Consortium was established as a nonprofit organization, bringing together academic institutions, biopharmaceutical companies, and imaging technology providers. The consortium collects, de-identifies, and harmonizes longitudinal optical coherence tomography (OCT) data, as well as associated clinical metadata, from multiple international clinical centers using a cloud-based infrastructure. Imaging data is converted and stored in DICOM format, and associated clinical data is mapped to the OMOP Common Data Model. All analyses are conducted within a secure cloud environment, supporting both built-in and member-contributed artificial intelligence (AI) tools.
RESULTS: As of the time of reporting, the Consortium has ingested over 100,000 OCT volumes from more than 5,000 subjects across 7 global cohorts spanning 4 continents and 3 major OCT platforms. Based on information provided by the data providers, the dataset encompasses a wide range of AMD stages, from normal aging to late-stage neovascular or atrophic AMD, with longitudinal follow-up extending beyond 15 years for some subjects. A data harmonization pipeline has been established to convert all ingested OCT data to the DICOM standard and is thus ready for automated analysis to gain disease-related insights.
CONCLUSIONS: The RIMR AMD Consortium represents a novel model for global collaboration in AMD research, enabling the pooling and analysis of heterogeneous imaging data while addressing privacy, regulatory, and interoperability challenges. This framework may serve as a model for similar initiatives in other ocular diseases.