Investigative Ophthalmology & Visual Science Cover Image for Volume 65, Issue 7
June 2024
Volume 65, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2024
Linking multimodal image-linkage with the FRB! registry to enhance its globally impactful research potential – the beginning of “FRB! 2.0”.
Author Affiliations & Notes
  • Ash Kras
    The University of Sydney Save Sight Institute, Sydney, New South Wales, Australia
  • Mark C Gillies
    The University of Sydney Save Sight Institute, Sydney, New South Wales, Australia
  • Jennifer Arnold
    The University of Sydney Save Sight Institute, Sydney, New South Wales, Australia
  • Daniel Barthelmes
    The University of Sydney Save Sight Institute, Sydney, New South Wales, Australia
  • Hemal Mehta
    The University of Sydney Save Sight Institute, Sydney, New South Wales, Australia
  • Footnotes
    Commercial Relationships   Ash Kras Sanro Health, Code O (Owner); Mark Gillies None; Jennifer Arnold None; Daniel Barthelmes None; Hemal Mehta Sanro Health, Code C (Consultant/Contractor)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2024, Vol.65, 5631. doi:
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      Ash Kras, Mark C Gillies, Jennifer Arnold, Daniel Barthelmes, Hemal Mehta; Linking multimodal image-linkage with the FRB! registry to enhance its globally impactful research potential – the beginning of “FRB! 2.0”.. Invest. Ophthalmol. Vis. Sci. 2024;65(7):5631.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To demonstrate connectivity between the Fight Retinal Blindness! (FRB!) registry’s high quality clinical data with patients' corresponding multimodal images to enhance its research capabilities. Utilizing demigraphic and clinical data, in addition to images, unlocks increasingly personalized artificial intelligence (AI) data-driven insights.

Methods : Adhering to RANZCO’s ethics approval and with informed patient consent, 2 FRB! sites in Sydney piloted this project. The pseudonymized tabular information (demographic and clinical data) routinely collected in FRB!’s age related macular degeneration module was migrated to a secure Australian-based cloud destination from locally hosted servers. Multimodal images from Heidelberg Engineering® devices, including ocular coherence tomography (OCT), infrared and autofluorescence were exported from pilot clinics. Corresponding FRB! pseudonymized identifiers were computationally inserted enabling linkage. Other personal information was removed.

Results : Image-linkage was achieved in over 100 patients and enrolment continues. Tabular data was successfully moved to the cloud, captured as standardized SNOMED-CT concepts and converted to FHIR® (Fast Healthcare Interoperability Resource) format facilitating multiple interoperable capabilities. Images were exported in both ‘e2e’ and DICOM (Digital Imaging and Communications in Medicine) formats from Heyex 1 and Heyex 2 platforms respectively. Using a cloud-based instance of Heyex 2, each macular OCT produced 2 DICOM files; an image and an ‘ePDF’, which contained 22 OCT computer-readable measurements. These were also converted to FHIR and linked to FRB! tabular data.

Conclusions : This process demonstrated successful longitudinal image-linkage at the patient-visit level, enriching FRB! research structure-function relationships and its potential for increased clinical impact on visual outcomes. This offers unprecedented possibilities for AI prognostication derived from multimodal data inputs realizable both at the point of care and for population health. This is increasingly important in our current rapidly changing paradigms with emerging treatments for retinal disease.

This abstract was presented at the 2024 ARVO Annual Meeting, held in Seattle, WA, May 5-9, 2024.

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