Phase II year
2020
(last award dollars: 2022)
Phase II Amount
$1,690,295
More than 20 million patients suffer from age-related macular degeneration, diabetic retinopathy, or glaucoma. These degenerative eye diseases develop over decades, and their prevalence is increasing. Retinal imaging technologies such as optical coherence tomography and adaptive optics ophthalmoscopy are essential tools in the investigation and management of eye disease. New quantitative biomarkers derived from these and other imaging modalities are critical to the clinical translation of emerging ophthalmic innovations. However, biomarker development in the era of artificial intelligence requires large volumes of annotated images and transparent, reproducible processes, which places new demands on the management of living subjects research, data sharing, and algorithm development. Unfortunately, current software platforms are not effective in integrating these data in a manner that meets specific requirements in ophthalmology, Our goal in this Direct-to-Phase II SBIR, consistent with objectives of the NIH Strategic Plan for Data Science, is to create an integrated platform (PaaS) for the collection, curation, analysis, and sharing of ocular images and data. We will extend the capabilities of systems developed by the Advanced Ocular Imaging Program (AOIP), Medical College of Wisconsin (MCW), which include: (a) LATTICE - a software solution that reduces costs, reduces errors, and improves communications in the management of living-subjects research; (b) MOSAIC - an image processing platform and algorithm library with traditional and AI-trained algorithms; and (c) The AOIP Image Bank - a Repository that houses images and data on 1578 fully-consent human research subjects. To create the integrative platform, we will address four aims: (a) Extend LATTICE to meet the workflow requirements of academic and sponsored research in local and multisite environments, including the extensible direct integration of data relevant to ocular studies; (b) Design and implement a hybrid (local + cloud) REPOSITORY architecture, data schema, knowledge ontology, and query architecture for Owners and Readers of data.; (c) Integrate and demonstrate LATTICE, REPOSITORY and MOSAIC into a continuous ocular science workflow and (d) integrate and demonstrate Lattice, Repository and Mosaic into a continuous ocular science workflow. Our Integrated Translational Imaging platform will enable ophthalmic innovators to translate sight-saving insights and interventions to the clinic faster, with less frustration, and greater confidence. Our proposal fills an important technology gap in the field of ophthalmic data science and biomarker development. While the number and type of imaging devices continues to grow, the tools to develop and deploy new biomarkers and clinical endpoints using these exquisite imaging devices has not kept pace. With this program we will enable a new generation of image-driven innovation to find its way to the clinic.
Public Health Relevance Statement: Project Narrative With more than 20 million patients suffering from age-related macular degeneration, diabetic retinopathy, or glaucoma, it is crucial to develop non-invasive biomarkers as early predictors of eye disease and reliable tests of the safety and efficacy of new preventative and restorative therapies. To meet the unmet need for rapid access and analysis of ophthalmic research data for the discovery of these biomarkers, we will create an integrated platform (PaaS) for the collection, curation, sharing, and analysis of ocular images and data. If we meet our objectives, our platform will reduce the cost of clinical research and increase the speed of translating critical research insights to saving the sight of millions of patients.
Project Terms: adaptive optics; Address; Age related macular degeneration; algorithm development; algorithm training; Algorithms; application programming interface; Architecture; Artificial Intelligence; Automobile Driving; Biological Markers; biomarker development; biomarker discovery; Blindness; Clinic; Clinical; Clinical Research; clinical translation; Collection; Communication; Computer software; Consent; cost; Data; data access; Data Discovery; data exchange; data integration; Data Science; data sharing; data warehouse; deep learning; design; Diabetic Retinopathy; Docking; Economics; efficacy testing; Environment; experience; Eye diseases; fighting; Foundations; Frustration; Funding; Glaucoma; Goals; Housing; Human Subject Research; Hybrids; Image; image processing; image reconstruction; Imaging Device; imaging modality; imaging platform; imaging program; Imaging technology; improved; Influentials; innovation; insight; Intervention; Investigation; Knowledge; Libraries; medical schools; microsystems; Mosaicism; ocular imaging; Ontology; Ophthalmology; Ophthalmoscopy; Optical Coherence Tomography; Patients; Phase; Policies; Prevalence; Process; process repeatability; programs; Reader; repository; Research; Research Subjects; retinal imaging; safety testing; Savings; Science; Site; Small Business Innovation Research Grant; software systems; Speed; Strategic Planning; Structure; structured data; System; Technology; tool; Translating; Translations; United States National Institutes of Health; Validation; verification and validation; Vision; vision science; Wisconsin
