The long-term goal of this project is to establish a quantitative framework for retinopathy of prematurity (ROP) care based on clinical, imaging, genetic, and informatics principles. In the previous grant period, we have developed artificial intelligence methods for ROP diagnosis, but real-world adoption has been limited by lack of prospective validation and by perception of these systems as ?black boxes? that do not explain their rationale for diagnosis. Furthermore, although biomedical research data are being generated at an enormous pace, much less work has been done to integrate disparate scientific findings across the spectrum from genomics to imaging to clinical medicine. This renewal will address current gaps in knowledge in these areas. Our overall hypotheses are that developing a quantitative framework for ROP care using artificial intelligence and analytics will improve clinical disease management, that building ?explainable? artificial intelligence systems will enhance clinical acceptance and educational opportunities, and that analysis of relationships among clinical, imaging, environmental, and genetic findings, in ROP will improve understanding of disease pathogenesis and risk. These hypotheses will be tested using three Specific Aims: (1) Evaluation performance of an artificial intelligence system for ROP diagnosis and screening prospectively. This will include: (a) recruit a target of over 2000 eye exams including wide-angle retinal images from 375 subjects at 5 centers, (b) optimize an image quality detection algorithm we have recently developed, and (c) analyze system accuracy for ROP diagnosis and screening (using a novel quantitative vascular severity scale). (2) Improve the interpretability of our existing artificial intelligence methods for ROP diagnosis. This will include: (a) increase ?explainability? of systems by combining deep learning with traditional feature extraction methods, (b) develop neural networks to identify changes between serial images, and (c) evaluate these methods through systematic feedback by experts. (3) Develop integrated models for ROP pathogenesis and risk. This will include: (a) build and improve ROP risk prediction models based on clinical, image, and demographic features, and (b) integrate genetic, imaging, clinical, and environmental variables through genetic risk prediction by machine learning, by investigating casual relationships with genetic variants and genetic risk scores, and by incorporating SNP associations with gene expression measurements to identify functional genes of ROP. Ultimately, these studies will significantly reduce barriers to adoption of technologies such as artificial intelligence for clinicians, and will demonstrate a prototype for health information management which combines genotypic and phenotypic data. This project will be performed by a multi-disciplinary team of investigators who have worked successfully together for nearly 10 years, and who have expertise in ophthalmology, biomedical informatics, computer science, computational biology, ophthalmic genetics, genetic analysis, and statistical genetics.
ROP is a leading cause of childhood blindness in the US and throughout the world, and the number of infants at risk for disease is increasing as the rate of premature birth rises. Rapidly-progressive changes associated with retinal vascular development may be visualized by clinical examination, captured by wide-angle imaging, and analyzed genetically. This project will develop, enhance, and validate artificial intelligence and analytic tools to help clinicians identify infants at risk for severe ROP using image analysis, genetic analysis, and integrative informatics that combines these factors ? while also providing insight about disease pathogenesis.
| Chee, Ru-Ik; Darwish, Dana; Fernandez-Vega, Alvaro et al. (2018) Retinal Telemedicine. Curr Ophthalmol Rep 6:36-45 |
| Biten, Hilal; Redd, Travis K; Moleta, Chace et al. (2018) Diagnostic Accuracy of Ophthalmoscopy vs Telemedicine in Examinations for Retinopathy of Prematurity. JAMA Ophthalmol 136:498-504 |
| Redd, Travis K; Campbell, John Peter; Brown, James M et al. (2018) Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity. Br J Ophthalmol : |
| Kim, Sang Jin; Port, Alexander D; Swan, Ryan et al. (2018) Retinopathy of prematurity: a review of risk factors and their clinical significance. Surv Ophthalmol 63:618-637 |
| Gupta, Mrinali P; Dow, Eliot; Jeng-Miller, Karen W et al. (2018) SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY FINDINGS IN COATS DISEASE. Retina : |
| Redd, Travis K; Campbell, J Peter; Chiang, Michael F (2018) Is This the Right Reference Standard Diagnosis for Retinopathy of Prematurity?-Reply. JAMA Ophthalmol 136:1429-1430 |
| Swan, Ryan; Kim, Sang Jin; Campbell, J Peter et al. (2018) The genetics of retinopathy of prematurity: a model for neovascular retinal disease. Ophthalmol Retina 2:949-962 |
| Kim, Sang Jin; Campbell, J Peter; Ostmo, Susan et al. (2017) Changes in Relative Position of Choroidal Versus Retinal Vessels in Preterm Infants. Invest Ophthalmol Vis Sci 58:6334-6341 |
| Moleta, Chace; Campbell, J Peter; Kalpathy-Cramer, Jayashree et al. (2017) Plus Disease in Retinopathy of Prematurity: Diagnostic Trends in 2016 Versus 2007. Am J Ophthalmol 176:70-76 |
| Peng Tian; Ataer-Cansizoglu, Esra; Kalpathy-Cramer, Jayashree et al. (2016) Toward a severity index for ROP: An unsupervised approach. Conf Proc IEEE Eng Med Biol Soc 2016:1312-1315 |
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