Automated retinopathy of prematurity classification using machine learning

Project Summary/Abstract
The goal of this project is to develop a web-based, semi-automated system for identifying severe retinopathy of
prematurity (ROP) with plus disease, using an existing data set of retinal images collected from previous NIH-
funded research studies. ROP is treatable if diagnosed early, yet continues to be a leading cause of childhood
blindness throughout the world. Diagnosis and documentation of ophthalmoscopic findings in ROP are
subjective and qualitative, and studies have found that there is often significant diagnostic variation, even when
experts are shown the exact same clinical data. Computer-based image analysis and the application of
machine learning techniques to feature extraction and image classification have potential to address many of
these limitations. Recent advances in image processing have had led to sophisticated techniques for tracing
vessel-like structures. Additionally, machine-learning techniques will enable us to leverage these existing
annotated image databases to improve the performance of our algorithms for vessel segmentation and disease
classification. Our overall hypothesis is that retinal vascular features may be quantified and used to assist
clinicians in the diagnosis of ROP. These hypotheses will be tested using two Specific Aims: (1) Develop and
evaluate semi-automated algorithms to segment retinal vessels and generate a set of retinal vessel-based
features. (2) Develop computer-based decision support algorithms that best correlate with expert opinions.
Overall, this project will build upon infrastructure developed from previous studies, create potential for
improving the accuracy and consistency of clinical ROP diagnosis, provide a demonstration of computer-based
decision support from image analysis during real-world medical care, and stimulate future research toward
understanding the vascular features associated with severe ROP. This project will be performed by a multi-
disciplinary team of investigators with expertise in ophthalmology, biomedical informatics, computer science,
machine learning, and image processing.