Early Detection of Retinopathy of Prematurity (ROP) in Retinal Fundus Images Via Convolutional Neural Networks

TL;DR

This paper presents a CNN-based method for early detection of retinopathy of prematurity in retinal images, achieving high accuracy and sensitivity, which can aid in timely diagnosis and treatment.

Contribution

The study formulates ROP detection as an optimization problem and demonstrates the effectiveness of deep CNNs in extracting significant features for ROP identification.

Findings

Achieved 100% sensitivity and 96% specificity in ROP detection.

Deeper networks extract more significant features for better ROP understanding.

High accuracy and precision demonstrate the method's potential for clinical use.

Abstract

Retinopathy of prematurity (ROP) is an abnormal blood vessel development in the retina of a prematurely-born infant or an infant with low birth weight. ROP is one of the leading causes for infant blindness globally. Early detection of ROP is critical to slow down and avert the progression to vision impairment caused by ROP. Yet there is limited awareness of ROP even among medical professionals. Consequently, dataset for ROP is limited if ever available, and is in general extremely imbalanced in terms of the ratio between negative images and positive ones. In this study, we formulate the problem of detecting ROP in retinal fundus images in an optimization framework, and apply state-of-art convolutional neural network techniques to solve this problem. Experimental results based on our models achieve 100 percent sensitivity, 96 percent specificity, 98 percent accuracy, and 96 percent precision. In addition, our study shows that as the network gets deeper, more significant features can be extracted for better understanding of ROP.