Abstract
BackgroundRetinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide but can be a treatable retinal disease with appropriate and timely diagnosis. This study was performed to develop a robust intelligent system based on deep learning to automatically classify the severity of ROP from fundus images and detect the stage of ROP and presence of plus disease to enable automated diagnosis and further treatment.MethodsA total of 36,231 fundus images were labeled by 13 licensed retinal experts. A 101-layer convolutional neural network (ResNet) and a faster region-based convolutional neural network (Faster-RCNN) were trained for image classification and identification. We applied a 10-fold cross-validation method to train and optimize our algorithms. The accuracy, sensitivity, and specificity were assessed in a four-degree classification task to evaluate the performance of the intelligent system. The performance of the system was compared with results obtained by two retinal experts. Moreover, the system was designed to detect the stage of ROP and presence of plus disease as well as to highlight lesion regions based on an object detection network using Faster-RCNN.ResultsThe system achieved an accuracy of 0.903 for the ROP severity classification. Specifically, the accuracies in discriminating normal, mild, semi-urgent, and urgent were 0.883, 0.900, 0.957, and 0.870, respectively; the corresponding accuracies of the two experts were 0.902 and 0.898. Furthermore, our model achieved an accuracy of 0.957 for detecting the stage of ROP and 0.896 for detecting plus disease; the accuracies in discriminating stage I to stage V were 0.876, 0.942, 0.968, 0.998 and 0.999, respectively.ConclusionsOur system was able to detect ROP and differentiate four-level classification fundus images with high accuracy and specificity. The performance of the system was comparable to or better than that of human experts, demonstrating that this system could be used to support clinical decisions.
Highlights
Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide but can be a treatable retinal disease with appropriate and timely diagnosis
Convolutional neural networks (CNNs) are Deep learning (DL) algorithms commonly applied in image classification, which have been successfully used in the diagnosis of skin cancer [8], lung cancer [9], glioma [10], and breast histopathology [11]
Our intelligent system was evaluated regarding its ability to discriminate the four-degree classification of ROP from fundus images; The results showed that the system can achieve an accuracy of 0.903, a sensitivity of 0.778 with a specificity of 0.932 and a F1-score of 0.761 for grading the ROP cases as “normal,” “mild,” “semi-urgent,” and “urgent” (Fig. 6)
Summary
Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide but can be a treatable retinal disease with appropriate and timely diagnosis. This study was performed to develop a robust intelligent system based on deep learning to automatically classify the severity of ROP from fundus images and detect the stage of ROP and presence of plus disease to enable automated diagnosis and further treatment. Retinopathy of prematurity (ROP) is a proliferative retinal vascular disease that affects approximately twothirds of premature infants who weigh less than 1250 g at birth. It is associated with abnormal retinal vascular development at the boundary of vascularized and avascular peripheral retina [1, 2]. An automated ROP diagnosis system that can analyze real-world clinical features (i.e., stage and zone of ROP as well as the presence of plus disease) is rare
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
