Automated identification of retinopathy of prematurity by image-based deep learning

Abstract

Background: 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. Methods: A 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. Results: The 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. Conclusions: Our 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.

Performance of deep-learning artificial intelligence algorithms in detecting retinopathy of prematurity

“…Thirty full-text articles were assessed for eligibility and 12 studies were included in the systematic review. [ 12 13 21 22 23 24 25 26 27 28 29 30 31 ] Fifty studies were excluded due to no test of diagnostic performance,[ 32 33 34 35 36 37 38 39 ] no classification task,[ 40 41 42 ] no internal validation,[ 23 43 ] no AI algorithm,[ 44 ] and not based on standard clinical care. [ 45 ]…”

“…All twelve studies obtained retrospective images as part of routine clinical care or from local screening programs. Seven of these studies collected images from China,[ 22 24 25 26 28 29 31 ] one from India, one from North America,[ 12 ] one from America and Mexico sites,[ 30 ] one from America and Nepal,[ 21 ] and one study included images from New Zealand. [ 13 ] Date range for image collection among all studies varied from July 2011 to June 2020.…”

“…Three studies specified their inclusion criteria[ 25 26 31 ] and five other studies specified their exclusion criteria. [ 12 13 21 28 29 ] Poor quality images were excluded in five studies[ 12 13 28 29 31 ] and image augmentation occurred in seven studies. [ 13 21 25 27 28 29 30 ] These characteristics are summarized in Table 1 .…”

“…[ 12 13 21 28 29 ] Poor quality images were excluded in five studies[ 12 13 28 29 31 ] and image augmentation occurred in seven studies. [ 13 21 25 27 28 29 30 ] These characteristics are summarized in Table 1 . Seven studies recorded demographic information,[ 21 24 25 26 27 29 31 ] of which the mean GA was 30.9 weeks and mean birth weight was 1501.25 g. A total of 178,459 images were used across all 12 studies ranging from 2668 to 52,249 images per study.…”

Computer-Aided Detection of Retinopathy of Prematurity Severity in Preterm Infants via Measurement of Temporal Vessel Width and Angle

“…Because of the recent development of artificial intelligence, automatic ROP diagnosis through the identification of plus disease has been achieved using deep learning technology (5)(6)(7)(8)(9). A few reports are available on retinal vessel angles and their relationship with ROP, especially as ROP progressively worsens (10,11).…”