Abstract
There is a disparity between the increasing application of digital retinal imaging to neonatal ocular screening and slowly growing number of pediatric ophthalmologists. Assistant tools that can automatically detect ocular disorders may be needed. In present study, we develop a deep convolutional neural network (DCNN) for automated classification and grading of retinal hemorrhage. We used 48,996 digital fundus images from 3770 newborns with retinal hemorrhage of different severity (grade 1, 2 and 3) and normal controls from a large cross-sectional investigation in China. The DCNN was trained for automated grading of retinal hemorrhage (multiclass classification problem: hemorrhage-free and grades 1, 2 and 3) and then validated for its performance level. The DCNN yielded an accuracy of 97.85 to 99.96%, and the area under the receiver operating characteristic curve was 0.989–1.000 in the binary classification of neonatal retinal hemorrhage (i.e., one classification vs. the others). The overall accuracy with regard to the multiclass classification problem was 97.44%. This is the first study to show that a DCNN can detect and grade neonatal retinal hemorrhage at high performance levels. Artificial intelligence will play more positive roles in ocular healthcare of newborns and children.
Highlights
Retinal hemorrhage (RH) is one of the most common ocular abnormalities in newborns and has a wide prevalence ranging from 2.6 to 50.0% [1,2,3]
Neonatal RH was diagnosed and graded according to Egge’s classification [16]: (1) Grade 1: small retinal hemorrhage confined to the area around the optic nerve, associated with dot or fine linear bleeding; (2) Grade 2: slightly larger amount of retinal hemorrhage than Grade 1; patchy, dot, blot or flame-shaped hemorrhage, size does not exceed the optic disc diameter; (3) Grade 3: retinal hemorrhage more than the diameter of the optic disc area, a line of flame-shaped hemorrhage along vessels, macular hemorrhage The images of graded RH and normal fundi were used for training and testing of the deep convolutional neural network (DCNN)
The area under the receiver operating characteristic curve (AUC) for each classification was 0.995, 1.000 and 0.989, respectively, implying that our DCNN has the best performance for classification of grade 2 RH
Summary
Retinal hemorrhage (RH) is one of the most common ocular abnormalities in newborns and has a wide prevalence ranging from 2.6 to 50.0% [1,2,3]. Direct or indirect ophthalmoscopy is usually used for primary ocular screening of newborns. Some ocular abnormalities or RH in the peripheral regions may be missed due to the limited inspection range. Indirect ophthalmoscopy requires experienced and skilled experts for both performance and diagnosis, which may limit the widespread use and screening accuracy of this technique [7]. The other difference between NH and other hemorrhages found in other ocular or retinal disease was rarely reported
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