Accuracy of Deep Learning Algorithms for the Diagnosis of Retinopathy of Prematurity by Fundus Images: A Systematic Review and Meta-Analysis. (6th August 2021)

• Record Type: Journal Article
• Authors: Zhang, Jingjing; Liu, Yangyang; Mitsuhashi, Toshiharu; Matsuo, Toshihiko
• Other Names: El Gendy Heba A Academic Editor.
• Is Part Of: Journal of ophthalmology. Volume 2021(2021)
• Publication Date: 2021-08-06
• Subjects: Ophthalmology -- Periodicals; Eye Diseases; Ophthalmology; Ophthalmology; Electronic journals; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 617.7
• DOI: 10.1155/2021/8883946
• Languages: English
• ISSNs: 2090-004X

Abstract

Abstract : Background . Retinopathy of prematurity (ROP) occurs in preterm infants and may contribute to blindness. Deep learning (DL) models have been used for ophthalmologic diagnoses. We performed a systematic review and meta-analysis of published evidence to summarize and evaluate the diagnostic accuracy of DL algorithms for ROP by fundus images. Methods . We searched PubMed, EMBASE, Web of Science, and Institute of Electrical and Electronics Engineers Xplore Digital Library on June 13, 2021, for studies using a DL algorithm to distinguish individuals with ROP of different grades, which provided accuracy measurements. The pooled sensitivity and specificity values and the area under the curve (AUC) of summary receiver operating characteristics curves (SROC) summarized overall test performance. The performances in validation and test datasets were assessed together and separately. Subgroup analyses were conducted between the definition and grades of ROP. Threshold and nonthreshold effects were tested to assess biases and evaluate accuracy factors associated with DL models. Results . Nine studies with fifteen classifiers were included in our meta-analysis. A total of 521, 586 objects were applied to DL models. For combined validation and test datasets in each study, the pooled sensitivity and specificity were 0.953 (95% confidence intervals (CI): 0.946–0.959) and 0.975 (0.973–0.977), respectively, and the AUC was 0.984 (0.978–0.989). For the validation dataset and test dataset, the AUC was 0.977 (0.968–0.986) and 0.987 (0.982–0.992), respectively. In the subgroup analysis of ROP vs. normal and differentiation of two ROP grades, the AUC was 0.990 (0.944–0.994) and 0.982 (0.964–0.999), respectively. Conclusions . Our study shows that DL models can play an essential role in detecting and grading ROP with high sensitivity, specificity, and repeatability. The application of a DL-based automated system may improve ROP screening and diagnosis in the future.