Automated detection of nine infantile fundus diseases and conditions in retinal images using a deep learning system

Abstract

Purpose: We developed an Infant Retinal Intelligent Diagnosis System (IRIDS), an automated system to aid early diagnosis and monitoring of infantile fundus diseases and health conditions to satisfy urgent needs of ophthalmologists.

Methods: We developed IRIDS by combining convolutional neural networks and transformer structures, using a dataset of 7697 retinal images (1089 infants) from four hospitals. It identifies nine fundus diseases and conditions, namely, retinopathy of prematurity (ROP) (mild ROP, moderate ROP, and severe ROP), retinoblastoma (RB), retinitis pigmentosa (RP), Coats disease, coloboma of the choroid, congenital retinal fold (CRF), and normal. IRIDS also includes depth attention modules, ResNet-18 (Res-18), and Multi-Axis Vision Transformer (MaxViT). Performance was compared to that of ophthalmologists using 450 retinal images. The IRIDS employed a five-fold cross-validation approach to generate the classification results.

Results: Several baseline models achieved the following metrics: accuracy, precision, recall, F1-score (F1), kappa, and area under the receiver operating characteristic curve (AUC) with best values of 94.62% (95% CI, 94.34%-94.90%), 94.07% (95% CI, 93.32%-94.82%), 90.56% (95% CI, 88.64%-92.48%), 92.34% (95% CI, 91.87%-92.81%), 91.15% (95% CI, 90.37%-91.93%), and 99.08% (95% CI, 99.07%-99.09%), respectively. In comparison, IRIDS showed promising results compared to ophthalmologists, demonstrating an average accuracy, precision, recall, F1, kappa, and AUC of 96.45% (95% CI, 96.37%-96.53%), 95.86% (95% CI, 94.56%-97.16%), 94.37% (95% CI, 93.95%-94.79%), 95.03% (95% CI, 94.45%-95.61%), 94.43% (95% CI, 93.96%-94.90%), and 99.51% (95% CI, 99.51%-99.51%), respectively, in multi-label classification on the test dataset, utilizing the Res-18 and MaxViT models. These results suggest that, particularly in terms of AUC, IRIDS achieved performance that warrants further investigation for the detection of retinal abnormalities.

Conclusions: IRIDS identifies nine infantile fundus diseases and conditions accurately. It may aid non-ophthalmologist personnel in underserved areas in infantile fundus disease screening. Thus, preventing severe complications. The IRIDS serves as an example of artificial intelligence integration into ophthalmology to achieve better outcomes in predictive, preventive, and personalized medicine (PPPM / 3PM) in the treatment of infantile fundus diseases.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-024-00350-y.

Keywords: Deep learning; infant; Fundus disease; Predictive preventive personalized medicine (PPPM / 3PM); Retinal image.

Fig. 1

Workflow of the overall study and architecture of the IRIDS. A. Overview of the study protocol. Images were collected from four clinical centers and filtered by quality assessment. Subsequently, the data was processed by IRIDS, and finally, an ophthalmologists-IRIDS comparison was conducted. B. The architecture of the IRIDS. The IRIDS consists of two branches: CNNs and Transformers. The model employs four-stage Transformer modules to extract global features, and the ResNet-34 with residual blocks is selected to extract local features. The extracted features are fused by the depth attention module to fully represent the specific features related to different diseases. C. Illustration of the used blocks. (a)-(c) represent the module of the Res_block, Transformer block, and Depth attention fusion module, respectively

Fig. 2

ROC curves of each of the deep learning methods analyzed in this study. A. ROC curves of the different baseline methods. (a)-(f) represent the Res-18, Res-34, Res-50, ViT-small, RegionViT and MaxViT models, respectively. B. The ROC curves of the comparison methods for the main classes of diseases and conditions. (a)-(f) represent the Coats, Coloboma, RB, ROP, RP, and CRF categories, respectively. C. The ROC curves of the comparison methods for ROP staging. (a)-(c) represent the Mild, Moderate, and Severe conditions, respectively

Fig. 3

Visualization of the classifier performances. A. Confusion matrixes of different methods. The confusion matrix is a specific matrix used to present the visualization effect of algorithm performance. Each column represents the predicted value and each row represents the actual category. (a)-(d) represent the Res-18, MaxViT, Res-18 + MaxViT and Res-18 + MaxViT + DA (Proposed) models, respectively. B. T-SNE visualization of different methods. (a)-(d) represent the Res-18, MaxViT, Res-18 + MaxViT and Res-18 + MaxViT + DA (Proposed) models, respectively