Five-Category Intelligent Auxiliary Diagnosis Model of Common Fundus Diseases Based on Fundus Images

Abstract

Purpose The discrepancy of the number between ophthalmologists and patients in China is large. Retinal vein occlusion (RVO), high myopia, glaucoma, and diabetic retinopathy (DR) are common fundus diseases. Therefore, in this study, a five-category intelligent auxiliary diagnosis model for common fundus diseases is proposed; the model's area of focus is marked. Methods A total of 2000 fundus images were collected; 3 different 5-category intelligent auxiliary diagnosis models for common fundus diseases were trained via different transfer learning and image preprocessing techniques. A total of 1134 fundus images were used for testing. The clinical diagnostic results were compared with the diagnostic results. The main evaluation indicators included sensitivity, specificity, F1-score, area under the concentration-time curve (AUC), 95% confidence interval (CI), kappa, and accuracy. The interpretation methods were used to obtain the model's area of focus in the fundus image. Results The accuracy rates of the 3 intelligent auxiliary diagnosis models on the 1134 fundus images were all above 90%, the kappa values were all above 88%, the diagnosis consistency was good, and the AUC approached 0.90. For the 4 common fundus diseases, the best results of sensitivity, specificity, and F1-scores of the 3 models were 88.27%, 97.12%, and 84.02%; 89.94%, 99.52%, and 93.90%; 95.24%, 96.43%, and 85.11%; and 88.24%, 98.21%, and 89.55%, respectively. Conclusions This study designed a five-category intelligent auxiliary diagnosis model for common fundus diseases. It can be used to obtain the diagnostic category of fundus images and the model's area of focus. Translational Relevance This study will help the primary doctors to provide effective services to all ophthalmologic patients.

Screening of Common Retinal Diseases Using Six-Category Models Based on EfficientNet

“…Their basic network structure mainly included convolutional layers, pooling layers, and fully connected layers. Our team previously used ResNet50 to train a five-category intelligent auxiliary diagnosis model for common retinal diseases (normal fundus and four common retinal diseases, excluding MD in that study) ( 15 ). Hence, in this study, the ResNet50 model was used to classify the normal fundus and the five common retinal diseases.…”

“…The pertinent features of the ophthalmologic diseases were manually selected then identified through machine learning (7)(8)(9)(10)(11)(12)(13). Deep learning used convolutional neural networks to automatically extract image features; it obtained satisfactory results in the field of ophthalmology (14)(15)(16)(17)(18)(19)(20)(21)(22)(23). Many researchers have used deep learning to diagnose retinal diseases using fundus images.…”

“…There were also a few studies that focused on the simultaneous screening of multiple diseases. Zheng et al ( 15 ) used 2,000 fundus images to design a five-category model of common retinal diseases based on ResNet50; the model was able to diagnose common retinal diseases, except for macular degeneration (MD). Cen et al ( 27 ) used deep neural networks to identify 39 retinal diseases and conditions that needed to be referred to higher facilities of care; although satisfactory results were achieved, the amount of data required for training was too large.…”

“…Our team in a previous study used the ResNet50 to create a five-category model that consisted of the normal fundus and four common fundus diseases (RVO, high myopia, glaucoma, and DR) ( 15 ), with an AUC above 0.92 and a kappa value of 89.33%. The retinal diseases in the previous study did not include MD because of its complicated features and different sub-types.…”

Research on an Intelligent Lightweight-Assisted Pterygium Diagnosis Model Based on Anterior Segment Images

“…Deep learning models have been used by lots of researchers to diagnose DR [ 11 – 14 ] since then. In addition to DR, researchers have used deep learning methods to detect common fundus diseases, including glaucoma [ 15 – 17 ], retinal vein occlusion [ 18 , 19 ], age-related macular degeneration [ 20 – 22 ], and even research on the classification of multiple common fundus diseases [ 23 ]. These studies obtained good results.…”