Abstract
Objective: This study aims to implement and investigate the application of a special intelligent diagnostic system based on deep learning in the diagnosis of pterygium using anterior segment photographs.Methods: A total of 1,220 anterior segment photographs of normal eyes and pterygium patients were collected for training (using 750 images) and testing (using 470 images) to develop an intelligent pterygium diagnostic model. The images were classified into three categories by the experts and the intelligent pterygium diagnosis system: (i) the normal group, (ii) the observation group of pterygium, and (iii) the operation group of pterygium. The intelligent diagnostic results were compared with those of the expert diagnosis. Indicators including accuracy, sensitivity, specificity, kappa value, the area under the receiver operating characteristic curve (AUC), as well as 95% confidence interval (CI) and F1-score were evaluated.Results: The accuracy rate of the intelligent diagnosis system on the 470 testing photographs was 94.68%; the diagnostic consistency was high; the kappa values of the three groups were all above 85%. Additionally, the AUC values approached 100% in group 1 and 95% in the other two groups. The best results generated from the proposed system for sensitivity, specificity, and F1-scores were 100, 99.64, and 99.74% in group 1; 90.06, 97.32, and 92.49% in group 2; and 92.73, 95.56, and 89.47% in group 3, respectively.Conclusion: The intelligent pterygium diagnosis system based on deep learning can not only judge the presence of pterygium but also classify the severity of pterygium. This study is expected to provide a new screening tool for pterygium and benefit patients from areas lacking medical resources.
Highlights
Pterygium is a common exterior ocular disease with unknown etiology
The development of an intelligent image processing method based on anterior segment photography, and the implementation of an AIassisted automatic detection of pterygium, by which surgical indications could be identified through deep learning, will be beneficial for pterygium diagnoses
We only focused on the three-group classification of pterygium at this stage, there was no preprocessing of the original image before deep learning; We continue working on localization of the lesions, involving segmentation and visualization for labeling and rating the image category
Summary
Pterygium is a common exterior ocular disease with unknown etiology. It is essentially a chronic conjunctival degeneration more common among people who live near the equator or work outdoors (e.g., fishermen and farmers) and is thought to be an irritative phenomenon due to ultraviolet light, drying, and windy environments (Coroneo, 2011; Delic et al, 2017). A pterygium is clinically divided into two phases: active and stationary. Triangular growth of the bulbar conjunctiva with the head extending toward the cornea, it appears as hypertrophy and hyperemia of the fibrovascular tissue in the active phase, with corneal infiltration. It shows no hyperemia, no or less fibrovascular proliferation, a flat head of the pterygium, and transparent cornea. Diagnosis helps to alleviate a patient’s pain, relieve their economic burden, and improve the quality of their vision
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