Affiliation:
1. School of Information Engineering, Huzhou University, Huzhou 313000, China
2. Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources, Huzhou University, Huzhou 313000, China
3. Jiangsu Testing and Inspection Institute for Medical Devices, Nanjing 210000, China
4. Department of Ophthalmology, The First People’s Hospital of Huzhou, Huzhou 313000, China
Abstract
A two-category model and a segmentation model of pterygium were proposed to assist ophthalmologists in establishing the diagnosis of ophthalmic diseases. A total of 367 normal anterior segment images and 367 pterygium anterior segment images were collected at the Affiliated Eye Hospital of Nanjing Medical University. AlexNet, VGG16, ResNet18, and ResNet50 models were used to train the two-category pterygium models. A total of 150 normal and 150 pterygium anterior segment images were used to test the models, and the results were compared. The main evaluation indicators, including sensitivity, specificity, area under the curve, kappa value, and receiver operator characteristic curves of the four models, were compared. Simultaneously, 367 pterygium anterior segment images were used to train two improved pterygium segmentation models based on PSPNet. A total of 150 pterygium images were used to test the models, and the results were compared with those of the other four segmentation models. The main evaluation indicators included mean intersection over union (MIOU), IOU, mean average precision (MPA), and PA. Among the two-category models of pterygium, the best diagnostic result was obtained using the VGG16 model. The diagnostic accuracy, kappa value, diagnostic sensitivity of pterygium, diagnostic specificity of pterygium, and F1-score were 99%, 98%, 98.67%, 99.33%, and 99%, respectively. Among the pterygium segmentation models, the double phase-fusion PSPNet model had the best results, with MIOU, IOU, MPA, and PA of 86.57%, 78.1%, 92.3%, and 86.96%, respectively. This study designed a pterygium two-category model and a pterygium segmentation model for the images of the normal anterior and pterygium anterior segments, which could help patients self-screen easily and assist ophthalmologists in establishing the diagnosis of ophthalmic diseases and marking the actual scope of surgery.
Funder
National Natural Science Foundation of China
Subject
Health Informatics,Biomedical Engineering,Surgery,Biotechnology
Reference36 articles.
1. Progress in pathogenesis and treatment of pterygium;J. Chen;Modern Journal of Integrated Traditional Chinese and Western Medicine,2020
2. Preliminary study of pterygium intelligent diagnosis system based on deep learning;L. Jin;Artificial Intelligence,2021
3. Study on application of quality nursing in dry eye after the surgery of primary pterygium;X. P. Pan;China Modern Doctor,2020
4. Evaluation of therapeutic contact lenses used in pterygium surgery combined with limbal conjunctival autograft transplantation;X. L. Zhang;International Eye Science,2019
5. A study on changes in corneal refractive and corneal surface regularity before and after pterygium excision surgery;L. H. Tao;China Medical Herald,2018