Multi-level ranking classification algorithm for nuclear cataract based on AS-OCT image
10.3760/cma.j.cn115989-20221205-00567
- VernacularTitle:基于AS-OCT图像的核性白内障多级排序分类算法研究
- Author:
Lixin FANG
1
;
Yu ZHOU
;
Yuanyuan GU
;
Ziyuan JIANG
;
Lei MOU
;
Yang WANG
;
Fang LIU
;
Yitian ZHAO
Author Information
1. 浙江工业大学机械工程学院，杭州 310014
- Keywords:
Deep learning;
Anterior segment optical coherence tomography;
Nuclear cataract grading;
Multi-scale fusion;
Multi-level ranking algorithm
- From:
Chinese Journal of Experimental Ophthalmology
2024;42(3):264-270
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the diagnostic value of an intelligent assisted grading algorithm for nuclear cataract using anterior segment optical coherence tomography (AS-OCT) images.Methods:A diagnostic test study was conducted.AS-OCT image data were collected from 939 cases of 1 608 eyes of nuclear cataract patients at the Shanghai Tenth People's Hospital of Tongji University from November 2020 to September 2021.The data were obtained from the electronic case system and met the requirements for clinical reading clarity.Among them, there were 398 cases of 664 male eyes and 541 cases of 944 female eyes.The ages of the patients ranged from 18 to 94 years, with a mean age of (65.7±18.6) years.The AS-OCT images were labelled manually from one to six levels according to the Lens Opacities Classification System Ⅲ (LOCS Ⅲ grading system) by three experienced clinicians.This study proposed a global-local cataract grading algorithm based on multi-level ranking, which contains five basic binary classification global local network (GL-Net).Each GL-Net aggregates multi-scale information, including the cataract nucleus region and original image, for nuclear cataract grading.Based on ablation test and model comparison test, the model's performance was evaluated using accuracy, precision, sensitivity, F1 and Kappa, and all results were cross-validated by five-fold.This study adhered to the Declaration of Helsinjki and was approrved by Shanghai Tenth People's Hospital of Tongji University (No.21K216).Results:The model achieved the results with an accuracy of 87.81%, precision of 88.88%, sensitivity of 88.33%, F1 of 88.51%, and Kappa of 85.22% on the cataract dataset.The ablation experiments demonstrated that ResNet18 combining local and global features for multi-level ranking classification improved the accuracy, recall, specificity, F1, and Kappa metrics.Compared with ResNet34, VGG16, Ranking-CNN, MRF-Net models, the performance index of this model were improved.Conclusions:The deep learning-based AS-OCT nuclear cataract image multi-level ranking classification algorithm demonstrates high accuracy in grading cataracts.This algorithm may help ophthalmologists in improving the diagnostic accuracy and efficiency of nuclear cataract.
