Multi-level ranking classification algorithm for nuclear cataract based on AS-OCT image

Authors: Fang Lixin,  Zhou Yu,  Gu Yuanyuan,  Jiang Ziyuan,  Mou Lei,  Wang Yang,  Liu Fang,  Zhao Yitian
DOI: 10.3760/cma.j.cn115989-20221205-00567
Published 2024-03-10
Cite as Chin J Exp Ophthalmol, 2024, 42(3): 264-270.

Abstract                              [Download PDF] [Read Full Text]

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.

Key words:

Deep learning; Anterior segment optical coherence tomography; Nuclear cataract grading; Multi-scale fusion; Multi-level ranking algorithm

Contributor Information

Fang Lixin

College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China

Zhou Yu

Department of Ophthalmology, Shanghai Tenth People’s Hospital of Tongji University, Shanghai 200072, China

Gu Yuanyuan

Ningbo Cixi Institute of Biomedical Engineering, Cixi 315300, China

Ningbo Institute of Materials Technology &

Engineering, CAS, Ningbo 315201, China

Jiang Ziyuan

Department of Ophthalmology, Shanghai Tenth People’s Hospital of Tongji University, Shanghai 200072, China

Mou Lei

Ningbo Cixi Institute of Biomedical Engineering, Cixi 315300, China

Ningbo Institute of Materials Technology &

Engineering, CAS, Ningbo 315201, China

Wang Yang

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

Liu Fang

Department of Ophthalmology, Shanghai Tenth People’s Hospital of Tongji University, Shanghai 200072, China

Zhao Yitian

Ningbo Cixi Institute of Biomedical Engineering, Cixi 315300, China

Ningbo Institute of Materials Technology &

Engineering, CAS, Ningbo 315201, China

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