The application and value evaluation of assisted diagnosis system for five fundus lesion based on artificial intelligence combined with optical coherence tomography
10.3760/cma.j.cn511434-20220104-00002
- VernacularTitle:人工智能结合光相干断层扫描技术辅助识别5种眼底病变系统的应用和价值评估
- Author:
Jian MA
1
;
Shumei CHEN
;
Min WANG
;
Fuli WU
;
Jian WU
;
Xiaoyun FANG
Author Information
1. 浙江大学医学院附属第二医院眼科中心, 杭州 310009
- Keywords:
Artificial intelligence;
Tomography, optical coherence;
Retinal perforations;
Macular edema;
Choroidal neovascularization;
Macular degeneration;
Macular epi
- From:
Chinese Journal of Ocular Fundus Diseases
2022;38(2):126-131
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish an artificial intelligence robot-assisted diagnosis system for fundus diseases based on deep learning optical coherence tomography (OCT) and evaluate its application value.Methods:Diagnostic test studies. From 2016 to 2019, 25 000 OCT images of 25 000 patients treated at the Eye Center of the Second Affiliated Hospital of Zhejiang University School of Medicine were used as training sets and validation sets for the fundus intelligent assisted diagnosis system. Among them, macular epiretinal membrane (MERM), macular edema, macular hole, choroidal neovascularization (CNV), and age-related macular degeneration (AMD) were 5 000 sheets each. The training set and the verification set are 18 124 and 6 876 sheets, respectively. Through the transfer learning Attention ResNet structure algorithm, the OCT image was characterized by lesion identification, the disease feature was extracted by a specific procedure, and the given image was distinguished from other types of disease according to the statistical characteristics of the target lesion. The model algorithms of MERM, macular edema, macular hole, CNV and AMD were initially formed, and the fundus intelligent auxiliary diagnosis system of five models was established. The performance of each model-assisted diagnosis in the fundus intelligent auxiliary diagnostic system was evaluated by applying the subject working characteristic curve, area under the curve (AUC), sensitivity, and specificity.Results:With the intelligent auxiliary diagnosis system, the diagnostic sensitivity of the MERM was 93.5%, the specificity was 99.23%, and AUC was 0.983 7; the diagnostic sensitivity of macular edema was 99.02%, the specificity was 98.17%, and AUC was 0.994 6; the diagnostic sensitivity of macular hole was 98.91%, the specificity was 99.91%, AUC was 0.996 2; the diagnostic sensitivity of CNV was 97.54%, the specificity was 94.71%, AUC was 0.987 5; the diagnostic sensitivity of AMD was 95.12%, the specificity was 97.09%, AUC was 0.985 3.Conclusions:The artificial intelligence robot-assisted diagnosis system for fundus diseases based on deep learning for OCT images has accurate and efficient diagnostic performance for assisting the diagnosis of MERM, macular edema, macular hole, CNV, and AMD.
