使用光学相干断层扫描血管造影血管密度图基于自动机器学习的增殖性和非增殖性糖尿病视网膜病变分类
Automated machine learning-based classification of proliferative and non-proliferative diabetic retinopathy using optical coherence tomography angiography vascular density maps.
作者信息
Khalili Pour Elias, Rezaee Khosro, Azimi Hossein, Mirshahvalad Seyed Mohammad, Jafari Behzad, Fadakar Kaveh, Faghihi Hooshang, Mirshahi Ahmad, Ghassemi Fariba, Ebrahimiadib Nazanin, Mirghorbani Masoud, Bazvand Fatemeh, Riazi-Esfahani Hamid, Riazi Esfahani Mohammad
机构信息
Retina Service, Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Qazvin Square, South Karegar Street, Tehran, Iran.
Department of Biomedical Engineering, Meybod University, Meybod, Iran.
出版信息
Graefes Arch Clin Exp Ophthalmol. 2023 Feb;261(2):391-399. doi: 10.1007/s00417-022-05818-z. Epub 2022 Sep 2.
PURPOSE
The study aims to classify the eyes with proliferative diabetic retinopathy (PDR) and non-proliferative diabetic retinopathy (NPDR) based on the optical coherence tomography angiography (OCTA) vascular density maps using a supervised machine learning algorithm.
METHODS
OCTA vascular density maps (at superficial capillary plexus (SCP), deep capillary plexus (DCP), and total retina (R) levels) of 148 eyes from 78 patients with diabetic retinopathy (45 PDR and 103 NPDR) was used to classify the images to NPDR and PDR groups based on a supervised machine learning algorithm known as the support vector machine (SVM) classifier optimized by a genetic evolutionary algorithm.
RESULTS
The implemented algorithm in three different models reached up to 85% accuracy in classifying PDR and NPDR in all three levels of vascular density maps. The deep retinal layer vascular density map demonstrated the best performance with a 90% accuracy in discriminating between PDR and NPDR.
CONCLUSIONS
The current study on a limited number of patients with diabetic retinopathy demonstrated that a supervised machine learning-based method known as SVM can be used to differentiate PDR and NPDR patients using OCTA vascular density maps.
目的
本研究旨在使用监督式机器学习算法，基于光学相干断层扫描血管造影（OCTA）血管密度图，对增殖性糖尿病视网膜病变（PDR）和非增殖性糖尿病视网膜病变（NPDR）的眼睛进行分类。
方法
使用78例糖尿病视网膜病变患者（45例PDR和103例NPDR）的148只眼睛的OCTA血管密度图（在浅表毛细血管丛（SCP）、深层毛细血管丛（DCP）和全视网膜（R）水平），基于一种称为支持向量机（SVM）分类器的监督式机器学习算法，通过遗传进化算法进行优化，将图像分类为NPDR组和PDR组。
结果
在三种不同模型中实施的算法在所有三个血管密度图水平上对PDR和NPDR进行分类时，准确率高达85%。视网膜深层血管密度图在区分PDR和NPDR方面表现最佳，准确率达90%。
结论
目前对有限数量糖尿病视网膜病变患者的研究表明，一种称为支持向量机的基于监督式机器学习的方法可用于使用OCTA血管密度图区分PDR和NPDR患者。
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