摘要
背景青光眼是一种不可逆性的致盲性眼病,研究青光眼眼底图像特征,利用人工智能技术在专业临床数据库的基础上建立诊断模型,能够快速、客观地对青光眼患者眼底图像进行判断。目的评价一种基于人工智能眼底分析技术的青光眼病灶诊断系统的性能,并利用该系统探索青光眼疾病进展中的杯盘比发展情况。方法选取2020年3月-2021年4月4000例在解放军总医院第三医学中心完成眼底照相患者的眼底照片,将采集的眼底照片进行视杯、视盘及视网膜纤维层缺损的分割标注、病灶多标签的分类标注及青光眼分期标注,获得眼底照片-青光眼数据集。建立一个基于深度学习算法模型的人工智能青光眼病灶诊断系统,并随机选取眼底照片进行内部验证以评估系统性能。利用该人工智能系统对不同分期的青光眼眼底照片进行杯盘比识别计算,分析随青光眼疾病进展的杯盘比分布差异。结果本研究共应用了6837张眼底照片,其中60%(4102张)用作训练集,40%(2735张)用作验证集。在验证集中人工智能青光眼病灶诊断系统在视杯分割预测结果上召回率平均为0.837,精确度平均为0.814,交并比平均为0.816,AUC平均为0.874;在视盘分割预测结果上召回率平均为0.928,精确度平均为0.926,交并比平均为0.916,AUC平均为0.941;在视网膜神经纤维层缺损(retinal nerve fiber layer defect,RNFLD)分割预测结果上召回率平均为0.653,精确度平均为0.612,交并比平均为0.480,AUC平均为0.749。在验证集中人工智能青光眼病灶诊断系统在局限性RNFLD病灶预测结果上准确度平均为0.890,敏感度平均为0.896,特异性平均为0.638,AUC平均为0.893;在弥漫性RNFLD病灶预测结果上准确度平均为0.950,敏感度平均为0.744,特异性平均为0.961,AUC平均为0.901;在视盘出血病灶预测结果上准确度平均为0.966,敏感度平均为0.650,特异性平均为0.967,AUC平均为0.969;在视杯切迹病灶预测结果上准确度平均为0.951,敏感度平均为0.794,特异性平均为0.957,AUC平均为0.892。对不同分期的青光眼眼底照片进行杯盘比识别计算,杯盘比值随青光眼视神经病变的进展逐渐增大。结论将人工智能眼底分析技术应用于青光眼病灶诊断系统,可以为实现青光眼筛查提供思路。
Background Glaucoma is an irreversible blinding eye disease,and research on the characteristics of glaucoma fundus images and establishment of a diagnostic model using artificial intelligent technology based on professional clinical databases can help to achieve a rapid and objective judgment of the fundus images of glaucoma patients.Objective To evaluate the performance of a glaucoma lesion diagnosis system based on artificial intelligent fundus analysis technology,and use the system to explore the development of the cup-to-disk ratio of glaucoma in disease progression.Methods Fundus images were collected from 4000 patients who completed fundus photography in the Third Medical Center of Chinese PLA General Hospital from March 2020 to April 2021,and then the images were used for segmenting the optic cup,the optic disc,and the retinal nerve fiber layer defect and labeling the multi-classification of lesions and the stage of glaucoma to obtain the fundus image-glaucoma dataset.An artificial intelligence glaucoma lesion diagnosis system was established based on deep learning model,and fundus images were randomly selected for internal validation to evaluate the performance of this system.The artificial intelligence system was used to identify and calculate the cup-to-disc ratio of glaucoma fundus images in different stages,in order to analyze the difference in the distribution of cup-to-disc ratio with the progression of glaucoma.Results A total of 6837 fundus images were included in this study,among which 60%(4102)were used as training set and 40%(2735)were used as validation set.In the validation set,the artificial intelligence glaucoma lesion diagnosis system had a mean recall rate of 0.837,a mean accuracy of 0.814,a mean intersection over union of 0.816,and a mean AUC of 0.874 in predicting the segmentation of the optic disc and the optic cup;this system had a mean recall rate of 0.928,a mean accuracy of 0.926,a mean intersection over union of 0.916,and a mean AUC of 0.941 in predicting optic disc segmentation;in predicting the segmentation of retinal nerve fiber layer defect,this system had a mean recall rate of 0.653,a mean accuracy of 0.612,a mean intersection over union of 0.480,and a mean AUC of 0.749.In the validation set,the artificial intelligence glaucoma lesion diagnosis system had a mean accuracy of 0.890,a mean sensitivity of 0.896,a mean specificity of 0.638,and a mean AUC of 0.893 in predicting focal RNFLD lesion;in predicting diffuse RNFLD lesion,the system had a mean accuracy of 0.950,a mean sensitivity of 0.744,a mean specificity of 0.961,and a mean AUC of 0.901;in predicting optic disc hemorrhage,the system had a mean accuracy of 0.966,a mean sensitivity of 0.650,a mean specificity of 0.967,and a mean AUC of 0.969;in predicting optic cup fissure,the system had a mean accuracy of 0.951,a mean sensitivity of 0.794,a mean specificity of 0.957,and a mean AUC of 0.892.The cup-to-disc ratio was identified and calculated for the fundus images of glaucoma in different stages,and the results showed that the cup-to-disc ratio gradually increased with the progression of glaucoma optic neuropathy.Conclusion The application of artificial intelligent fundus analysis technology in the glaucoma lesion diagnosis system can provide ideas for realizing glaucoma screening.
作者
吴星
黄烨霖
叶子
马彤
陈羽中
王大江
WU Xing;HUANG Yelin;YE Zi;MA Tong;CHEN Yuzhong;WANG Dajiang(Senior Department of Ophthalmology,the Third Medical Center,Chinese PLA General Hospital,Beijing 100039,China;Beijing Airdoc Technology Co.,Ltd.,Beijing 100089,China)
出处
《解放军医学院学报》
CAS
北大核心
2022年第10期1014-1018,共5页
Academic Journal of Chinese PLA Medical School
基金
北京市自然科学基金(7212092)
首都医学发展科研基金(2022-2-5041)
解放军总医院医疗大数据研发项目(2019MBD-037)。
关键词
人工智能
深度学习
青光眼
视杯
视盘
artificial intelligence
deep learning
glaucoma
optic cup
optic disc
