Clinically applicable artificial intelligence algorithm for the diagnosis, evaluation, and monitoring of acute retinal necrosis
Clinically applicable artificial intelligence algorithm for the diagnosis, evaluation, and monitoring of acute retinal necrosis
复制标题
临床适用的人工智能算法用于急性视网膜坏死的诊断、评估和监测
DOI：
10.1631/jzus.b2000343
复制
发表时间：
2021-06-01
影响因子：
5.1
通讯作者：
Yao, Ke
中科院分区：
文献类型：
--
作者：
Feng, Lei;Zhou, Daizhan;Yao, Ke
视网膜坏死区的及时发现和正确评估对急性视网膜坏死(ARN)的诊断和治疗尤为重要。人工智能(AI)算法在这些临床研究领域的潜在应用之前还没有报道。本研究旨在创建一种用于从视网膜眼底照片中自动检测和评估视网膜坏死的计算算法。收集32例(40只眼)ARN患者的149张广角眼底照片，采用U-net方法构建人工智能算法。在此基础上，首次构建了一种基于深度机器学习的视网膜坏死检测与评价算法。该算法的受试者工作曲线下面积为0.92，检测视网膜坏死的灵敏度为86%，特异度为88%。对于视网膜坏死的评估，AI算法计算的坏死面积与房水中的病毒载量(R2=0.7444，P<；0.0001)和ARN的疗效(R2=0.999，P<；0.0001)呈显著正相关。因此，我们的人工智能算法在ARN的临床辅助诊断、ARN严重程度的评估和治疗反应监测等方面具有潜在的应用价值。
The prompt detection and proper evaluation of necrotic retinal region are especially important for the diagnosis and treatment of acute retinal necrosis (ARN). The potential application of artificial intelligence (AI) algorithms in these areas of clinical research has not been reported previously. The present study aims to create a computational algorithm for the automated detection and evaluation of retinal necrosis from retinal fundus photographs. A total of 149 wide-angle fundus photographs from 40 eyes of 32 ARN patients were collected, and the U-Net method was used to construct the AI algorithm. Thereby, a novel algorithm based on deep machine learning in detection and evaluation of retinal necrosis was constructed for the first time. This algorithm had an area under the receiver operating curve of 0.92, with 86% sensitivity and 88% specificity in the detection of retinal necrosis. For the purpose of retinal necrosis evaluation, necrotic areas calculated by the AI algorithm were significantly positively correlated with viral load in aqueous humor samples (R2=0.7444, P<0.0001) and therapeutic response of ARN (R2= 0.999, P<0.0001). Therefore, our AI algorithm has a potential application in the clinical aided diagnosis of ARN, evaluation of ARN severity, and treatment response monitoring.