Hybrid Deep Learning on Single Wide-field Optical Coherence tomography Scans Accurately Classifies Glaucoma Suspects.
Hybrid Deep Learning on Single Wide-field Optical Coherence tomography Scans Accurately Classifies Glaucoma Suspects.
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DOI：
10.1097/ijg.0000000000000765
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发表时间：
2017-12
影响因子：
2
通讯作者：
Hood DC
中科院分区：
文献类型：
--
作者：
Muhammad H;Fuchs TJ;De Cuir N;De Moraes CG;Blumberg DM;Liebmann JM;Ritch R;Hood DC
基于光学相干断层扫描（OCT）扫描和/或视野（VF）的现有汇总统计量在临床上用于区分健康眼睛和青光眼眼睛是次优的。这项研究评估了混合深度学习方法（HDLM）与单一宽场OCT协议相结合的程度，可以区分先前被分类为健康嫌疑人或轻度青光眼的眼睛。来自102名患有或疑似开角型青光眼的患者的102只眼睛先前被两位青光眼专家分类为青光眼（57只眼睛）或健康/疑似（45只眼睛）。HDLM只能访问每例患者单次宽视野（9× 12 mm）扫频源OCT扫描的信息。卷积神经网络用于从这些扫描得到的地图中提取丰富的特征。随机森林分类器被用来训练一个基于这些特征的模型来预测是否存在脑损伤。将该算法与传统的OCT和VF指标进行比较。HDLM的准确度范围从63.7%到93.1%，取决于输入的地图。RNFL概率图的准确率最高（93.1%），有4个假阳性和3个假阴性。相比之下，OCT和24-2和10-2 VF指标的准确性范围为66.7%至87.3%。OCT象限分析具有最佳准确性（87.3%）的指标，有4个FP和9个FN。HDLM方案在区分健康可疑眼与早期青光眼眼方面优于标准OCT和VF临床指标。这应该是可能的，以进一步改善这一算法，并与改善，它可能是有用的筛选
Existing summary statistics based upon optical coherence tomography (OCT) scans and/or visual fields (VF) are suboptimal for distinguishing between healthy and glaucomatous eyes in the clinic. This study evaluates the extent to which a hybrid deep learning method (HDLM), combined with a single wide-field OCT protocol, can distinguish eyes previously classified as either healthy suspects or mild glaucoma. 102 eyes from 102 patients, with or suspected open-angle glaucoma, had previously been classified by two glaucoma experts as either glaucomatous (57 eyes) or healthy/suspects (45 eyes). The HDLM had access only to information from a single, wide-field (9×12mm) swept-source OCT scan per patient. Convolutional neural networks were used to extract rich features from maps derived from these scans. Random forest classifier was used to train a model based on these features to predict the existence of glaucomatous damage. The algorithm was compared against traditional OCT and VF metrics. The accuracy of the HDLM ranged from 63.7% to 93.1% depending upon the input map. The RNFL probability map had the best accuracy (93.1%), with 4 false positives, and 3 false negatives. In comparison, the accuracy of the OCT and 24-2 and 10-2 VF metrics ranged from 66.7% to 87.3%. The OCT quadrants analysis had the best accuracy (87.3%) of the metrics, with 4 FP and 9 FN. The HDLM protocol outperforms standard OCT and VF clinical metrics in distinguishing healthy suspect eyes from eyes with early glaucoma. It should be possible to further improve this algorithm and with improvement it might be useful for screening