Objective: To developed an image analysis system of anterior segment optical coherence tomography (AS-OCT) examination results based on deep learning technology, and to evaluate its effect in identifying various types of corneal pathologies and quantified indices. Methods: A total of 4 026 patients (5 617 eyes), including 1 977 males and 2 049 females, aged (45±23) years, were enrolled in Qingdao Eye Hospital from January 2011 to August 2019. The AS-OCT images were used as a training dataset, which were labeled with location information of 16 corneal pathologies (including corneal epithelial defect, corneal epithelial thickening, corneal thinning and so on) by clinical experts, as well as the tissue stratification of the corneal epithelium and stroma. The labeled AS-OCT images were used to train the corneal pathology detection model and corneal stratification model based on deep convolutional neural network algorithm. Then 1 709 AS-OCT images of the affected eyes were collected as a validation dataset. Compared with the artificial labeling results, the accuracy, sensitivity and specificity were evaluated in the corneal pathology detection model, and the overlapping rate (Dice coefficient) between the labeled area of the model and the artificial labeling area was used to evaluate the corneal stratification model. Results: The results of 5 617 training sets showed that there were 1 472 cases of corneal epithelial defect, 2 416 cases of corneal epithelial thickening, 2 001 cases of corneal thinning, 780 cases of corneal lordosis, 2 064 cases of corneal thickening, 358 cases of subepithelial blisters, 486 cases of subepithelial opacity, 1 010 cases of corneal ulcer, 3 635 cases of stromal opacity, 1 060 cases of posterior elastic layer fold, 137 cases of posterior elastic layer detachment, 665 cases of keratic precipitate, 176 cases of corneal perforation, 127 cases of corneal foreign body, 299 cases of after lamellar keratoplasty (LKP) and 234 cases of after penetrating keratoplasty (PKP). Among 1 709 images, 1 596 were manually labeled. The average sensitivity and specificity of the corneal pathology detection model were 96.5% and 96.1% compared with the results of manual labeling. Fifteen samples were missed for detection, and the rate was 0.93%. The average Dice coefficients of the corneal stratification model for the corneal epithelium and stroma were 0.985 and 0.917, respectively. Conclusions: Our artificial intelligence-based diagnosis system with AS-OCT is able to give quantified information and location information of corneal lesions with high accuracy, which can help ophthalmologists improve the efficiency and accuracy of diagnosis. (Chin J Ophthalmol, 2021, 57: 447-453).
目的： 基于深度学习方法开发眼前节相干光层析成像术（AS-OCT）图像分析系统，并评价其对常见角膜病变及特征的自动识别与定位效果。 方法： 收集2011年1月至2019年8月于青岛眼科医院就诊的患者4 026例（5 617只眼），男性1 977例，女性2 049例，年龄（45±23）岁，将其AS-OCT图像作为训练集，由临床医师人工标注角膜上皮缺损、角膜上皮增厚、角膜变薄等16种特征的类型和位置，以及角膜上皮层和基质层的组织分层，用于训练基于深度卷积神经网络算法构建的AS-OCT图像特征识别模型和角膜分层模型。再收集1 709幅患眼AS-OCT图像作为验证集，由模型对特征和角膜分层情况进行识别，并与人工标注结果相比，通过准确度、灵敏度和特异度来评价角膜特征检测模型，采用模型标注区域与人工标注区域的重合率（Dice系数）来评价角膜分层模型。 结果： 5 617幅训练集人工对角膜特征的标注结果（训练数量）为角膜上皮缺损1 472例、角膜上皮增厚2 416例、角膜变薄2 001例、角膜前凸780例、角膜增厚2 064例、上皮下水泡358例、上皮下混浊486例、角膜溃疡1 010例、基质混浊3 635例、后弹力层褶皱1 060例、后弹力层脱离137例、角膜后沉积物665例、角膜穿孔176例、角膜异物127例、LKP术后299例、PKP术后234例。验证集中1 709幅图像中1 596幅被人工标注特征，角膜特征检测模型对16种特征的检测结果与人工标注结果相比，平均灵敏度为96.5%，平均特异度为96.1%；15幅图像存在特征漏检，漏检率为0.93%。角膜分层模型对于角膜上皮层和基质层分割的平均Dice系数分别为0.985及0.917。 结论： 该系统可为医师提供AS-OCT图像中角膜特征的类型和位置信息，准确率较高，可以帮助眼科医师提升诊断效率及准确性。（中华眼科杂志，2021，57：447-453）.