Wu Xing, Xu Di, Ma Tong, Li Zhao Hui, Ye Zi, Wang Fei, Gao Xiang Yang, Wang Bin, Chen Yu Zhong, Wang Zhao Hui, Chen Ji Li, Hu Yun Tao, Ge Zong Yuan, Wang Da Jiang, Zeng Qiang
Senior Department of Ophthalmology, The Third Medical Center of Chinese PLA General Hospital, Beijing, China.
Beijing Airdoc Technology Co., Ltd., Beijing, China.
Front Cell Dev Biol. 2022 Jul 22;10:906042. doi: 10.3389/fcell.2022.906042. eCollection 2022.
Cataract is the leading cause of blindness worldwide. In order to achieve large-scale cataract screening and remarkable performance, several studies have applied artificial intelligence (AI) to cataract detection based on fundus images. However, the fundus images they used are original from normal optical circumstances, which is less impractical due to the existence of poor-quality fundus images for inappropriate optical conditions in actual scenarios. Furthermore, these poor-quality images are easily mistaken as cataracts because both show fuzzy imaging characteristics, which may decline the performance of cataract detection. Therefore, we aimed to develop and validate an antiinterference AI model for rapid and efficient diagnosis based on fundus images. The datasets (including both cataract and noncataract labels) were derived from the Chinese PLA general hospital. The antiinterference AI model consisted of two AI submodules, a quality recognition model for cataract labeling and a convolutional neural networks-based model for cataract classification. The quality recognition model was performed to distinguish poor-quality images from normal-quality images and further generate the pseudo labels related to image quality for noncataract. Through this, the original binary-class label (cataract and noncataract) was adjusted to three categories (cataract, noncataract with normal-quality images, and noncataract with poor-quality images), which could be used to guide the model to distinguish cataract from suspected cataract fundus images. In the cataract classification stage, the convolutional-neural-network-based model was proposed to classify cataracts based on the label of the previous stage. The performance of the model was internally validated and externally tested in real-world settings, and the evaluation indicators included area under the receiver operating curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). In the internal and external validation, the antiinterference AI model showed robust performance in cataract diagnosis (three classifications with AUCs >91%, ACCs >84%, SENs >71%, and SPEs >89%). Compared with the model that was trained on the binary-class label, the antiinterference cataract model improved its performance by 10%. We proposed an efficient antiinterference AI model for cataract diagnosis, which could achieve accurate cataract screening even with the interference of poor-quality images and help the government formulate a more accurate aid policy.
白内障是全球失明的主要原因。为了实现大规模白内障筛查并取得显著成效，多项研究已将人工智能（AI）应用于基于眼底图像的白内障检测。然而，他们使用的眼底图像源自正常光学条件，由于实际场景中存在因光学条件不佳导致的低质量眼底图像，这种方法不太实用。此外，这些低质量图像很容易被误认为是白内障，因为两者都呈现出模糊成像特征，这可能会降低白内障检测的性能。因此，我们旨在开发并验证一种基于眼底图像的抗干扰AI模型，用于快速高效的诊断。数据集（包括白内障和非白内障标签）来自中国人民解放军总医院。抗干扰AI模型由两个AI子模块组成，一个用于白内障标注的质量识别模型和一个基于卷积神经网络的白内障分类模型。质量识别模型用于区分低质量图像和正常质量图像，并进一步为非白内障生成与图像质量相关的伪标签。通过这种方式，原始的二分类标签（白内障和非白内障）被调整为三类（白内障、正常质量图像的非白内障和低质量图像的非白内障），这可用于指导模型区分白内障与疑似白内障的眼底图像。在白内障分类阶段，提出基于卷积神经网络的模型根据上一阶段的标签对白内障进行分类。该模型的性能在实际环境中进行了内部验证和外部测试，评估指标包括受试者工作特征曲线下面积（AUC）、准确率（ACC）、灵敏度（SEN）和特异度（SPE）。在内部和外部验证中，抗干扰AI模型在白内障诊断中表现出强大性能（三种分类的AUC>91%，ACC>84%，SEN>71%，SPE>89%）。与基于二分类标签训练的模型相比，抗干扰白内障模型的性能提高了10%。我们提出了一种用于白内障诊断的高效抗干扰AI模型，即使在低质量图像的干扰下也能实现准确的白内障筛查，并有助于政府制定更准确的援助政策。
