Jiang Jiewei, Wang Liming, Fu Haoran, Long Erping, Sun Yibin, Li Ruiyang, Li Zhongwen, Zhu Mingmin, Liu Zhenzhen, Chen Jingjing, Lin Zhuoling, Wu Xiaohang, Wang Dongni, Liu Xiyang, Lin Haotian
School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, China.
School of Computer Science and Technology, Xidian University, Xi'an, China.
Ann Transl Med. 2021 Apr;9(7):550. doi: 10.21037/atm-20-6635.
Lens opacity seriously affects the visual development of infants. Slit-illumination images play an irreplaceable role in lens opacity detection; however, these images exhibited varied phenotypes with severe heterogeneity and complexity, particularly among pediatric cataracts. Therefore, it is urgently needed to explore an effective computer-aided method to automatically diagnose heterogeneous lens opacity and to provide appropriate treatment recommendations in a timely manner.
We integrated three different deep learning networks and a cost-sensitive method into an ensemble learning architecture, and then proposed an effective model called CCNN-Ensemble [ensemble of cost-sensitive convolutional neural networks (CNNs)] for automatic lens opacity detection. A total of 470 slit-illumination images of pediatric cataracts were used for training and comparison between the CCNN-Ensemble model and conventional methods. Finally, we used two external datasets (132 independent test images and 79 Internet-based images) to further evaluate the model's generalizability and effectiveness.
Experimental results and comparative analyses demonstrated that the proposed method was superior to conventional approaches and provided clinically meaningful performance in terms of three grading indices of lens opacity: area (specificity and sensitivity; 92.00% and 92.31%), density (93.85% and 91.43%) and opacity location (95.25% and 89.29%). Furthermore, the comparable performance on the independent testing dataset and the internet-based images verified the effectiveness and generalizability of the model. Finally, we developed and implemented a website-based automatic diagnosis software for pediatric cataract grading diagnosis in ophthalmology clinics.
The CCNN-Ensemble method demonstrates higher specificity and sensitivity than conventional methods on multi-source datasets. This study provides a practical strategy for heterogeneous lens opacity diagnosis and has the potential to be applied to the analysis of other medical images.
晶状体混浊严重影响婴儿的视觉发育。裂隙照明图像在晶状体混浊检测中发挥着不可替代的作用；然而，这些图像呈现出多样的表型，具有严重的异质性和复杂性，尤其是在儿童白内障中。因此，迫切需要探索一种有效的计算机辅助方法，以自动诊断异质性晶状体混浊并及时提供适当的治疗建议。
我们将三种不同的深度学习网络和一种成本敏感方法集成到一个集成学习架构中，然后提出了一种名为CCNN-Ensemble[成本敏感卷积神经网络（CNN）集成]的有效模型，用于自动晶状体混浊检测。总共470张儿童白内障的裂隙照明图像用于训练，并在CCNN-Ensemble模型和传统方法之间进行比较。最后，我们使用两个外部数据集（132张独立测试图像和79张基于互联网的图像）进一步评估该模型的通用性和有效性。
实验结果和比较分析表明，所提出的方法优于传统方法，并且在晶状体混浊的三个分级指标方面提供了具有临床意义的性能：面积（特异性和敏感性；92.00%和92.31%）、密度（93.85%和91.43%）和混浊位置（95.25%和89.29%）。此外，在独立测试数据集和基于互联网的图像上的可比性能验证了该模型的有效性和通用性。最后，我们开发并实施了一个基于网站的自动诊断软件，用于眼科诊所的儿童白内障分级诊断。
CCNN-Ensemble方法在多源数据集上比传统方法表现出更高的特异性和敏感性。本研究为异质性晶状体混浊诊断提供了一种实用策略，并且有潜力应用于其他医学图像的分析。
