Eye Hospital, The First Affiliated Hospital of Harbin Medical University, No.143, Yiman Street, Nangang District, Harbin City, 150001, Heilongjiang Province, China.
Key Laboratory of Basic and Clinical Research of Heilongjiang Province, Harbin, 150001, China.
Sci Rep. 2022 Jan 7;12(1):264. doi: 10.1038/s41598-021-04006-z.
Diabetes can cause microvessel impairment. However, these conjunctival pathological changes are not easily recognized, limiting their potential as independent diagnostic indicators. Therefore, we designed a deep learning model to explore the relationship between conjunctival features and diabetes, and to advance automated identification of diabetes through conjunctival images. Images were collected from patients with type 2 diabetes and healthy volunteers. A hierarchical multi-tasking network model (HMT-Net) was developed using conjunctival images, and the model was systematically evaluated and compared with other algorithms. The sensitivity, specificity, and accuracy of the HMT-Net model to identify diabetes were 78.70%, 69.08%, and 75.15%, respectively. The performance of the HMT-Net model was significantly better than that of ophthalmologists. The model allowed sensitive and rapid discrimination by assessment of conjunctival images and can be potentially useful for identifying diabetes.
糖尿病可引起微血管损伤。然而，这些结膜病理变化不容易被识别，限制了它们作为独立诊断指标的潜力。因此，我们设计了一个深度学习模型来探索结膜特征与糖尿病之间的关系，并通过结膜图像来实现糖尿病的自动识别。从 2 型糖尿病患者和健康志愿者中采集图像。使用结膜图像开发了分层多任务网络模型 (HMT-Net)，并对该模型进行了系统评估，并与其他算法进行了比较。HMT-Net 模型识别糖尿病的灵敏度、特异性和准确率分别为 78.70%、69.08%和 75.15%。HMT-Net 模型的性能明显优于眼科医生。该模型通过评估结膜图像可以实现敏感和快速的区分，可能有助于识别糖尿病。
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