Identifying diabetes from conjunctival images using a novel hierarchical multi-task network

作者：Li, Xinyue; Xia, Chenjie; Li, Xin; Wei, Shuangqing; Zhou, Sujun; Yu, Xuhui; Gao, Jiayue; Cao, Yanpeng^*; Zhang, Hong^*

来源：Scientific Reports, 2022, 12(1): 264.

DOI：10.1038/s41598-021-04006-z

• RETINOPATHY
• MELLITUS

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.

• 单位
  上海交通大学; 浙江大学; 哈尔滨医科大学