Table 1_Automatic diagnosis of retinoblastoma and quantitative analysis of retinal vascular morphology using deep learning.docx

Purpose<p>To develop a convolutional neural network-based artificial intelligence identification diagnostic system for ultra-wide field fundus images of retinoblastoma (RB) and quantify its vascular morphology features.</p>Methods<p>A retrospective study. 183 ultra-wide field fundus images from 54 patients diagnosed with RB were included, along with 250 images of healthy control. Of these, 277 images were used as the training set, 100 images were used as the validation set, and 56 images were used for the performance test.

A deep learning based on DenseNet was used to train the AI auto-diagnostic network for RB. After manual labelling to remove the lesion area, the binary skeleton vessels of the retina were automatically extracted and segmented. Vascular morphological characteristics were subsequently assessed quantitatively using the corresponding mathematical model.</p>Results<p>The diagnostic performance of the network was evaluated using sensitivity, specificity and F1 score, respectively.