The aim of this study was to investigate the detection ability of multi-task model based on deep learning combined with optic disc change classification in glaucomatous optic neuropathy.

21 969 participants (32 548 eyes) were included from medical institutions of 29 Provinces, municipalities and autonomous regions from August 2017 to August 2018. There were 12 762 male and 9207 female, and the age range was 17 to 75 with the average age (57.1±7.9) years-old. The two-level group diagnosed and labeled the fundus images as normal eyes and glaucomatous eyes, and divided them into training set, homologous testing set and two non-homologous testing sets. Our research was based on Tencent Miying Fundus Photo Auxiliary Diagnosis System using Python 3.6 and Pytorch 0.4. We trained our model with NVIDIA Tesla P40 as running chip. Using the multi-task convolution neural network model based on ResNet 34, with the main task of glaucoma classification and the auxiliary task of optic disc atrophy, the model with the best performance in the optimization set was selected as the final model. Moreover, class activation map was used to explain the feature areas of the model. The sensitivity, specificity and area under the curve are used to predict and evaluate its performance.

The sensitivity of the model was95.9%, 95.4% and 95.7%, respectively; specificity of that was 97.7%, 91.6% and 92.2%, respectively; area under the curve of that was 0.993, 0.968 and 0.974, respectively. The sensitivity and specificity of the model were effectively improved after the introduction of multi-task model of optic disc atrophy.

The multi-task model based on deep learning combined with optic disc change classification has a high accuracy for the detection of glaucomatous optic neuropathy. Moreover, the ability of this model to distinguish high myopia from glaucoma was explored and analyzed by interpretable experiment.