Abstract
Thyroid-associated ophthalmopathy (TAO) is a very common autoimmune orbital disease. Approximately 4%-8% of TAO patients will deteriorate and develop the most severe dysthyroid optic neuropathy (DON). According to the current data provided by clinical experts, there is still a certain proportion of suspected DON patients who cannot be diagnosed, and the clinical evaluation has low sensitivity and specificity. There is an urgent need for an efficient and accurate method to assist physicians in identifying DON. This study proposes a hybrid deep learning model to accurately identify suspected DON patients using computed tomography (CT). The hybrid model is mainly composed of the double multiscale and multi attention fusion module (DMs-MAFM) and a deep convolutional neural network. The DMs-MAFM is the feature extraction module proposed in this study, and it contains a multiscale feature fusion algorithm and improved channel attention and spatial attention, which can capture the features of tiny objects in the images. Multiscale feature fusion is combined with an attention mechanism to form a multilevel feature extraction module. The multiscale fusion algorithm can aggregate different receptive field features, and then fully obtain the channel and spatial correlation of the feature map through the multiscale channel attention aggregation module and spatial attention module, respectively. According to the experimental results, the hybrid model proposed in this study can accurately identify suspected DON patients, with Accuracy reaching 96%, Specificity reaching 99.5%, Sensitivity reaching 94%, Precision reaching 98.9% and F1-score reaching 96.4%. According to the evaluation by experts, the hybrid model proposed in this study has some enlightening significance for the diagnosis and prediction of clinically suspect DON.