Deep learning-enabled automatic screening of SLE diseases and LR using OCT images

Optical coherence tomography (OCT) is a noninvasive imaging technique that enables the visualization of tissue microstructure in vivo. Recent studies have suggested that OCT can be used for detecting and monitoring retinal changes over time in patients with systemic lupus erythematosus (SLE), an auto-immune disease that damages various organs, including the eye itself. This research work discusses the potential of using OCT as a screening tool for SLE. OCT provides a detailed view of the retina, allowing the detection of subtle changes that may indicate early-stage SLE-related eye disease to screen SLE patients. The use of OCT as a screening tool may help to identify lupus erythematosus retinopathy (LR) and facilitate earlier interventions, ultimately improving patient outcomes. In addition, we used deep learning-based automated screening using OCT images of SLE patients. We present a novel deep-learning model combining a pre-trained CNN, a multi-scale module, a pooling module, and an FC classifier. Our prediction model for SLE disease has outperformed the state-of-the-art method using the in-house dataset from Peking Union Medical College Hospital. Our model achieved a higher AUC indicating a high correlation between the ground truth and predicted output. However, further studies are needed to determine the sensitivity and specificity of OCT in detecting SLE and to establish appropriate screening protocols for this patient population.