Deep neural networks for prediction and detection of ocular sequelae among survivors of Stevens-Johnson syndrome/toxic epidermal necrolysis

Ocular complications, including recurrent corneal inflammation, progressive corneal neovascularization, conjunctival epithelial ingrowth, and permanent vision loss, are the major long-lasting sequelae of Stevens-Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN), a species of adverse drug reactions. As such, predicting the severity of ocular complications in chronic stage SJS/TEN allows for optimized ocular prognosis. In this study, with external eye photos, fluorescein staining, and photos of slit lamp examination during acute stage and chronic stage SJS/TEN, we first predicted ocular prognosis of chronic stage SJS/TEN using acute stage images, and then determined the severity of ocular complications of chronic stage SJS/TEN using chronic stage images, so as to facilitate the management of ocular complications of SJS/TEN with convolutional neural networks (CNNs). In our study, the prediction of chronic stage ocular complications of SJS/TEN during acute stage, and automatic detection of ocular complications of SJS/TEN during chronic stage, were both clinically satisfying when using external eye photos and photos of slit lamp examination for Schirmer’s test values, superficial punctate keratopathy (SPK), hyperemia, and Ocular Surface Grading Score (OSGS), but not for best corrected visual acuity (BCVA). Moreover, since the CNN model may accurately detect the age and sex of patients with the photos, it is suggested that our model possessed high validity. In conclusion, CNN-based multi-class classification may both predict ocular complications of SJS/TEN before their onset, and accurately detect them upon the development of these sequelae.