Prediction of the Reactivation of Retinopathy of Prematurity After Anti-VEGF Treatment Using Machine Learning in Small Numbers

Abstract Aim To create and validate a prediction model for retinopathy of prematurity (ROP) reactivation after anti-VEGF therapy with clinical risk factors and retinal images. Methods Infants with TR-ROP undergoing anti-VEGF treatment were recruited from two hospitals, and three models were constructed using machine learning and deep learning algorithms. The areas under the curve (AUC), sensitivity (SEN) and specificity (SPC) were used to show the performances of the prediction models. Results Finally, we included 87 cases, including 21 with recurrent and 66 nonrecurrent cases. The AUC for the clinical risk factor model was 0.80 and 0.77 in the internal and external validation groups, respectively. The average AUC, sensitivity, and specificity in the internal validation for the retinal image model were 0.82, 0.93, and 0.63, respectively. The SPC, AUC, and SEN for the combined model were 0.73, 0.84, and 0.93, separately. Conclusion We constructed a prediction model for the reactivation of ROP. Using this prediction model, we can optimize strategies for treating TR-TOP infants and developing screening plans after treatment.