It is important to detect recurrent retinal detachment (RD) among patients after retinal reattachment surgery. The application of deep learning (DL) methods to detect recurrent RD with ultra-widefield (UWF) fundus images is promising, but the feasibility and efficiency have not been studied. A DL system with ResNet-50 and Inception-ResNet-V2 is developed and internally validated to identify recurrent RD and retina reattachment after surgery. The performance is further validated and compared with human ophthalmologists in a prospective dataset assessed by area under curve (AUC), accuracy, sensitivity, and specificity. Five hundred fifty-four UWF fundus images from 173 RD patients (mean [standard deviation] age: 39.2 +/- 16.2 years; male: 115 [66.5%]) are used to develop the DL system. DL shows AUCs of 0.912 (95% confidence interval [CI]: 0.855-0.968) and 0.906 (95% CI: 0.818-0.995) for the two models. Eighty-nine UWF fundus images from 23 RD patients (mean [standard deviation] age: 31.4 +/- 12.3 years; male: 15 [65.2%]) are collected as prospective dataset. DL also shows the ability to detect recurrent RD with the AUCs of 0.929 and 0.930 for the two models, respectively. DL reaches a similar and even better diagnostic performance than junior ophthalmologists and performs much better than medical students.