Clinical application of an automatic facial recognition system based on deep learning for diagnosis of Turner syndrome

Purpose Automated facial recognition technology based on deep learning has achieved high accuracy in diagnosing various endocrine diseases and genetic syndromes. This study attempts to establish a facial diagnostic system for Turner syndrome (TS) based on deep convolutional neural networks. Methods Photographs of 207 TS patients and 1074 female controls were collected from July 2016 to April 2019. Finally, 170 patients diagnosed with TS and 1053 female controls were included. Deep convolutional neural networks were used to develop the facial diagnostic system. A prospective study, which included two TS patients and 35 controls, was conducted to test the efficacy in the real clinical setting. Results The average areas under the curve (AUCs) in three different scenarios were 0.9540 +/- 0.0223, 0.9662 +/- 0.0108 and 0.9557 +/- 0.0119, separately. The average sensitivity and specificity of the prospective study were 96.7% and 97.0%, respectively. Conclusions The facial diagnostic system achieved high accuracy. Prospective study results demonstrated the application value of this system, which is promising in the screening of Turner syndrome.