Department of Allergy, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC), 100730, Beijing, China.
State Key Laboratory for Management and Control of Complex Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
Endocrine. 2021 Jun;72(3):865-873. doi: 10.1007/s12020-020-02539-3. Epub 2020 Nov 10.
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.
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.
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.
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.
基于深度学习的自动人脸识别技术在诊断各种内分泌疾病和遗传综合征方面已达到较高的准确性。本研究试图建立基于深度卷积神经网络的特纳综合征（TS）面部诊断系统。
从 2016 年 7 月至 2019 年 4 月，共收集了 207 例 TS 患者和 1074 名女性对照者的照片。最终，纳入了 170 例经诊断为 TS 的患者和 1053 名女性对照者。使用深度卷积神经网络开发了面部诊断系统。进行了一项前瞻性研究，该研究纳入了 2 例 TS 患者和 35 名对照者，以在真实临床环境中测试其疗效。
在三种不同情况下，平均曲线下面积（AUCs）分别为 0.9540±0.0223、0.9662±0.0108 和 0.9557±0.0119。前瞻性研究的平均敏感性和特异性分别为 96.7%和 97.0%。
面部诊断系统具有较高的准确性。前瞻性研究结果表明了该系统的应用价值，其在特纳综合征的筛查中具有广阔的应用前景。
