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

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Abstract

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.

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Data availability

The statistical data are available on request. The photographs are not available due to the privacy of participants.

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Funding

This work was supported by the Beijing Municipal Natural Science Foundation (Grant No. 7192153), the National Natural Science Foundation of China (Grant No. 81673184), the CAMS Initiative for Innovative Medicine (2016-I2M-1–008), the National Natural Science Foundation of China (Grants Nos. 61773382, U190920015 & 61773381), and the CAS Key Technology Talent Program (Z.S.).

Author information

Author notes

  1. These authors contributed equally: Zhouxian Pan, Zhen Shen

Authors and Affiliations

  1. Department of Allergy, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC), 100730, Beijing, China

    Zhouxian Pan

  2. 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

    Zhen Shen, Yin Bao, Lulu Niu, Xisong Dong, Xiuqin Shang & Gang Xiong

  3. Qingdao Academy of Intelligent Industries, 266109, Qingdao, China

    Zhen Shen

  4. Department of Endocrinology, Endocrine Key Laboratory of Ministry of Health, PUMCH, CAMS & PUMC, 100730, Beijing, China

    Huijuan Zhu, Siyu Liang, Shirui Wang, Xiangying Li, Shi Chen & Hui Pan

  5. School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK

    Yin Bao

  6. School of Artificial Intelligence, University of Chinese Academy of Sciences, 101408, Beijing, China

    Lulu Niu

  7. Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing, Cloud Computing Center, Chinese Academy of Sciences, 523808, Dongguan, China

    Gang Xiong

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  1. Zhouxian Pan
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  2. Zhen Shen
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  3. Huijuan Zhu
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  7. Xiangying Li
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  8. Lulu Niu
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  10. Xiuqin Shang
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  11. Shi Chen
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  12. Hui Pan
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  13. Gang Xiong
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Contributions

The first draft of the paper was written by Z.P., and all authors commented on previous versions of the paper. Material preparation and data collection were performed by X.L., Z.P., S.L., and S.W. Y.B., Z.S., L.N., X.D., and X.S. were responsible for the establishment of the automatic classification system. Y.B. and L.N. wrote and run the process of face classification using a computer. Statistical analyses were performed by Z.P. S.C., H.P., G.X. and H.Z. designed the study and provided guidance. All authors contributed to the study conception and design. All authors read and approved the final paper.

Corresponding authors

Correspondence to Shi Chen, Hui Pan or Gang Xiong.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The ethical approval for the present study was obtained from the Institutional Review Board of Chinese Academy of Medical Sciences, PUMCH (Project No: ZS-1242). The study procedures were performed in accordance with the approved guidelines.

Informed consent

All patients provided written informed consent. In the case of minors, consent is provided by parents or legal guardians.

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Pan, Z., Shen, Z., Zhu, H. et al. Clinical application of an automatic facial recognition system based on deep learning for diagnosis of Turner syndrome. Endocrine 72, 865–873 (2021). https://doi.org/10.1007/s12020-020-02539-3

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  • DOI: https://doi.org/10.1007/s12020-020-02539-3

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