Clinical Application of an Automatic Facial Recognition System Based on Deep Learning for Diagnosis of Turner Syndrome

Overview

Journal Endocrine

Specialty Endocrinology

Date 2020 Nov 10

PMID 33170449

Citations 13

Authors

Zhouxian Pan

Zhen Shen

Huijuan Zhu

Yin Bao

Siyu Liang

Shirui Wang

Xiangying Li

Lulu Niu

Xisong Dong

Xiuqin Shang

Shi Chen

Hui Pan

Gang Xiong

Affiliations

Soon will be listed here.

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.

Citing Articles

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Computer aided diagnosis of neurodevelopmental disorders and genetic syndromes based on facial images - A systematic literature review.

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