Evidence map›Paper›PMID 33170449›Full record

ArticleEndocrine2021

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

Zhouxian Pan, Zhen Shen, Huijuan Zhu, Yin Bao, Siyu Liang, Shirui Wang, Xiangying Li, Lulu Niu, Xisong Dong, Xiuqin Shang and 3 more

Abstract read
PubMed Publisher
In one paragraph

Article in Endocrine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
4.3field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 40 citations in OpenAlex.

  1. Review
  2. Article
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  7. Automatic Identification of Depression Using Facial Images with Deep Convolutional Neural Network.Medical science monitor : international medical journal of experimental and clinical research · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 6 institutions in 2 countries.

Zhouxian Pan *Department of Allergy, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC), 100730, Beijing, China.
Zhen Shen *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.
Huijuan ZhuDepartment of Endocrinology, Endocrine Key Laboratory of Ministry of Health, PUMCH, CAMS & PUMC, 100730, Beijing, China.
Yin BaoState 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.
Siyu LiangDepartment of Endocrinology, Endocrine Key Laboratory of Ministry of Health, PUMCH, CAMS & PUMC, 100730, Beijing, China.
Shirui WangDepartment of Endocrinology, Endocrine Key Laboratory of Ministry of Health, PUMCH, CAMS & PUMC, 100730, Beijing, China.
Xiangying LiDepartment of Endocrinology, Endocrine Key Laboratory of Ministry of Health, PUMCH, CAMS & PUMC, 100730, Beijing, China.
Lulu NiuState 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.
Xisong DongState 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.
Xiuqin ShangState 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.
Shi ChenDepartment of Endocrinology, Endocrine Key Laboratory of Ministry of Health, PUMCH, CAMS & PUMC, 100730, Beijing, China. cspumch@163.com.
Hui PanDepartment of Endocrinology, Endocrine Key Laboratory of Ministry of Health, PUMCH, CAMS & PUMC, 100730, Beijing, China. panhui20111111@163.com.
Gang XiongState 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. gang.xiong@ia.ac.cn.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNChinese Academy of Sciences · CNBeijing Academy of Artificial Intelligence · CNPeking Union Medical College Hospital · CNShandong Institute of Automation · CNUniversity of Southampton · GB

Funding

Beijing Municipal Natural Science Foundation 7192153National Natural Science Foundation of China 81673184the CAMS Initiative for Innovative Medicine 2016-I2M-1-008
6 · The paper itself

Abstract

purposeAutomated 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.

methodsPhotographs 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.

resultsThe 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.

conclusionsThe 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.

Indexed as

Deep LearningFacial RecognitionTurner SyndromeFemaleHumansNeural Networks, ComputerProspective StudiesDeep convolutional neural networkFacial pattern recognitionProspective studyTurner syndrome

Identifiers

PMID33170449
OpenAlexW3100323964

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.