Facial Recognition Intensity in Disease Diagnosis Using Automatic Facial Recognition

doi:10.3390/jpm11111172

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化团队

	Facial Recognition Intensity in Disease Diagnosis Using Automatic Facial Recognition
	Wu, Danning 2,3; Chen, Shi 3; Zhang, Yuelun 4; Zhang, Huabing 3; Wang, Qing 5; Li, Jianqiang 6; Fu, Yibo 2; Wang, Shirui 3; Yang, Hongbo 3; Du, Hanze 3
刊名	JOURNAL OF PERSONALIZED MEDICINE
	2021-11-01
卷号	11 期号:11 页码:15
关键词	artificial intelligence computer-aided diagnosis facial phenotypes machine learning complexity theory
DOI	10.3390/jpm11111172
通讯作者	Pan, Hui(panhui20111111@163.com) ; Shen, Zhen(zhen.shen@ia.ac.cn)
英文摘要	Artificial intelligence (AI) technology is widely applied in different medical fields, including the diagnosis of various diseases on the basis of facial phenotypes, but there is no evaluation or quantitative synthesis regarding the performance of artificial intelligence. Here, for the first time, we summarized and quantitatively analyzed studies on the diagnosis of heterogeneous diseases on the basis on facial features. In pooled data from 20 systematically identified studies involving 7 single diseases and 12,557 subjects, quantitative random-effects models revealed a pooled sensitivity of 89% (95% CI 82% to 93%) and a pooled specificity of 92% (95% CI 87% to 95%). A new index, the facial recognition intensity (FRI), was established to describe the complexity of the association of diseases with facial phenotypes. Meta-regression revealed the important contribution of FRI to heterogeneous diagnostic accuracy (p = 0.021), and a similar result was found in subgroup analyses (p = 0.003). An appropriate increase in the training size and the use of deep learning models helped to improve the diagnostic accuracy for diseases with low FRI, although no statistically significant association was found between accuracy and photographic resolution, training size, AI architecture, and number of diseases. In addition, a novel hypothesis is proposed for universal rules in AI performance, providing a new idea that could be explored in other AI applications.
资助项目	Beijing Municipal Natural Science Foundation[7192153] ; National Natural Science Foundation of China[61773382] ; National Natural Science Foundation of China[61872365] ; National Natural Science Foundation of China[U1909218]
WOS关键词	ARTIFICIAL-INTELLIGENCE
WOS研究方向	Health Care Sciences & Services ; General & Internal Medicine
语种	英语
出版者	MDPI
WOS记录号	WOS:000727737800001
资助机构	Beijing Municipal Natural Science Foundation ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46929]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者	Pan, Hui; Shen, Zhen
作者单位	1.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China 2.Chinese Acad Med Sci, Peking Union Med Coll, Peking Union Med Coll Hosp, Eight Year Program Clin Med, Beijing 100730, Peoples R China 3.Chinese Acad Med Sci, Dept Endocrinol, Peking Union Med Coll Hosp, Peking Union Med Coll, Beijing 100730, Peoples R China 4.Chinese Acad Med Sci, Peking Union Med Coll Hosp, Peking Union Med Coll, Med Res Ctr, Beijing 100730, Peoples R China 5.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China 6.Beijing Univ Technol, Sch Software Engn, Beijing 100124, Peoples R China 7.Chinese Acad Med Sci & Peking Union Med Coll, Dept Endocrinol, Key Lab Endocrinol Natl Hlth Commiss, State Key Lab Complex Severe & Rare Dis Peking Un, Beijing 100730, Peoples R China 8.Chinese Acad Sci, Inst Automat, Beijing Engn Res Ctr Intelligent Syst & Technol, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wu, Danning,Chen, Shi,Zhang, Yuelun,et al. Facial Recognition Intensity in Disease Diagnosis Using Automatic Facial Recognition[J]. JOURNAL OF PERSONALIZED MEDICINE,2021,11(11):15.
APA	Wu, Danning.,Chen, Shi.,Zhang, Yuelun.,Zhang, Huabing.,Wang, Qing.,...&Shen, Zhen.(2021).Facial Recognition Intensity in Disease Diagnosis Using Automatic Facial Recognition.JOURNAL OF PERSONALIZED MEDICINE,11(11),15.
MLA	Wu, Danning,et al."Facial Recognition Intensity in Disease Diagnosis Using Automatic Facial Recognition".JOURNAL OF PERSONALIZED MEDICINE 11.11(2021):15.