Establishment of an auxiliary diagnosis system of newborn screening for inherited metabolic diseases based on artificial intelligence technology and a clinical trial
- VernacularTitle:新生儿遗传代谢病人工智能疾病风险评估模型的建立及验证
- Author:
Rulai YANG
1
;
Yanling YANG
;
Ting WANG
;
Weize XU
;
Gang YU
;
Jianbin YANG
;
Qiaoling SUN
;
Maosheng GU
;
Haibo LI
;
Dehua ZHAO
;
Juying PEI
;
Tao JIANG
;
Jun HE
;
Hui ZOU
;
Xinmei MAO
;
Guoxing GENG
;
Rong QIANG
;
Guoli TIAN
;
Yan WANG
;
Hongwei WEI
;
Xiaogang ZHANG
;
Hua WANG
;
Yaping TIAN
;
Lin ZOU
;
Yuanyuan KONG
;
Yuxia ZHOU
;
Mingcai OU
;
Zerong YAO
;
Yulin ZHOU
;
Wenbin ZHU
;
Yonglan HUANG
;
Yuhong WANG
;
Cidan HUANG
;
Ying TAN
;
Long LI
;
Qing SHANG
;
Hong ZHENG
;
Shaolei LYU
;
Wenjun WANG
;
Yan YAO
;
Jing LE
;
Qiang SHU
Author Information
- Keywords: Infant, newborn; Artificial intelligence; Inherited metabolic diseases; Auxiliary diagnosis system
- From: Chinese Journal of Pediatrics 2021;59(4):286-293
- CountryChina
- Language:Chinese
- Abstract: Objective:To establish a disease risk prediction model for the newborn screening system of inherited metabolic diseases by artificial intelligence technology.Methods:This was a retrospectively study. Newborn screening data ( n=5 907 547) from February 2010 to May 2019 from 31 hospitals in China and verified data ( n=3 028) from 34 hospitals of the same period were collected to establish the artificial intelligence model for the prediction of inherited metabolic diseases in neonates. The validity of the artificial intelligence disease risk prediction model was verified by 360 814 newborns ' screening data from January 2018 to September 2018 through a single-blind experiment. The effectiveness of the artificial intelligence disease risk prediction model was verified by comparing the detection rate of clinically confirmed cases, the positive rate of initial screening and the positive predictive value between the clinicians and the artificial intelligence prediction model of inherited metabolic diseases. Results:A total of 3 665 697 newborns ' screening data were collected including 3 019 cases ' positive data to establish the 16 artificial intelligence models for 32 inherited metabolic diseases. The single-blind experiment ( n=360 814) showed that 45 clinically diagnosed infants were detected by both artificial intelligence model and clinicians. A total of 2 684 cases were positive in tandem mass spectrometry screening and 1 694 cases were with high risk in artificial intelligence prediction model of inherited metabolic diseases, with the positive rates of tandem 0.74% (2 684/360 814)and 0.46% (1 694/360 814), respectively. Compared to clinicians, the positive rate of newborns was reduced by 36.89% (990/2 684) after the application of the artificial intelligence model, and the positive predictive values of clinicians and artificial intelligence prediction model of inherited metabolic diseases were 1.68% (45/2 684) and 2.66% (45/1 694) respectively. Conclusion:An accurate, fast, and the lower false positive rate auxiliary diagnosis system for neonatal inherited metabolic diseases by artificial intelligence technology has been established, which may have an important clinical value.
