R L Yang1, Y L Yang2, T Wang3, W Z Xu4, G Yu5, J B Yang1, Q L Sun6, M S Gu7, H B Li8, D H Zhao9, J Y Pei10, T Jiang11, J He12, H Zou13, X M Mao14, G X Geng15, R Qiang16, G L Tian17, Y Wang18, H W Wei19, X G Zhang20, H Wang21, Y P Tian22, L Zou23, Y Y Kong24, Y X Zhou25, M C Ou26, Z R Yao27, Y L Zhou28, W B Zhu29, Y L Huang30, Y H Wang31, C D Huang32, Y Tan33, L Li34, Q Shang35, H Zheng36, S L Lyu37, W J Wang37, Y Yao37, J Le37, Q Shu4. 1. Department of Genetics and Metabolism, the Children's Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China. 2. Department of Pediatrics, Peking University First Hospital, Beijing 100034, China. 3. Center for Reproduction and Genetics, Suzhou Municipal Hospital, Suzhou 215002, China. 4. Department of Cardiac Surgery, the Children's Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China. 5. Department of Information, the Children's Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China. 6. Neonatal Screening Center, Anhui Province Maternity & Child Health Hospital, Hefei 230001, China. 7. Genetics Medicine Center, Xuzhou Maternity and Child Health Hospital, Xuzhou 221000, China. 8. The Central Laboratory of Birth Defects Prevention and Control, Ningbo Women & Children's Hospital, Ningbo 315012, China. 9. Neonatal Screening Center, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China. 10. Department of Child Health Care, Hebei Center for Women and Children's Health, Shijiazhuang 050031, China. 11. Prenatal Diagnosis Center, Nanjing Maternity and Child Health Hospital, Nanjing 210004, China. 12. Department of Maternal Health, Changsha Hospital for Maternal & Child Health Care, Changsha 410001, China. 13. Neonatal Screening Center, Jinan Maternity and Child Care Hospital, Jinan 250000, China. 14. Neonatal Screening Center, Ningxia Maternal and Child Health Care Hospital, Yinchuan 750011, China. 15. Genetic Metabolic Center, Maternity and Child Health Care of Guangxi Zhuang Autonomous Region, Nanning 538001, China. 16. Center for Medical Genetics, Northwest Women's and Children's Hospital, Xi'an 710003, China. 17. Neonatal Screening Center, Children's Hospital of Shanghai, Shanghai 200040, China. 18. Birth Defects Intervention Engineering Laboratory, the 7th Medical Center of the People's Liberation Army General Hospital, Beijing 100000, China. 19. Newborn's Diseases Screening Center, Women & Children's Health Care Hospital of Linyi, Linyi 276016, China. 20. Neonatal Screening Center, Children's Hospital of Shanxi, Taiyuan 030013, China. 21. Center for Genetics, Hunan Provincial Maternal and Child Health Care Hospital, Changsha 410008, China. 22. Center for Birth Defects Prevention and Control Laboratory, the First Medical Center, General Hospital of the People's Liberation Army of China, Beijing 100000, China. 23. Center for Clinical Molecular Medicine, Children's Hospital Affiliated to Chongqing Medical University, Chongqing 400014, China. 24. Department of Child Healthcare, Beijing Maternal and Child Health Care Hospital, Beijing 100010, China. 25. Neonatal Screening Center, Shandong Maternal and Child Health Hospital, Jinan 250000, China. 26. Department of Neonatal Screening, Sichuan Provincial Maternity and Child Health Care Hospital, Chengdu 610000, China. 27. Department of Obstetrics, Changzhi Maternal and Child Health Hospital, Changzhi 046011, China. 28. Neonatal Screening Center, Xiamen Maternal and Child Health Care Hospital, Xiamen 361003, China. 29. Neonatal Screening Center, Fujian Provincial Maternity and Child Health Care Hospital, Fuzhou 350005, China. 30. Neonatal Screening Center, Guangzhou Women and Children's Medical Center, Guangzhou 510000, China. 31. Department of Neonatology, Maternity and Child Care Hospital of Hubei, Wuhan 430070, China. 32. Neonatal Screening Center, Hainan Women and Children's Medical Center, Haikou 570206, China. 33. Department of Reproductive Medicine, the First People's Hospital of Yunnan Province, Kunming 650032, China. 34. Department of Neonatology, Xinjiang Uigur Municipal People's Hospital, Urumqi 830001, China. 35. Department of Rehabilitation Medicine, Henan Children's Hospital, Zhengzhou 450007, China. 36. Department of Pediatrics, the First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou 450000, China. 37. Zhejiang Biosan Biochemical Technologies Co. Ltd, Hangzhou 310000, China.
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.
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.