[Establishment of an auxiliary diagnosis system of newborn screening for inherited metabolic diseases based on artificial intelligence technology and a clinical trial].

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.

[Association between fruit consumption and stroke in adults in Qingdao: a prospective study].

Objective: To understand the fruit consumption of adults of Qingdao and examine the association between fruit consumption and stroke. Methods: We analyzed baseline data and stroke incidence of the participants who were aged 30-79 years and had been enrolled into the China Kadoorie Biobank (CKB) study from Qingdao city. Cox proportional hazards regression model was conducted to estimate the association of fruit consumption with risk of stroke. Results: A total of 35 509 participants were investgated in the baseline survey. Ratio of male to female was 1∶1.27, and the average age was (50.3±10.2) years. Respondents with higher frequency of fruit consumption were younger, more women, with higher education level and higher income (P<0.05). A total of 1 011 new cases of stroke were observed, with a stroke incidence of 387.63/100 000 person-years. Multivariate Cox regression analysis showed that fruit consumption had a protective effect on stroke incidence. Compared to the respondents who never consumed fruit, respondents who consumed fruit more than 4 days per week had a 44% lower risk of stroke incidence (HR=0.56, 95%CI: 0.50-0.62, P<0.05), and the risk reduced by 46% (HR=0.54, 95%CI: 0.46-0.64, P<0.05) and 42% (HR=0.58, 95%CI: 0.52-0.69, P<0.05) in male and female, respectively. Further adjustment for WC, BMI, SBP and random blood glucose did not change the association. Conclusion: Increasing fruit consumption can effectively decrease the risk of stroke. People should increase fruit consumption advisably to set up reasonable and healthy dietary habits.