[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.

[Analysis of four carnitine-acylcarnitine translocase deficiency cases caused by homozygous mutation of SLC25A20 c.199-10T> G].

Objective: To investigate the clinical, biochemical and genetic features of four carnitine-acylcarnitine translocase deficiency cases. Methods: Four cases diagnosed with carnitine-acylcarnitine translocase deficiency from Guangxi Maternal and Child Health Hospital were studied. DNA was extracted from dry blood filter for gene analysis. SLC25A20 gene analysis was performed in 1 case and the whole exon sequence analysis was performed in 3 cases. Results: Retrospective study on unrelated carnitine-acylcarnitine translocase deficiency patients, the age of onset was 1-28 d, the age of death were 1.5-30 d, main clinical features were hypoglycemia (4 cases), arrhythmia (2 cases), sudden death (2 cases). Biochemical test showed hypoglycemia (1.2-2.0 mmol/L) , elevated creatine kinase (955-8 361 U/L) and creatine kinase isozyme(199-360 U/L), normal or decreased free carnitine level (3.70-27.07 µmol/L) , elevated long-chain acylcarnitine (palmityl carnitine 1.85-14.84 µmol/L). The gene tests showed that all 4 cases carried SLC25A20 gene c.199-10T> G homozygous mutation, inherited from their parents. By analyzing the haplotype, we found that the mutation loci of C. 199-10T> G were all in the same haplotype. Conclusion: The c.199-10T> G mutation is an important molecular cause of carnitine-acylcarnitine translocase deficiency, which has relatively high frequency in Guangxi population, and is related to the founder effect.