Objective:To investigate the diagnosis and treatment of abdominal cocoon (AC).Methods:The clinical manifestations, findings during surgery, treatments, and follow-up results of 26 cases of AC were retrospectively studied from Jan 2001 to Jan 2019.Results:All of 26 cases were diagnosed as AC definitely by laparotomy, and were categorized into 2 types: type Ⅰ is of absence of second enterocoelia (18 cases, 69%) while type Ⅱ shows second enterocoelia (8cases, 31%). Twenty cases (12 type Ⅰ, 8 type Ⅱ) underwent membrane excision and careful enterodialysis to release the small intestine entirely or partially, while the other 6 cases (all were type Ⅰ) did not. All the patients recovered smoothly.Conclusions:AC can be categorized into two types, Surgery is recommended for type Ⅱ and part of type Ⅰ with severe complications.

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.