Application and significance of artificial intelligence technology in nutritional management of gestational diabetes mellitus
10.3760/cma.j.cn431274-20211009-01046
- VernacularTitle:人工智能技术在妊娠期糖尿病营养管理中的应用及其意义
- Author:
Hua LI
1
;
Wenxia LI
;
Qiuling CHEN
;
Yanxia DENG
;
Yongqi LI
Author Information
1. 长沙市妇幼保健院产科，长沙 410007
- Keywords:
Diabetes, gestational;
Artificial intelligence;
Nutrition management
- From:
Journal of Chinese Physician
2022;24(5):719-722
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the application and effect of artificial intelligence technology in perinatal management of gestational diabetes mellitus.Methods:240 pregnant women with gestational diabetes diagnosed during 24-26 weeks of pregnancy in Changsha Maternal and Child Health Hospital were prospectively selected and randomly divided into control group (120 cases) and observation group (120 cases). The control group used the traditional management mode for nutritional management, and the observation group used AI technology for nutritional management. The weight gain, blood glucose control level, insulin use, pregnancy complications, pregnancy outcome and other indicators of the two groups were compared.Results:(1) Monitoring indicators during pregnancy: there was no significant difference in weight gain between the two groups ( P>0.05). The proportion of weight gain in the appropriate range in the observation group was significantly higher than that in the control group ( P<0.05); The prevalence of full-term anemia, insulin use rate and the incidence of blood glucose exceeding the control standard in the observation group were significantly higher than those in the control group (all P<0.05). (2) Pregnancy outcome: there was no significant difference in the incidence of gestational hypertension, cesarean section, fetal growth restriction, premature delivery and neonatal hypoglycemia between the two groups (all P>0.05); The incidence of conversion to cesarean section, macrosomia, neonatal blood glucose <2.6 mmol/L, mild asphyxia and admission to neurosurgical intensive care unit (NICU) in the observation group were significantly higher than those in the control group (all P<0.05). Conclusions:Application of AI technology to nutritional management of gestational diabetes can better control the maternal perinatal weight gain and blood glucose level, reduce the incidence of anemia in the third trimester of pregnancy, the incidence of macrosomia, the use of insulin and the rate of conversion to cesarean section, and improve the neonatal outcome.
