Clinical application of remote fetal heart rate monitoring system for AI early warning1

To compare the safety, effectiveness and effect of the AI early warning system and the traditional fetal monitoring system. Methods: Establish and deploy AI remote fetal monitoring system; From January 1, 2022 to December 31, 2022, 440 pregnant women in the third trimester of pregnancy admitted to the Maternal and Child Health Hospital of Haikou City and meeting the admission criteria were selected and randomly assigned to the artificial intelligence early warning remote fetal visit group 220 cases and the traditional group 220 cases. In the AI group, remote fetal monitoring system was used for examination and treatment, while in the traditional group, pregnant women went to the hospital for examination and delivery according to the usual standard procedures. The effectiveness and safety of the AI-early warning impromptu fetal heart test system were observed, and the rates of perinatal adverse outcomes (low birth weight, perinatal death, premature delivery, neonatal asphyxia, cord blood oxygen saturation and fetal distress) of pregnant women in the two groups were compared. Results: In the test of artificial intelligence early warning fetal monitoring system, the AI group detected 332 normal fetal cardiogram cases of pregnant women, 206 suspected pathological changes in 18 cases; In the traditional group, 421 cases of normal fetal cardiogram were detected, 117 cases of suspected pathological changes were detected in 16 cases. The sensitivity and specificity of normal pregnant women (true positive was normal group, false negative was suspicious + lesion) were 81.55% and 96.98% respectively. The sensitivity and specificity of pregnant women with abnormal fetal monitoring map were 100% and 82%. The low birth weight rate was 6/220 in the AI group and 13/220 in the control group, with a total value of 2.695 (p value 0. 101). Perinatal death was 0 in both groups. The rate of preterm infants in AI group was 7/220, compared with 15/220 in control group, and peason2 was 3.062,p value 0.08; Neonatal asphyxia rate was 1/220 in the AI group and 0/220 in the control group, and the exact detection rate by Fisher was p 0. 3 72. The low oxygen saturation rate of umbilical cord blood was 3/220 in the AI group and 2/220 in the control group. The p value of Fisher's accurate detection was 1.0. The fetal distress rate was 50/220 in the AI group and 76/220 in the control group, with a total of 7.518 (p value 0.006). Among the monitored perinatal adverse outcome indicators, only the fetal distress rate was significantly different between the two groups (p =0.05). Conclusions: The AI early-warning remote fetal care system is as safe and effective as the traditional fetal care system, which is helpful to break the time and space limitations of the traditional fetal care system and to sinking high-quality medical resources to the primary care. Remote fetal heart monitoring with ai warning can improve the adverse perinatal outcomes of fetal distress.