Validation of Artificial Intelligence based Software in prediction of fetal distress

Background: Fetal distress is also known as an emergency pregnancy, labor, and delivery complication where a baby experiences oxygen deprivation. Decreased fetal movement in the womb and an abnormal fetal heart rate, abnormal amniotic fluid levels, abnormal results of biophysical profile, insufficient or excessive maternal weight gain are few signs of fetal distress. Fetal distress increases the concern of the obstetrician about the fetal condition and can help in an immediate intervention like cesarean section or instrumental vaginal delivery in order to prevent fetal death.

Objectives: The Main aim of this study is to validate the Artificial Intelligence based Software for prediction of fetal distress.

Methods:  This is a Prospective observational study. Pregnant women will be admitted for delivery in the labor unit of Bharati Vidyapeeth Deemed University Medical College, Pune, who are willing to participate in the study. Singleton pregnancy with an estimated gestational age of more than 28 weeks will be included. Informed consent will be obtained from the patients willing to participate in the study and those satisfying the inclusion criteria.  The accuracy of prediction of fetal distress cases will be done by using a fetal monitoring device that provides a digitized data for FHR and UC directly or indirectly to the  “DAKSH†application/ dashboard from which the application will categorize the cases into 3 classes.

Expected Outcomes:

1. The prediction will help us in categorizing a patient with potential fetal distress, hereby helping the doctor in taking a faster-informed decision.
2. The outcome will also be able to help in improving the accuracy of the predictive model