Prediction of mode of delivery using the first ultrasound-based “intrapartum app”

Abstract

groups, indicating that this condition has been common for decades. This finding is supported by data from hospital records and published data that pregnancy and miscarriage under age 14 is substantially higher in Izabal 4,5 compared with Guatemala City and other major cities in the country. Puerto Barrios is located on the Caribbean coast and is geographically isolated from the rest of Guatemala. The Puerto Barrios study population was recruited from a public, urban hospital in a city of over 106,000 inhabitants, made up of admixed Amerindians/Europeans, Native Mayans (mostly speaking Q'eqchi'), and Garifuna, a population of African origin. The diversity of the population makes a genetic cause unlikely. Several plausible environmental causes may contribute to the earlier observed age at menarche in our study population, including water contamination, plant toxins related to the estrogenic compound zearalenone-a mycotoxin produced by numerous species of Fusarium growing on corn, or fumonisin, which is more prevelant in lowland areas of Guatemala, including Puerto Barrios. 6 Precocious menarche is associated with adverse reproductive outcomes in young women, including an early age of pregnancy or miscarriage, and the early age at menarche we describe warrants further investigation. Understanding the factors contributing to precocious menarche in this population may be useful in helping to reduce adverse reproductive outcomes in Guatemala.

The views of pregnant women, midwives, and a women's panel on intrapartum ultrasound research: A pilot study

“…This allows healthcare professionals to input maternal characteristics and ultrasound values for nulliparous women in the first stage of labour, and the output is the likelihood of vaginal delivery, aligning with pregnant women's view that knowing their likelihood of Caesarean section would be ‘Extremely valuable'. The validity of the risk prediction derived from the app has been recently validated in a different obstetric population …”

Exploring Machine Learning Algorithms to Find the Best Features for Predicting Modes of Childbirth

“…Some research related to pregnancy has been carried out in the past, as briefly described in the literature review section. Most previous works focused on determining pregnancy outcomes based on certain factors; for example, Usman and colleagues [21] and Khazardoost and coworkers [22] predicted the outcomes of deliveries using ultrasound measurements. Similarly, Pereira and team [20] predicted the mode of delivery using obstetric risk factors.…”

“…Few studies have considered the prediction of pregnancy outcomes and modes of delivery, and even fewer clinical decision-support systems have been developed in accordance with such research; for example, Pereira and colleagues [20] conducted a study to design data mining classification models to predict types of delivery using obstetric risk factors in real time. Usman and coworkers [21] conducted a study based on ultrasound technology and developed an Android application called "Intrapartum," which allows medical professionals to estimate the likelihood of a normal delivery. Other studies are reviewed in the Section II.…”

Intrapartum ultrasound and the choice between assisted vaginal and cesarean delivery

“…19,32 A prediction model combining clinical parameters (maternal age, gestational age, maternal body mass index, presence or absence of prolonged labor) and sonographic parameters (head perineum distance, caput succedaneum, occiput posterior position) showed promising results in predicting women at first stage with low likelihood of vaginal delivery. 60 We propose an algorithm model using a concept of the "traffic light" when assessing women for instrumental vaginal delivery, based on sonographic parameters with reference to current evidence from the literature, while taking into account the clinical characteristics.…”