Artificial intelligence, which has been applied as an innovative technology in multiple fields of healthcare, analyzes large amounts of data to assist in disease prediction, prevention, and diagnosis, as well as in patient monitoring. In obstetrics, artificial intelligence has been actively applied and integrated into our daily medical practice. This review provides an overview of artificial intelligence systems currently used for obstetric diagnostic purposes, such as fetal cardiotocography, ultrasonography, and magnetic resonance imaging, and demonstrates how these methods have been developed and clinically applied.

Objective: To examine the prevalence of novel newborn types among 165 million live births in 23 countries from 2000 to 2021. Design: Population‐based, multi‐country analysis. Setting: National data systems in 23 middle‐ and high‐income countries. Population: Liveborn infants. Methods: Country teams with high‐quality data were invited to be part of the Vulnerable Newborn Measurement Collaboration. We classified live births by six newborn types based on gestational age information (preterm <37 weeks versus term ≥37 weeks) and size for gestational age defined as small (SGA, 90th centile) for gestational age, according to INTERGROWTH‐21st standards. We considered small newborn types of any combination of preterm or SGA, and term + LGA was considered large. Time trends were analysed using 3‐year moving averages for small and large types. Main outcome measures: Prevalence of six newborn types. Results: We analysed 165 017 419 live births and the median prevalence of small types was 11.7% – highest in Malaysia (26%) and Qatar (15.7%). Overall, 18.1% of newborns were large (term + LGA) and was highest in Estonia 28.8% and Denmark 25.9%. Time trends of small and large infants were relatively stable in most countries. Conclusions: The distribution of newborn types varies across the 23 middle‐ and high‐income countries. Small newborn types were highest in west Asian countries and large types were highest in Europe. To better understand the global patterns of these novel newborn types, more information is needed, especially from low‐ and middle‐income countries.

This study aimed to investigate trends in the rate of cesarean sections (CSs) in South Korea from 2006 to 2015 and identify the risk factors associated with these changes. Using the National Health Insurance Corporation dataset, all women who gave birth between 2006 and 2015 were included in the study. We investigated 1) the mode of delivery, 2) the complication rates during pregnancy (i.e., preeclampsia and placenta previa), and 3) pre-pregnancy factors (body mass index, hypertension [HTN], diabetes mellitus [DM], and other pre-existing medical conditions), and their trends during the study period. Over 10 years, the rate of CS increased from 36.3% in 2006 to 40.6% in 2015 (P<0.01). The rate of CS increased in primiparous women, women with multiple pregnancy, and women with preeclampsia. Maternal age and the incidence of placenta previa also increased. In contrast, the rate of vacuum deliveries and vaginal birth after CS decreased during the study period. The rate of women with pre-pregnancy obesity and DM increased, but the rate of women with pre-pregnancy HTN decreased. The rate of CS in South Korea increased from 2006 to 2015. This trend may reflect changes in the rate of different risk factors. Identifying the causes of the increasing CS trend observed in this study will allow clinicians to monitor these factors and possibly reduce the rate of CS.