Hospitalizations for Obstetrical Conditions During and After the Covid-19 Pandemic.

BACKGROUND: During the Covid-19 pandemic there have been a drastic decrease in hospitalizations for non-Covid conditions. The aim of this study was to evaluate the trend in hospitalizations for obstetrical conditions during and after the Covid-19 pandemic. METHODS: For this study there we used electronical data base in order to search for all the obstetrical patients that were hospitalized in a tertiary maternity, Clinical Emergency County Hospital Craiova, during the pre-pandemic period (between March - December 2019), during pandemics (2020 March - December, 2021 March - December) and post pandemics (2022 March - December). RESULTS: The total number of hospitalizations during 2020 dropped by 28% compared to the pre-pandemic year - 2019, and further by 30% in 2021, and by 26% in 2022. In terms of day admissions, a decreasing trend can be observed, with a total of 3230 admissions, from which, 208 in 2020 showing a decrease of 93%, 695 in 2021 with a decrease of 78% and 941 in 2022 with a decrease of 70% compared to 2019.We experienced a significant increase of vaginal birth rate during the pandemic (2020-2021) of 24% that can be attributed to the unavailability of many surrounding low-risk birth units during the pandemic. CONCLUSION: The obstetrical conditions hospitalizations dramatically dropped during the COVID-19 pandemic and have not yet recovered to the pre-pandemic level.

Artificial Intelligence in Obstetric Anomaly Scan: Heart and Brain.

BACKGROUND: The ultrasound scan represents the first tool that obstetricians use in fetal evaluation, but sometimes, it can be limited by mobility or fetal position, excessive thickness of the maternal abdominal wall, or the presence of post-surgical scars on the maternal abdominal wall. Artificial intelligence (AI) has already been effectively used to measure biometric parameters, automatically recognize standard planes of fetal ultrasound evaluation, and for disease diagnosis, which helps conventional imaging methods. The usage of information, ultrasound scan images, and a machine learning program create an algorithm capable of assisting healthcare providers by reducing the workload, reducing the duration of the examination, and increasing the correct diagnosis capability. The recent remarkable expansion in the use of electronic medical records and diagnostic imaging coincides with the enormous success of machine learning algorithms in image identification tasks. OBJECTIVES: We aim to review the most relevant studies based on deep learning in ultrasound anomaly scan evaluation of the most complex fetal systems (heart and brain), which enclose the most frequent anomalies.