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.

Background and objectives. The risk of developing invasive cancer increased during the COVID-19 pandemic, especially in Romania, where the incidence of this disease is high due to limited medical education and broad screening. This study’s objective is to analyze the number of patients admitted with different types of cervical dysplasia and the treatment applied for the lesions during the SARS-CoV-2 pandemic compared to the same period for the year before the pandemic. Materials and methods: This is a retrospective study that took place in the Obstetrics and Gynecology Clinics I/II (OG I/II) of the Emergency County Hospital of Craiova during the SARS-CoV-2 pandemic (SP) (15.03.2020–14.03.2021) and in the 12 months before (non-pandemic period) (NPP) (15.03.2019–14.03.2020). The study includes 396 patients with pathological PAP smear results. All the patients included in this study were clinically examined and with colposcopy. The patients with Low-Grade Dysplasia were managed in a conservatory manner and reevaluated after six months. The patients with High-Grade Dysplasia were admitted for an excisional biopsy of the lesion. The excised fragments were sent to the Pathological Anatomy Laboratory for a histopathological examination. Results: This study reveals a decrease of more than half in the number of patients admitted with cervical intraepithelial neoplasia (CIN) lesions during the pandemic compared to the same period of the year before. The number of biopsies and excisional procedures has been decreasing by more than a factor of three during the pandemic period compared to the year before. Conclusion: During the SARS-CoV-2 pandemic, we found that the patients’ admission rate, diagnosis, and treatment was almost four times lower. As hospital restrictions were not dictated for cancer/precancer management during SP, we may assume that the differences were due to the fear of becoming infected with SARS-CoV-2 due to hospitalization. In the context of poor screening performance and high cervical cancer incidence, the influence of the SP may result in a further increase of severe cases related to this condition.

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.

Introduction: the COVID-19 pandemic has had a considerable impact on healthcare systems worldwide. Since the actual influence of the pandemic on gynecological care is still unclear, we aim to evaluate the effect of the SARS-CoV-2 pandemic on gynecological procedures compared to the pre-pandemic period in Romania. Materials and Methods: this is a single-center retrospective observational study, involving patients hospitalized in the year before the SARS-CoV-2 pandemic (PP), in the first year of the pandemic (P1), and in the second year of the pandemic until February 2022 (P2). The percentages of interventions were analyzed globally but also according to the type of surgery applied on the female genital organs. Results: during pandemic, the number of gynecological surgeries dropped considerably, by more than 50% in some cases, or even decreased by up to 100%, having a major impact on women’s health, especially in the first year of the pandemic (P1), before slightly increasing in the post-vaccination period (PV). Surgically treated cancer cases dropped by over 80% during the pandemic, and the consequences of this will be seen in the future. Conclusions: the COVID-19 pandemic played an important part in gynecological care management in the Romanian public health care system, and the effect will have to be investigated in the future.

Background/Objectives: Artificial intelligence (AI)-assisted automatic fetal measurements during ultrasound evaluation have become a major subject of interest. Most publications include systems developed and tested for a single anatomical component. In routine clinical practice, multiple biometric parameters are required for fetal weight estimation. Our objective is to assess the studies that incorporated three or more standard plane fetal automatic measurements with the help of AI systems in the second and third trimesters. Methods: We performed a systematic review of the published research focusing on automated AI systems analyzing at least three standard fetal biometric parameters or their corresponding standard ultrasound planes for fetal biometric, gestational-age, or fetal-growth assessment. The literature screening had to meet the predefined inclusion and exclusion criteria. Results: Nineteen studies, comprising more than 730,000 ultrasound images, met the inclusion criteria, and eighteen studies compared automated measurements with manual reference measurements. Overall, the included studies reported favourable performance for automated assessment of biparietal diameter, head circumference, abdominal circumference, and femur length, although substantial heterogeneity was observed in study design, AI architecture, validation methodology, and reported performance metrics. Conclusions: Automated AI systems show promising performance for assessing multiple standard fetal biometric parameters and may improve measurement consistency and examination efficiency. However, substantial heterogeneity in datasets, model architectures, and outcome reporting restricts direct comparison and the certainty of the evidence. Standardized reporting and prospective multicentre evaluation across gestational ages (GA), ultrasound systems, and operators are needed before these systems can be recommended for routine clinical use.