The role of the gynaecologist in the promotion and maintenance of oral health during pregnancy

Objectives: The aim of the study was to assess routine dental examination attendance of pregnant women and a possible impact of gynaecological referrals on the attendance rate. Material and methods: An electronic survey was conducted that was inclusive of women up to 5 years following delivery. The questions related to socio-demographic data, the course of pregnancy and childbirth, and visits to dental office during pregnancy. For statistical analysis, the authors utilized the chi-square test, Spearman’s rank correlation coefficient and odds ratios. A significance level of 0.05 has been assumed. Results: A total of 3455 questionnaires were analyzed encompassing women aged 13.1–45.4 years. The respondents were on average 1.78 ± 1.44 years after childbirth. The population comprises of women in 59.1% from large cities, in 74.8% with higher education and in 41% with good socio-economic status. A total of 62.3% of women from the study population have visited a dentist for a routine dental examination. Gynaecologists have given a simple referral to a dentist to 17.6% of all women. 45.9% of them were further requested to provide back the feedback of their dental consultation. Dental appoint­ments were upheld by 87.3% of referred women and by 56.9% of those without a referral (OR = 5.20 (4.05–6.67); p < 0.001). Among those who were referred, dental appointments were upheld in 91.7% of cases when further asked to provide oral health feedback and in 83.5% of cases in absence of such further request (OR = 2.19 (1.3–3.66); p = 0.003). Conclusions: It was determined that referrals from a gynaecologist, and associated oral health feedback requests increase the frequency of abiding to dental appointments during pregnancy. As such, it is necessary to increase the involvement of gynaecologists in the promotion and maintenance of perinatal oral health

Machine Learning Methods for Preterm Birth Prediction: A Review

Preterm births affect around 15 million children a year worldwide. Current medical efforts focus on mitigating the effects of prematurity, not on preventing it. Diagnostic methods are based on parent traits and transvaginal ultrasound, during which the length of the cervix is examined. Approximately 30% of preterm births are not correctly predicted due to the complexity of this process and its subjective assessment. Based on recent research, there is hope that machine learning can be a helpful tool to support the diagnosis of preterm births. The objective of this study is to present various machine learning algorithms applied to preterm birth prediction. The wide spectrum of analysed data sets is the advantage of this survey. They range from electrohysterogram signals through electronic health records to transvaginal ultrasounds. Reviews of works on preterm birth already exist; however, this is the first review that includes works that are based on a transvaginal ultrasound examination. In this work, we present a critical appraisal of popular methods that have employed machine learning methods for preterm birth prediction. Moreover, we summarise the most common challenges incurred and discuss their possible application in the future