Faculty Bibliography

Machine learning is becoming increasingly important in both research and clinical applications in radiology due to recent technological developments, particularly in deep learning. As these technologies are translated toward clinical practice, there is a need for radiologists and radiology trainees to understand the basic principles behind them. This primer provides an accessible introduction to the vocabulary and concepts that are central to machine learning and relevant to the radiologist.

OBJECTIVE:This study aims to investigate predictors of inadequate prenatal care (PNC) use among pregnant women in Canada. STUDY DESIGN:Data for this secondary analysis was drawn from the Maternity Experiences Survey, a cross sectional, nationally representative survey that assessed peri- and post-natal experiences of mothers aged 15 and above in the Canadian provinces and territories. PNC use was measured by the Adequacy of Prenatal Care Utilization Index. Multivariate logistic regression analysis was conducted to determine socio-economic, demographic, maternal, delivery related and health service characteristics associated with inadequate PNC use. RESULTS:Prevalence of inadequate PNC was at 18.9%. Regression analysis revealed that mothers who were immigrants (odds ratio (OR)=1.40; 95% (confidence interval) CI: 1.13-1.74), primiparous (OR=1.22; 95% CI: 1.04-1.44), smoked (OR=1.33; 95% CI: 1.04-1.69) or consumed alcohol (OR=1.32; 95% CI: 1.03-1.68) during their pregnancy were more likely to receive inadequate PNC. Mothers with a family doctor as PNC provider versus those with an obstetrician (OR=1.26; 95% CI: 1.08-1.48) were more likely to have inadequate PNC. CONCLUSIONS:This is the first nationwide study in Canada to examine the factors associated with inadequate PNC use. Results of this study may help design interventions that target women with profiles of socio-demographic and behavioral risk to optimize their PNC use.