Novel machine learning applications in peripartum care: a scoping review

Objective

Machine learning (ML), a subtype of artificial intelligence (AI), presents predictive modeling and dynamic diagnostic tools to facilitate early interventions and improve decision-making. Considering the global challenges of maternal, fetal, and neonatal morbidity and mortality, ML holds the potential to enable significant improvements in maternal and neonatal health outcomes. We aimed to conduct a comprehensive review of ML applications in peripartum care, summarizing the potential of these tools to enhance clinical decision-making and identifying emerging trends and research gaps.

Data Sources

We conducted a scoping review on MEDLINE, Cochrane Library, and EMBASE databases from inception to April 2024. We gathered additional relevant studies through snowball sampling. We meticulously screened titles and abstracts and chose full-text articles for further analysis.

Study Eligibility Criteria

We included primary research articles and abstracts focusing on pregnant individuals, employing ML methods for peripartum care.

Study Appraisal and Synthesis Methods

No formal quality assessment was performed. Data were extracted using a custom template to capture study characteristics and ML models. Findings were synthesized using summary tables and figures to highlight key trends and results.

Results

Among 406 studies, 78% were published within the last five years. Most studies originated from high-income or well-resourced countries, with 27% from North America (including 24% from the United States) and 34% from Asia, predominantly China (18%). Studies from low- and middle-income regions were notably scarce, reflecting significant regional disparities. Predictive modeling tasks were the most prevalent (59%), followed by classification tasks (29%). Supervised learning dominated (90%), with algorithms such as Support Vector Machines, Random Forests, and Logistic Regression most commonly used. Key topics included fetal distress and acidemia (32%), preterm birth (22%), mode of delivery (13%), and birth weight (13%). Notably, Explainable AI methods were utilized in only 19% of studies, and external validation was performed in just 5%. Despite these advancements, only 1% of models resulted in accessible clinical tools, and none were fully integrated into healthcare systems.

Conclusions

ML holds significant potential to enhance peripartum care by improving diagnostic accuracy and predictive capabilities. However, realizing this potential requires responsible AI practices, including robust validation with external datasets, prospective investigations across diverse populations, and the development of digital and data infrastructure for seamless integration into electronic health records. Additionally, transparent AI that provides insights into risk stratification logic is essential for clinician trust in ML tools. Future research should address understudied areas, prioritize neglected low-income settings, and explore advanced ML approaches to improve maternal and neonatal outcomes.