# Generational Leaps in Intrapartum Fetal Surveillance

## TL;DR

This paper reviews the history and limitations of fetal monitoring and suggests future improvements using intelligent systems and risk assessment.

## Contribution

The paper introduces the Fetal Reserve Index as a promising approach combining clinical risk factors with fetal heart rate patterns.

## Key findings

- Ancillary methods like fetal ECG analysis have shown limited success in improving fetal monitoring outcomes.
- Automated systems for fetal heart rate analysis have not consistently improved intrapartum surveillance.
- The Fetal Reserve Index shows promise by integrating clinical risk factors with traditional fetal heart rate patterns.

## Abstract

Background/Objectives: Electronic fetal monitoring (EFM) has been used for intrapartum fetal surveillance for over 50 years. Despite numerous trials comparing EFM with standard fetal heart rate (FHR) auscultation, it remains contentious whether continuous monitoring with standard interpretation has reliably improved perinatal outcomes, specifically lower rates of perinatal morbidity and mortality. This review examines previous attempts to improve fetal monitoring and presents future directions for novel intrapartum fetal surveillance systems. Methods: We conducted a chronological review of EFM developments, including ancillary methods such as fetal ECG analysis, automated systems for FHR analysis, and artificial intelligence applications. We analyzed the evolution from visual interpretation to intelligent systems and evaluated the performance of various automated monitoring platforms. Results: Various ancillary methods developed to improve EFM accuracy for predicting fetal compromise have shown limited success. Only a limited number of studies demonstrated that adding fetal ECG analysis to visual FHR pattern interpretation resulted in better fetal outcomes. Automated systems for FHR analysis have not consistently enhanced intrapartum fetal surveillance. However, novel approaches such as the Fetal Reserve Index (FRI) show promise by incorporating clinical risk factors with traditional FHR patterns to provide higher-level risk assessment and prognosis. Conclusions: The shortcomings of visual interpretation of FHR patterns persist despite technological advances. Future intelligent intrapartum surveillance systems must combine conventional fetal monitoring with comprehensive risk assessment that incorporates maternal, fetal, and obstetric factors. The integration of artificial intelligence with contextualized metrics like the FRI represents the most promising direction for improving intrapartum fetal surveillance and clinical outcomes.