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Noninvasive Electrocardiography in the Perinatal Mouse

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Challenges of Developing Robust AI for Intrapartum Fetal Heart Rate Monitoring.

M E O'Sullivan¹, E C Considine¹, M O'Riordan^1,2

¹INFANT Research Centre, University College Cork, Cork, Ireland.

Frontiers in Artificial Intelligence

|November 12, 2021

Summary

This summary is machine-generated.

Artificial intelligence (AI) applied to cardiotocography (CTG) shows promise for improving fetal monitoring. Overcoming current challenges could lead to more reliable interpretation of fetal distress during labor, potentially preventing brain injury.

Keywords:

artificial intelligence cardiotocography (CTG)fetal heart rate (FHR)fetal hypoxia hypoxic ischaemic encephalopathy (HIE)labour machine learning pregnancy

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Area of Science:

Medical technology
Artificial intelligence in healthcare
Obstetrics and Gynecology

Background:

Cardiotocography (CTG) is the primary non-invasive method for monitoring fetal well-being during labor.
Difficulties in CTG interpretation are linked to fetal hypoxia and subsequent brain injury.
Artificial intelligence (AI) offers potential for more objective and reliable CTG interpretation.

Purpose of the Study:

To review the literature on AI application to CTG, identifying impediments to its success.
To examine prior randomized control trials (RCTs) of AI decision support systems for CTG.
To discuss novel engineering approaches and challenges in developing robust AI tools for fetal distress detection.

Main Methods:

Literature review of AI in CTG.
Analysis of randomized control trials (RCTs) of CTG decision support systems.
Review of novel engineering approaches for AI-assisted CTG.

Main Results:

Three RCTs of AI decision support systems were reviewed, detailing algorithms, outcomes, and limitations.
Preliminary findings indicate that incorporating clinical data enhances AI-assisted CTG performance.
Newer classification methods for CTG traces show potential for future AI development.

Conclusions:

AI has the potential to improve the reliability and objectivity of CTG interpretation.
Addressing current technical and clinical challenges is crucial for developing effective AI tools.
Integrating clinical data and advanced classification techniques are promising avenues for future AI-assisted CTG development.