Cardiotocography-Based Experimental Comparison of Artificial Intelligence and Human Judgment in Assessing Fetal Asphyxia During Delivery

TL;DR

This study compares AI and human experts in assessing fetal asphyxia using CTG data, finding that human judgment outperforms AI in accuracy but AI can help reduce false positives.

Contribution

The study experimentally compares AI and human specialists in predicting fetal asphyxia using CTG data, revealing the potential of AI to complement human judgment.

Findings

Human specialists achieved a higher AUC (0.693) than AI-based methods (ML: 0.514, DL: 0.524) in predicting fetal asphyxia.

Combining human and AI predictions improved specificity but did not surpass human accuracy alone.

AI has potential to reduce false positives and complement human judgment in clinical settings.

Abstract

Cardiotocography (CTG) has long been the standard method for monitoring fetal status during delivery. Despite its widespread use, human error and variability in CTG interpretation contribute to adverse neonatal outcomes, with over 70% of stillbirths, neonatal deaths, and brain injuries potentially avoidable through accurate analysis. Recent advancements in artificial intelligence (AI) offer opportunities to address these challenges by complementing human judgment. This study experimentally compared the diagnostic accuracy of AI and human specialists in predicting fetal asphyxia using CTG data. Machine learning (ML) and deep learning (DL) algorithms were developed and trained on 3,519 CTG datasets. Human specialists independently assessed 50 CTG figures each through web-based questionnaires. A total of 984 CTG figures from singleton pregnancies were evaluated, and outcomes were compared using receiver operating characteristic (ROC) analysis. Human diagnosis achieved the highest area under the curve (AUC) of 0.693 (p = 0.0003), outperforming AI-based methods (ML: AUC = 0.514, p = 0.788; DL: AUC = 0.524, p = 0.662). Although DL-assisted judgment improved sensitivity and identified cases missed by humans, it did not surpass the accuracy of human judgment alone. Combining human and AI predictions yielded a lower AUC (0.693) than human diagnosis alone, but improved specificity (91.92% for humans, 98.03% for humans and DL), highlighting AI's potential to complement human judgment by reducing false-positive rates. Our findings underscore the need for further refinement of AI algorithms and the accumulation of CTG data to enhance diagnostic accuracy. Integrating AI into clinical workflows could reduce human error, optimize resource allocation, and improve neonatal outcomes, particularly in resource-limited settings. These advancements promise a future where AI assists obstetricians in making more objective and accurate decisions during delivery.

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