Time-aware machine learning enables early intrapartum identification of fetuses at risk of hypoxic-ischemic encephalopathy from cardiotocography

Cardiotocography is the standard of care for intrapartum fetal monitoring, but its poor specificity leads to unnecessary interventions without reducing hypoxic-ischemic encephalopathy (HIE). Most artificial intelligence models are trained exclusively on the final hour before delivery, a window that can only be identified retrospectively. The authors hypothesized that this framework prevents models from learning the early evolving signs of HIE.

The study analyzed 174,186 deliveries at gestational age 35 weeks or later from Kaiser Permanente Northern California. Forty CTG features were extracted from 20-minute epochs to train Random Forest classifiers targeting severe acidosis (N=2,636) and clinically validated HIE (N=304). The training window was progressively extended from the final hour to the full duration of labor, and time from labor onset was added as a feature. Models were evaluated continuously throughout labor at a fixed 15% false positive rate.

Early identification of fetuses at risk of HIE improved as the training window was extended, plateauing at 18 hours. Adding time from labor onset produced the best early-warning performance: 40.3% of HIE cases were identified at least 3 hours before delivery and 27.3% at least 6 hours before delivery, absolute gains of 8.4% and 10.7% over the last-hour classifier. Additionally, 55.7% of HIE cases were identified at least 40 minutes before delivery.

The authors conclude that classifiers trained only on the last hour may identify HIE patterns occurring near delivery, whereas time-aware models trained across labor map the temporal evolution of these patterns and provide actionable early warnings. They argue that AI systems for intrapartum monitoring must account for the temporal evolution of labor to shift from end-of-labor diagnosis to early-warning decision support.