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

Cardiotocography (CTG) is the standard of care for intrapartum fetal monitoring, yet its poor specificity drives unnecessary intervention without reducing hypoxic-ischemic encephalopathy (HIE). Most artificial intelligence (AI) models are trained exclusively on the final hour before delivery, a window only identifiable retrospectively. We hypothesized that this framework prevents models from learning the early evolving signs of HIE. We analyzed 174,186 deliveries, with gestational age GA[≥]35 weeks, from Kaiser Permanente Northern California. Forty CTG features were extracted from 20-minute

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