Before the Emergency: How AI Is Detecting Pregnancy Complications Earlier

By Henry Duah

Every day, hundreds of women die from complications related to pregnancy and childbirth. In 2023 alone, an estimated 260,000 maternal deaths occurred worldwide, about 712 deaths every single day. Sub-Saharan Africa carries the heaviest burden, accounting for roughly 70% of global maternal deaths.
What makes this especially tragic is that most of these deaths are preventable. The medical knowledge already exists. The challenge is often timing.

Complications such as postpartum haemorrhage, preeclampsia, and preterm labour are frequently detected too late. Referrals happen late, interventions happen late, and clinicians are forced into emergency response rather than prevention. According to the World Health Organisation, the global maternal mortality ratio in 2023 stood at 197 deaths per 100,000 live births, and the current pace of progress is still insufficient to meet the Sustainable Development Goal target by 2030.

The Shift

A new generation of maternal health technologies is trying to change one critical moment: when risk becomes visible. Instead of waiting for complications to emerge, these systems use artificial intelligence and machine learning to analyse data clinicians already collect: medical history, vital signs, laboratory results, ultrasound images, and other clinical signals. By detecting subtle patterns in this data, AI systems can flag potential risks earlier in pregnancy, giving care teams more time to intervene.
In practical terms, these tools act like an early warning system for pregnancy, helping clinicians identify patients who may need closer monitoring, specialist referral, or preventative treatment.

Why This Matters in African Health Systems

Across many African healthcare settings, maternity care operates under difficult constraints: limited specialist availability, high patient volumes, fragmented medical records, and long referral chains between primary clinics and hospitals. In these environments, the most useful AI solutions are not necessarily the most technically complex. They are the ones that help frontline health workers answer simple but critical questions:
– Which patients require urgent attention?
– Which pregnancies are likely to develop complications?
– What should we prepare for before it becomes an emergency?

AI tools can help overstretched healthcare systems prioritise care more effectively by turning routine clinical data into actionable insights.

Company Spotlights

CognitiveCare – Risk Prediction Across the Pregnancy Journey

CognitiveCare has developed the MIHIC Score, an AI-driven approach designed to assess the likelihood of critical maternal and infant health outcomes throughout the entire pregnancy journey, from the antepartum stage through delivery and postpartum care.

Rather than focusing on a single complication, the platform evaluates a broad set of clinical and demographic variables to estimate the propensity for multiple adverse outcomes at both individual and population levels. This multi-risk approach allows healthcare providers to view pregnancy not as a single clinical event but as a dynamic process where risks evolve over time. The result is a kind of “risk radar” for maternity care, helping health systems prioritise monitoring and allocate resources more strategically.

Ultrasound AI: Extracting Earlier Insight from a Familiar Tool

Ultrasound imaging is already a routine part of antenatal care, but interpreting scans consistently can be challenging, particularly where experienced specialists are scarce.

Ultrasound AI is developing models that analyse ultrasound images to predict delivery dates as early as eight weeks into pregnancy and to provide signals that may indicate elevated risks of preterm delivery. Instead of introducing a completely new device category, the company builds on an existing clinical workflow, ultrasound scanning and enhances it with algorithmic interpretation. By improving the consistency and predictive power of ultrasound analysis, this approach could help clinicians detect potential complications earlier, particularly in settings where access to highly trained specialists is limited.

Delfina: Integrated Pregnancy Platform

Delfina Care is an AI-driven maternal health platform that uses predictive analytics to identify high-risk pregnancies and help care teams intervene earlier.

What makes Delfina interesting is that it combines clinical data with social and behavioural factors, including patient engagement, appointment adherence, and other contextual signals, to improve risk prediction. Their platform is used by health systems and maternity care providers to help reduce complications such as preterm birth and severe maternal morbidity.

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