Last updated: October 21, 2026
Pregnancy is one of the most closely monitored periods in a person’s life: blood pressure at almost every visit, regular blood tests, ultrasound scans and, during labour, continuous recording of the baby’s heartbeat. Someone has to turn all those numbers, images and signals into decisions: is this pregnancy high risk, is this baby growing well, is this heart trace worrying?
Algorithms have helped with those decisions for years. What is changing is their type: simple rules and statistical scores are being joined by machine learning models that read ultrasound videos or analyse heart traces. Some have strong evidence behind them, some failed in large trials and many are too new to judge. This guide follows pregnancy stage by stage and explains what the research shows.
Why Pregnancy Monitoring Needs Better Tools
According to estimates published by the WHO and other UN agencies in 2025, about 260,000 women died during or after pregnancy and childbirth in 2023, roughly one every two minutes. Maternal deaths fell by about 40% between 2000 and 2023, but most of the remaining deaths are preventable and they are heavily concentrated in low- and lower-middle-income countries. The leading causes, such as severe bleeding, high blood pressure disorders and infections, are often detectable or treatable when the warning signs are recognized in time.
In wealthier countries the problem is interpretation rather than access: two experienced clinicians can read the same heart trace differently, and ultrasound quality depends on the skill of the person holding the probe. In poorer settings, trained staff and equipment are often missing altogether. Algorithms promise to help with both, by making assessments more consistent and extending expert skills to places where experts are scarce. They only help, however, if a health system can act on the result.
Three Kinds of Algorithms in Pregnancy Care
The word “algorithm” covers very different technologies. Knowing which kind a tool uses helps explain both its strengths and its risks.
| Type | How it works | Example in maternity care | Typical weakness |
|---|---|---|---|
| Rule-based program | Follows fixed if-then instructions written by clinicians | Automated replies and alerts when a new mother texts in a high blood pressure reading | Only as good as the rules; cannot learn new patterns |
| Statistical risk model | Combines measured factors with weights derived from large studies to calculate a probability | First-trimester preeclampsia screening; calculators for vaginal birth after caesarean | Can carry the biases of the data and the choices made when building it |
| Machine learning and deep learning | Learns patterns directly from large sets of images, signals or records | Estimating gestational age from ultrasound video; flagging possible heart defects; research models for labour heart traces | Harder to explain; performance can drop in new populations or with different equipment |
A Pregnancy Timeline: Where Algorithms Appear
| Stage | What is being monitored | How algorithms are used | Maturity |
|---|---|---|---|
| First trimester (11–14 weeks) | Risk of preeclampsia and chromosomal conditions | Risk models combining history, blood pressure, ultrasound and blood tests | In routine use in many countries |
| Mid-pregnancy anatomy scan | Baby’s organs, including the heart | AI software that checks scan views and flags possible abnormalities | Recently cleared in the US; limited outcome data |
| Throughout pregnancy | Gestational age and growth | Standard formulas from measurements; AI models that read simple ultrasound sweeps | Formulas routine; AI largely in research and pilots |
| Labour | Baby’s heart rate and contractions | Computerised trace interpretation and alerts | Available commercially; no proven outcome benefit so far |
| After birth | Blood pressure and recovery | Remote monitoring with automated feedback and escalation | Growing use; good evidence for follow-up rates |
What Does the Evidence Actually Show?
Predicting accurately, improving a care process and improving health outcomes are three different things. The table below shows which of these each tool has demonstrated so far.
| Tool | Type of algorithm | Level of evidence | What it does and does not show |
|---|---|---|---|
| Preeclampsia screening | Statistical model | Clinical benefit shown | A validated screening pathway combined with preventive aspirin reduced preterm preeclampsia in the ASPRE trial. The benefit comes from screening plus treatment, not from the algorithm alone. |
| Computerised fetal heart-rate interpretation | Rule-based decision support | No benefit shown | The INFANT randomized trial found no improvement in outcomes for babies with the software tested. |
| AI ultrasound assessment | Deep learning | Promising; outcome evidence limited | Studies support specific technical abilities, such as estimating gestational age or flagging findings. Better image interpretation does not automatically mean better outcomes for mothers or babies, and real-world performance still needs confirmation. |
| Remote postpartum blood pressure monitoring | Rule-based automation | Better follow-up shown | A randomized trial found much higher rates of timely blood pressure measurement, but did not establish fewer complications. |
First Trimester: Predicting Preeclampsia
Preeclampsia involves high blood pressure and signs of organ damage; the preterm form, leading to delivery before 37 weeks, is especially dangerous. Checklists of risk factors miss many cases.
The Fetal Medicine Foundation developed a statistical model that combines maternal characteristics and history with blood pressure, an ultrasound measurement of blood flow in the uterine arteries and two blood markers, taken at 11 to 13 weeks. In the ASPRE study, which screened 26,941 singleton pregnancies across several European countries, this approach identified about 77% of women who later developed preterm preeclampsia, while flagging around 10% of all women as high risk.
Screening only matters if something can be done with the result. In the ASPRE randomized trial, published in The New England Journal of Medicine in 2017, women identified as high risk were given either daily low-dose aspirin or a placebo from the first trimester until 36 weeks. Preterm preeclampsia was 62% less common in the aspirin group. Aspirin did not reduce preeclampsia at term, and the benefit applied to the preterm form only.
This is one of the clearest examples of an algorithm improving pregnancy outcomes, but the model is classic statistics, not machine learning. New machine learning versions need to beat this well-validated model, not just a checklist.
Ultrasound: Extending Skills and Checking Scans
Gestational age without a trained sonographer
Knowing how far along a pregnancy is underpins almost every antenatal decision. Where ultrasound and sonographers are scarce, it is estimated from the last menstrual period, which is often uncertain.
A study published in NEJM Evidence in 2022 tested a different approach. Researchers recruited 4,695 pregnant volunteers in North Carolina and Zambia and recorded short “blind sweep” videos, simple passes of the probe across the abdomen that do not require the operator to find or interpret specific views. A neural network trained on these videos estimated gestational age with accuracy similar to trained sonographers performing standard measurements. The performance appeared to hold when the sweeps were done by a nurse-midwife with no ultrasound training, using a low-cost, battery-powered handheld probe.
The authors stressed that whether this works when rolled out in low-resource clinics, and whether it improves outcomes, still needs to be tested.
Screening the baby’s heart
Congenital heart defects are the most common type of birth defect, affecting nearly 1 in 100 newborns, and many are not detected before birth. Checking the fetal heart is one of the most technically demanding parts of the mid-pregnancy scan. In November 2024, the US Food and Drug Administration cleared AI software from the Paris-based company BrightHeart that flags ultrasound findings suggestive of congenital heart defects during second-trimester scans. Further clearances followed in May 2025, including software that checks whether all the required heart views have been captured.
Regulatory clearance is not the same as proven clinical benefit. An ultrasound AI system may be authorized to assist with a specific task, such as identifying suspicious findings or checking whether required views have been captured. That does not establish that using the system reduces missed diagnoses, unnecessary procedures or complications for mothers and babies. Those outcomes require separate clinical evaluation in the settings where the software is used.
Labour: The Fetal Heart Rate Lesson
During labour, cardiotocography (CTG) records the baby’s heart rate alongside contractions to spot babies who may be short of oxygen. Reading these traces is notoriously difficult: some babies in difficulty are missed, while some emergency caesareans turn out to have been unnecessary.
Computer interpretation seemed like an obvious solution, and it was put to a rigorous test. The INFANT trial, run in maternity units in the UK and Ireland and published in The Lancet in 2017, randomized 47,062 women having continuous monitoring in labour. Half had decision-support software that analysed the trace and produced colour-coded alerts; the other half relied on clinicians’ own interpretation. Among 46,042 women analysed, poor outcomes for babies occurred in 0.7% of both groups. Rates of caesarean section and assisted delivery were also unchanged.
Research has continued. At the University of Oxford, a team has built a system called OxSys using a large archive of labour heart traces linked to outcomes for mothers and babies. An early version compared favourably with bedside diagnosis in a retrospective study of 22,790 labours. The team reports that its deep learning models are not yet suitable for detecting certain rare, severe injuries, and OxSys has not yet been tested in a randomized trial.
After Birth: Blood Pressure Monitoring at Home
The risks of pregnancy-related high blood pressure continue after delivery, including in the days after a mother goes home. US guidelines recommend checks within 72 hours and again at 7 to 10 days for women with hypertensive disorders, but many new mothers struggle to return to a clinic that soon.
A randomized trial at the University of Pennsylvania, published in BMJ Quality & Safety in 2018, compared two approaches in 206 women with pregnancy-related hypertension. One group received a home blood pressure cuff and texted their readings twice a day for two weeks, with automated feedback and alerts to a physician for severe readings. The other group was asked to attend an office visit 4 to 6 days after discharge.
New mothers with at least one blood pressure recorded in the first 10 days after birth
Randomized trial of 206 women with hypertensive disorders of pregnancy. Source: Hirshberg A, et al. BMJ Quality & Safety, 2018. The outcome measured was obtaining blood pressure readings, not reduced complications.
In the texting group, 84% met the recommended schedule. The automated responses followed clinicians’ rules, so this is a rule-based algorithm, not AI, which is part of the point: a simple, well-designed system made a large practical difference. The trial measured whether readings were obtained, not whether complications were prevented.
When an Algorithm Encodes Bias: The VBAC Calculator
Algorithms can also cause harm, and maternity care offers one of the best-documented examples. In 2007, the US Maternal-Fetal Medicine Units Network published a calculator to estimate the chance of a successful vaginal birth after a previous caesarean (VBAC). Alongside age, body mass index and obstetric history, it included race and ethnicity. As a result, Black and Hispanic women with otherwise similar profiles were given lower predicted chances of success than white women.
It was used for more than a decade before concerns that it steered women of colour away from vaginal birth, and treated race as a biological risk factor, led its authors to publish a revised version in 2021 without race and ethnicity, which is now the model used in the network’s online calculator. Studies suggest the new version predicts outcomes about as well.
Privacy and Data Protection in Pregnancy AI
Pregnancy-related data can reveal highly sensitive information about a person’s health, medical history and reproductive life. Before using an AI-powered pregnancy app or connected device, check what information it collects, whether data are shared with third parties, how long they are retained and whether they can be deleted.
Legal protections depend on where you live and who holds the data. In the European Union, the GDPR treats health data as a special category with stronger protections. In the United States, HIPAA rules apply mainly to healthcare providers, insurers and their contractors, and many consumer apps fall outside them. Patients using remote monitoring through their care team can ask who reviews their information, how it is accessed and how it is protected. A device’s technical accuracy does not, by itself, establish that its data practices are safe or appropriate.
Apps and Wearables at Home
Pregnancy apps, home fetal Dopplers, smart blood pressure cuffs and wearable patches are widely sold. Some are part of programs supervised by a care team; many are wellness products never evaluated as medical devices. A reassuring reading should never override symptoms: reduced fetal movements, severe headache, visual changes, bleeding or abdominal pain need prompt contact with a maternity unit.
Questions to Ask Your Care Team
- Is a risk score or software being used in my care, and what does it measure?
- What happens if the result says I am high risk, and what are my options?
- Does a clinician review the result, and who makes the final decision?
- If I use an app or home device, who looks at my readings and how quickly?
- What symptoms should make me call the maternity unit regardless of any reading?
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Limitations
Most research on AI in pregnancy measures agreement with experts rather than outcomes for mothers and babies. Many studies are retrospective, limited to one health system or supported by manufacturers, and regulatory clearance does not require proof of clinical benefit. Because severe events are rare, outcome trials must be very large. This article covers selected, well-documented examples, not every algorithm in maternity care.
The Bottom Line
Frequently Asked Questions
Can AI predict preeclampsia?
A statistical screening model used at 11 to 13 weeks identifies most women who will develop preterm preeclampsia, and preventive treatment for those at high risk has been shown to reduce cases. Machine learning models are being developed, but they still need to prove they outperform this existing approach. Any preventive treatment should only be started on medical advice.
Does computer analysis of my baby’s heart rate in labour make birth safer?
A large trial of more than 46,000 women found that adding computerised decision support to heart-rate monitoring did not improve outcomes for babies or change caesarean rates. Newer data-driven systems are being developed, but they have not yet been proven in randomized trials. Continuous one-to-one care from a midwife remains central.
Are home fetal heart monitors and pregnancy apps safe to rely on?
They should not replace professional care. A normal reading at home does not rule out a problem, and symptoms such as reduced movements, bleeding, severe headache or pain need prompt assessment by a maternity unit. If a device or app is part of a program run by your care team, ask who reviews the data and how quickly.
Can algorithms be unfair to some pregnant women?
Yes. The original US calculator for vaginal birth after caesarean gave lower predicted chances to Black and Hispanic women and was revised in 2021 to remove race and ethnicity. Any algorithm trained on historical data can reflect existing inequalities, which is why performance should be checked across different groups.
Official Sources
- World Health Organization, UNICEF, UNFPA, World Bank Group and UNDESA. Trends in maternal mortality 2000 to 2023, 2025.
- Rolnik DL, et al. Aspirin versus Placebo in Pregnancies at High Risk for Preterm Preeclampsia (ASPRE). The New England Journal of Medicine, 2017.
- Rolnik DL, et al. ASPRE trial: performance of screening for preterm pre-eclampsia. Ultrasound in Obstetrics & Gynecology, 2017.
- Pokaprakarn T, et al. AI Estimation of Gestational Age from Blind Ultrasound Sweeps in Low-Resource Settings. NEJM Evidence, 2022.
- BrightHeart. FDA clearance announcements (company press release), 2024–2025.
- The INFANT Collaborative Group. Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet, 2017.
- NIHR Evidence. Computerised interpretation of fetal heart rate during labour does not improve outcomes.
- Georgieva A, et al. Computerized data-driven interpretation of the intrapartum cardiotocogram: a cohort study. Acta Obstetricia et Gynecologica Scandinavica, 2017.
- Hirshberg A, et al. Comparing standard office-based follow-up with text-based remote monitoring in the management of postpartum hypertension. BMJ Quality & Safety, 2018.
- Grobman WA, et al. Prediction of vaginal birth after cesarean in term gestations: a calculator without race and ethnicity. American Journal of Obstetrics & Gynecology, 2021.
- How the vaginal birth after cesarean calculator automated racism (peer-reviewed qualitative study).
Medical disclaimer: This article is for general information only and is not a substitute for professional medical advice, diagnosis or treatment. Every pregnancy is different. Do not start, stop or change any medication, monitoring plan or birth decision without guidance from your midwife, obstetrician or doctor. If you are pregnant and notice reduced fetal movements, bleeding, severe headache, visual changes, abdominal pain or any other worrying symptom, contact your maternity unit or emergency services immediately. Helathora is not affiliated with any company or product mentioned.