At the recent World RCOG Congress, a Malaysian medical-AI advancement quietly marked its place on the global stage — a juncture where the timeless art of obstetrics intertwines with the precision of machine intelligence. The research, titled “MoirAI System®: An Artificial Intelligence Analysis to Predict Fetal Weight Progression,” was presented as an electronic poster by Dr Fathi Ramly, Prof Dr Jamiyah Hassan, Dr Quek Yek Song and the medical-AI team of Moirai Tech Sdn Bhd, in collaboration with Universiti Teknologi MARA (UiTM) and Columbia Asia Hospital Iskandar Puteri. Their work represents a monumental stride in modern obstetrics: using AI-driven predictive analytics to monitor and forecast a baby’s growth while still in the womb.
🤖 The Birth of an Idea
Every pregnancy tells a story — of hope, anxiety, and countless decisions made by doctors and mothers alike. But traditional tools, though essential, only capture snapshots in time.
The MoirAI System®, developed under the umbrella of Moirai Momcare, changes that narrative. It continuously collects and analyses a mother’s clinical and ultrasound data — including fetal measurements such as biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femur length (FL) — to predict estimated fetal weight (EFW) and its likely trajectory.
By training its neural-network on population-based data and historical pregnancy records, the system learns patterns of normal and abnormal growth, alerting both patient and clinician when a baby’s development deviates from the expected curve.
📊 A Case That Changed the Course
In one documented scenario, a high-risk mother — with a complex obstetric history and gestational diabetes — was monitored through the MoirAI Momcare® mobile app. At 27 weeks of gestation, the system detected that her fetus’s growth had slowed. The AI projected a 59% risk of fetal growth restriction (FGR) — weeks before it became clinically evident via standard ultrasound. This early warning triggered timely interventions. The obstetrician transferred the mother to a tertiary hospital, where the baby was delivered safely by emergency caesarean section at 32 weeks. Though premature at 1.1 kg, the infant thrived under neonatal care. For the medical team, this wasn’t just data — it was a life saved through foresight.
🧠 From Numbers to Nurture
What sets MoirAI apart is not just technical accuracy but clinical empathy. Its analysis mirrors the vigilance of an experienced obstetrician yet brings round-the-clock consistency and the ability to process thousands of data points per second.
Recent studies lend strong support to this approach: AI-driven models have achieved Mean Absolute Percentage Errors (MAPE) under 6% in fetal weight estimation using deep neural networks — outperforming many conventional formula-based methods. BioMed Central+2Nature+2 Moreover, predictive models for fetal growth restriction (FGR) have demonstrated pooled sensitivities of ~0.84 and specificities of ~0.87 — suggesting that AI/ML techniques can significantly improve early detection of growth-related fetal risk. PMC+1
In simpler terms: the AI’s estimations are close enough to guide real-time medical decisions. This is more than technology — it is the evolution of care itself.
💡 Why It Matters
Obstetricians often face overwhelming workloads, limited consultation time, and restricted access to continuous patient monitoring. AI offers a way to extend the clinician’s reach — not to replace judgment, but to enhance it.
With the MoirAI System®, doctors can anticipate complications earlier, counsel mothers more effectively, and make evidence-based decisions with greater confidence. Patients, meanwhile, gain clearer understanding of their own pregnancies — empowering them to take part in their care journey.
In conjunction with current research showing that AI-enabled ultrasonography (used by non-sonographers) could match expert sonographers in gestational age estimates (mean error ~3 days) in low-resource settings, the empowerment potential is clear. JAMA Network+1
🌟 A Vision Beyond Birth
The success of this study lays the groundwork for a new era of digital obstetrics. Future updates will integrate emotional analytics from wearable devices, enabling AI to sense physiological stress and mood shifts in real time — bringing medical empathy closer to reality.
Further, the broader field of AI in obstetrics is advancing rapidly: deep-learning models are now being applied to ultrasound image analysis, placental assessment, and prediction of adverse outcomes such as pre-eclampsia, preterm birth, and fetal hypoxia. PMC+2PMC+2
❤️ Malaysia’s Contribution to the World
That this innovation was presented at the World RCOG Congress is more than an academic achievement — it’s a declaration that Malaysia can stand among global leaders in medical AI. By merging medical expertise, data science, and compassionate design, Moirai Tech demonstrates how technology born in Asia can change the course of healthcare worldwide.
⚕️ The Future is MoirAI
From the gentle pulse of a smartwatch to deep-learning models behind a mobile app, Moirai Tech is building an ecosystem where every heartbeat tells a story — and every story teaches AI how to predict better.
The MoirAI System® is not merely a tool; it’s a promise — that every mother and every child deserve the best that science and compassion can offer.
MoirAI system® summary
| Gestational Age (weeks) | BPD/cm | HC/cm | AC/cm | FL/cm | EFW/g | |||||
| Predicted | Ultrasound | Predicted | Ultrasound | Predicted | Ultrasound | Predicted | Ultrasound | Predicted | Ultrasound | |
| 15 | - | 3.02 | - | 11.63 | - | 9.09 | - | 1.63 | - | 115 |
| 19 | - | 4.18 | - | 15.04 | - | 12.59 | - | 2.04 | - | 241 |
| 23 | 6.17 | 5.30 | 19.28 | 19.11 | 17.67 | 17.39 | 3.38 | 3.50 | 615 | 455 |
| 27 | 6.02 | 6.14 | 21.84 | 22.71 | 21.31 | 20.46 | 4.42 | 4.41 | 739 | 746 |
| 29 | 6.84 | 6.90 | 24.12 | 25.31 | 22.93 | 21.52 | 5.26 | 4.94 | 876 | 974 |
| 30 | 7.25 | 6.84 | 25.51 | 24.53 | 23.22 | 21.52 | 5.63 | 5.25 | 1011 | 1038 |
| 31 | 7.32 | 7.49 | 25.74 | 26.16 | 23.32 | 22.01 | 5.94 | 5.37 | 1095 | 1176 |
| 32 | 7.62 | 6.96 | 26.28 | 25.75 | 23.38 | 22.86 | 6.10 | 5.46 | 1174 | 1222 |
Table 1: Comparison between Ultrasound data and MoirAI® Predicted Data
| BPD | HC | AC | FL | EFW | |
| RMSE | 5.03 | 8.04 | 11.31 | 4.06 | 86.5 |
| MAPE (%) | 6.2 | 2.8 | 4.7 | 6.6 | 9.9 |
Table2: RMSE & MAPE between ultrasound and predicted MoirAI® System.
