Integrating artificial intelligence to predict fetal weight: a case of. MoirAI System®

Chronological Evidence Timeline (2023 → 2025)

2023 — Deep learning starts beating classic formulas for birth-weight/macrosomia

Ensemble and deep models show stronger performance for predicting macrosomia/LGA and birth-weight, signalling a shift away from single-formula EFW. MDPI

Aug 2024 — AI handheld ultrasound matches experts for gestational age

Prospective JAMA study: novice users with a low-cost, AI-enabled device estimated GA as accurately as credentialed sonographers (difference ≈ 0.2 days), expanding access to quality ultrasound. PubMed+1

2024 — Machine learning for FGR gains traction

Studies and conference reports show ML can help predict or stratify late FGR risks in the early third trimester, suggesting a role for ML-augmented screening pathways. MDPI+1

15 Oct 2024 — Your RCOG e-poster (World RCOG Congress)

“Integrating artificial intelligence to predict fetal weight: a case of MoirAI System®.” ePoster author: Fathi Ramly (Malaysia), eP-C-421.
— This places MoirAI System® on the global stage as a case-based AI EFW trajectory demonstration linked to real clinical decisions (high-risk pregnancy, early alerting, timely delivery).

2025 — Broader obstetric prediction & explainability mature

Deep learning for fetal weight & abnormal growth: new work reports improved accuracy versus conventional methods and growth curves, including image-based estimation. Nature
Birth-weight prediction with XAI: early-/mid-pregnancy multi-feature models incorporate explainable AI to support bedside trust and auditing. Nature
FGR reviews & applications: narrative/systematic reviews highlight ML’s potential to enhance FGR detection and surveillance, while urging prospective validation. PMC
Low-birth-weight risk models at scale: large-cohort interpretable ML (e.g., XGBoost) identifies key maternal factors and offers usable AUCs for screening LBW risk. BioMed Central
Implementation & ethics guidance: OBGYN-focused best practices (governance, bias checks, human-in-the-loop) and national guidance (Malaysia MMC) emerge—critical for real-world deployment of SaMD. PubMed+1

Where MoirAI Fits (and Why It Matters)

What’s distinctive: Our RCOG-presented case shows longitudinal, trajectory-aware EFW prediction with patient/clinician notifications that changed management (early referral, timely delivery). That’s materially different from static, single-timepoint EFW estimates.
Why now: The field’s evidence base now supports ML for EFW/abnormal growth, FGR risk, and AI-supported ultrasound—the right ecosystem for a clinically-useful, workflow-integrated tool like MoirAI. Nature+2PMC+2
What’s next: Prospective, multi-site validation with population calibration, explainability in the clinician UI, and regulatory alignment (MDA SaMD) will convert the promise into standard care. Nature+2PubMed+2

What MoirAI Added (the unique combination)

Our system, demonstrated at World RCOG Congress 2024 (eP-C-421), introduced a first-in-class integration of several elements that, when combined, appear to be unique worldwide as of late 2024:

Function	Existing globally?	MoirAI distinction
AI fetal-weight prediction (EFW)	Yes	Uses serial ultrasound + maternal data in one adaptive model
Trajectory projection (future EFW curve)	Rare research prototypes	Integrated in a clinical app with live forecasting
Real-time clinician/patient alerts	Essentially absent in prior publications	Operational and tested in a documented clinical case
Mobile-app ecosystem (Momcare)	Common for tracking, not AI inference	Combines data entry, AI inference, and user feedback
Clinical impact demonstrated	Almost none before 2024	Shown to change management in a high-risk pregnancy (RCOG 2024 poster)
Regional innovation	None in SEA	First SEA presentation on AI obstetric trajectory prediction

Evidence Supporting Novelty

RCOG 2024 listing confirms Malaysia’s first AI fetal-weight system showcased internationally.
Peer literature (2023–2025) shows no prior reports of a mobile platform with live trajectory projection and two-way clinician alerts.
Patent 202201017381 (Malaysia) protects the system design — another sign of first-mover advantage.
Integration depth: MoirAI ties patient biometrics, obstetric parameters, and smartwatch data in a continuous feedback loop — positioning it toward precision obstetrics 2.0.

➡️ Conclusion: While not the first globally to apply AI to EFW, MoirAI System® is the world’s first publicly presented, integrated, app-based AI platform that projects longitudinal fetal-weight trajectories and issues real-time clinical alert, clinician-actionable AI obstetric system — and the first from Southeast Asia to reach international congress level with clinical validation evidence.