A large-scale prediction model to predict large for gestational age infants conceived by IVF/ICSI
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By
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Xiuyun Li
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Aijuan Zhang
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Gang Bai
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Yue Liu
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Wenlan Xing
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Yan Li
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July 14, 2026
Clinical Scorecard: Development of a Comprehensive Prediction Model for Identifying Large-for-Gestational-Age Infants Conceived via IVF/ICSI
At a Glance
| Category | Detail |
|---|
| Condition | Large-for-Gestational-Age (LGA) Infants |
| Key Mechanisms | Embryo transfer strategy, maternal and paternal characteristics |
| Target Population | Singleton live births resulting from IVF/ICSI |
| Care Setting | Center for Reproductive Medicine |
Key Highlights
- Machine learning model developed using XGBoost achieved an AUC of 0.7003.
- Key predictors include embryo transfer strategy and parental characteristics.
- LGA is associated with significant neonatal morbidity and long-term health issues.
Guideline-Based Recommendations
Diagnosis
- Identify risk factors for LGA in infants conceived via IVF/ICSI.
Management
- Utilize predictive models to assess risk in clinical settings.
Monitoring & Follow-up
- Monitor maternal and paternal health indicators pre- and post-embryo transfer.
Risks
- Increased risk of complications during delivery for mothers of LGA infants.
Patient & Prescribing Data
Couples undergoing IVF/ICSI treatment.
Focus on optimizing parental health and embryo transfer strategies.
Clinical Best Practices
- Incorporate machine learning models in clinical assessments.
- Consider both maternal and paternal factors in risk evaluations.
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