Development and preliminary evaluation of a machine learning model for predicting low birth weight using placental...

To identify key placental intravoxel incoherent motion (IVIM) MRI parameters and maternal factors associated with low birth weight (LBW), and develop a prenatal predictive model for LBW risk assessment.

This retrospective study analyzed 113 term neonates (January 2023-December 2024), categorized as LBW or normal birth weight. Twenty-one antenatal metrics, including maternal characteristics and region-specific placental IVIM MRI parameters (perfusion fraction [f], true diffusion coefficient [D], pseudo-diffusion coefficient [D*]), were evaluated. Feature importance was ranked using Shapley Additive Explanations (SHAP) analysis in a Random Forest algorithm. Key predictors were used to build a multivariable logistic regression nomogram. Discrimination (ROC-AUC), calibration, and clinical utility (DCA) were assessed. Internal validation employed bootstrap resampling (1000 iterations).

SHAP identified f values from maximal placental section (f_MPS), central zone (f_CPZ), and fetal side (f_FS) as top predictors. The nomogram showed good discrimination (AUC = 0.86, 95 % CI: 0.74-0.98). Bootstrap validation yielded an AUC of 0.82 (95 % CI: 0.61-0.98), with high sensitivity and specificity. The calibration curve showed good model fit. DCA demonstrated considerable clinical benefit.

Placental IVIM MRI f values from distinct placental regions are significant LBW predictors. The model provides accurate prenatal risk assessment, guiding early interventions to optimize perinatal outcomes.