# Placenta–pulmonary coupling–guided multimodal AI for fetal lung maturity staging and individualized glucocorticoid therapy

## TL;DR

A new AI framework uses placenta-lung interactions and multimodal data to assess fetal lung maturity and guide glucocorticoid therapy, improving interpretability and clinical alignment.

## Contribution

A physiology-informed hybrid AI model that integrates multimodal data and provides interpretable fetal lung maturity staging and individualized glucocorticoid dosing.

## Key findings

- The AI model clustered fetal lung maturity into four stages with a silhouette score of 0.72, aligning with biochemical benchmarks.
- The model predicted glucocorticoid doses within ±0.5 mg of clinical regimens and reduced projected RDS risk by 27%.
- The framework maintained neurotoxicity index below thresholds while improving decision traceability and clinical alignment.

## Abstract

Deep learning has improved medical image analysis but often produces opaque decisions and correlation-driven predictions that may diverge from clinical reasoning. We hypothesize that a physiology-informed hybrid framework, which explicitly models placenta–pulmonary interactions and integrates multimodal data, could provide interpretable and reliable guidance for assessing fetal lung maturity (FLM) and optimizing antenatal glucocorticoids (GCs).

In a prospective cohort study involving 320 pregnancies—including 160 with hypertensive disorders of pregnancy (HDP)—each with weekly acquisitions from 28 to 36 weeks, we combined 2D/3D ultrasound, shear-wave elastography, Doppler, and maternal plasma metabolomics. A biophysical placenta–pulmonary coupling model used the umbilical artery pulsatility index (PI) and a metabolomic hypoxia–steroid score to represent placental reserve, while backscatter integrals and elastography were used to characterize fetal lung properties. Constrained by this model, a dual-branch network was developed: (i) a cross-modal attention Transformer with self-supervised contrastive learning to generate unsupervised FLM stages from fused representations and (ii) a spatiotemporal convolution–LSTM network to predict individualized GC dosing and the optimal administration window. A composite loss penalized both projected respiratory distress syndrome (RDS) risk and the biomarker-derived neurotoxicity index.

The cross-modal representations clustered into four distinct maturity stages matching biochemical benchmarks, with an inter-stage silhouette score of 0.72. A downstream classifier achieved 92.3% accuracy in discriminating early from late maturity. The dosing branch predicted the GC dose within ±0.5 mg of clinically prescribed regimens and reduced projected RDS risk by 27% compared to standard dosing, while maintaining the biomarker-derived neurotoxicity index below the prespecified threshold.

A mechanism-guided, multimodal AI framework constrained by placenta–pulmonary physiology transforms imaging features into traceable decision pathways that align with clinical cognition. This interpretable framework may enable non-invasive FLM staging and individualized GC therapy, providing hypothesis-generating decision support that warrants external validation and prospective trials.