What question did this study set out to answer?

This research aims to develop a non-invasive predictive tool for neonatal respiratory distress syndrome (NRDS) in pregnancies affected by gestational diabetes mellitus (GDM).

March 2, 2026Open Access

Harnessing Artificial Intelligence-Derived Pulmonary Signatures and Uterine Hemodynamics: A Novel Dual-Pathway Framework for Predicting Neonatal Respiratory Distress in Gestational Diabetes Mellitus

Key Points

This research aims to develop a non-invasive predictive tool for neonatal respiratory distress syndrome (NRDS) in pregnancies affected by gestational diabetes mellitus (GDM).
Enrolled a prospective cohort of 50 GDM patients.
Utilized TensorFlow to extract 342 texture features from fetal lung images.
Measured uterine artery pulsatility index (PI) transvaginally according to ISUOG guidelines.
Employed a support vector machine (SVM) classifier for NRDS risk stratification.
Evaluated predictive performance of AI model, uterine artery PI, and their combination.
Uterine artery PI was significantly higher in the NRDS group (median 1.52 vs. 1.16; p < 0.001).
AI-based pulmonary texture analysis had 86.4% sensitivity and 78.6% specificity for NRDS.
Combining AI assessment with uterine artery PI ≥1.28 achieved 92.0% overall accuracy.
Decision curve analysis indicated superior net benefit of the combined model across relevant thresholds.

Abstract

Background: Gestational diabetes mellitus (GDM) elevates the risk of neonatal respiratory distress syndrome (NRDS), highlighting the need for robust predictive tools. Current assessments of fetal lung maturity assessments are invasive, creating a clinical demand for non-invasive alternatives. This study presents a dual-parameter framework that combines artificial intelligence (AI)-derived fetal lung texture signatures with uterine artery pulsatility index (PI) to predict NRDS risk in GDM pregnancies. Methods: A prospective cohort of 50 patients with GDM patients was enrolled. Standardized four-chamber view ultrasound images were processed using a TensorFlow-based framework to extract 342 gray-level co-occurrence matrix (GLCM) texture features from the fetal lungs. A support vector machine (SVM) classifier was then employed for NRDS risk stratification. Concurrently, uterine artery PI was measured transvaginally following International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) guidelines. The predictive performance of the AI model, uterine artery PI, and their combination was evaluated for predicting NRDS. Results: The uterine artery PI was significantly elevated in the NRDS group (n = 22) compared with controls (n = 28) (median 1.52 interquartile range, IQR: 1.35–1.70 vs. 1.16 IQR: 0.95–1.30; p < 0.001). The standalone AI-based pulmonary texture analysis achieved 86.4% sensitivity and 78.6% specificity for NRDS prediction, with substantial agreement with clinical diagnosis (κ = 0.67). The synergistic integration of an AI-based high-risk classification with a uterine artery PI ≥1.28 yielded superior predictive performance, attaining 92.0% overall accuracy (46/50). Decision curve analysis confirmed that the combined model provided a superior net benefit across clinically relevant threshold probabilities (10%–50%). Conclusions: The integration of AI-quantified fetal lung texture analysis with uterine artery Doppler hemodynamics provides a refined, non-invasive tool for NRDS risk stratification in pregnancies complicated by GDM. This dual-pathway framework effectively captures the interplay between placental vascular insufficiency and pulmonary immaturity, offering high diagnostic accuracy and clinical utility to guide perinatal management decisions.

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