A Simplified and Data-Driven Lung Ultrasound Approach for Predicting Surfactant Need in Preterm Infants: A Pilot Study Using Machine Learning and Rule-Based Models.

Journal: Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Published Date: Oct 17, 2025

Abstract

OBJECTIVES: Six-region lung ultrasound (LUS) scores show good predictive value for predicting surfactant need in preterm infants but rely on a fixed threshold, which may lead to misclassification near the cut-off and lack data-driven justification for selecting these 6 regions. This study explored whether evaluating individual regions-and combinations-could improve predictive accuracy and utility. METHODS: Data from preterm infants born at ≤34 weeks and enrolled in the Serial Lung Ultrasound for Surfactant Replacement Therapy (SLURP) cohort study were analyzed to develop predictive models for surfactant administration based on regional LUS scores. Univariate, bivariate, and machine learning analyses were conducted to identify the most informative lung regions. Rule-based, decision tree, and logistic regression models were then developed, compared to the 6-region model, and validated on an external dataset. RESULTS: The training set consisted of 77 patients from the SLURP cohort study. The rule-based, decision tree, and logistic regression models showed the best performance, primarily using 2 lung regions-left lateral and left upper posterior. A refined model that included the right upper anterior (RUA) region further improved performance. On the external test set (n = 42), the rule-based model with RUA achieved the highest accuracy (0.93) and the lowest false negative rate (0.11), outperforming the 6-region model. Adding more regions did not enhance accuracy. CONCLUSIONS: A simplified, rule-based model that accounts for the differential predictive value of individual lung regions may enhance the accuracy of LUS-based prediction of surfactant need in preterm infants. It is also more accessible, effective, and time-efficient for clinicians.

Authors

  • Virginie Meau-Petit
    Neonatal Unit, Franco-Britannique Hospital, Levallois-Perret, France.
  • Clemence Mottez
    Centre of Artificial Intelligence in Medicine and Imaging, Stanford University, Stanford, California, USA.
  • Bikash Bhojnagarwala
    Oliver Fisher Neonatal Unit, Medway NHS Foundation Trust, Kent, UK.
  • Mahmoud Montasser
    Neonatal Unit, University Hospital Wishaw, Scotland, UK.
  • Yogen Singh
    Department of Paediatric Cardiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
  • Prakash Kannan Loganathan
    Neonatal Unit, James Cook University Hospital, Middlesbrough, UK.

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