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Updated: Nov 19, 2025

Protocol and Guidelines for Point-of-Care Lung Ultrasound in Diagnosing Neonatal Pulmonary Diseases Based on International Expert Consensus
Published on: March 6, 2019
Fetal lung maturity assessment: A historic perspective and Non - invasive assessment using an automatic quantitative
Badreldeen Ahmed1, Justin C Konje2
1Feto-Maternal Center Doha, Weill Cornell Medicine, Qatar University, Medical School, Doha, Qatar.
Abstract:
Immature fetal lung is associated with many adverse outcomes including respiratory distress syndrome and transient tachypnoea of the newborn. Several methods/tools have been used over several decades to assess fetal lung maturity prior to delivery. Some of the methods that have been used to assess fetal lung maturity include amniocentesis for the biochemical markers, lecithin and sphingomyelin, lamellar body counts, gray scale ultrasound scan and magnetic resonance imaging. Amniocentesis an invasive procedure which carries a small risk of miscarriage has almost become obsolete. Magnetic resonance imaging on the other hand is expensive and not very practical. Quantitative ultrasound fetal lung maturity (quantusFLM) assessment is a new technique aimed at assessing fetal lung texture using ultrasound. The technique depends on visualization of fetal lungs at the level of the 4- chamber view. Images obtained are then uploaded via a web page application and these are analyzed remotely and results generated in minutes. The analysis depends on studying changes in the texture of lung images that depend on changes at histological level especially of collagen, fat and water. These changes are undetectable to the human eye. Randomized clinical trials have shown this technique to be accurate, reproducible, and completely non - invasive. The aim of this review was to take a historic look at methods/tools for assessing fetal lug maturity and discuss further advances and a potential non-invasive tool/method especially the non-invasive assessment that combines ultrasound scan and machine learning to accurately assess lung maturity.
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