Background Prediction of neonatal respiratory morbidity may be useful to plan delivery in complicated pregnancies. The limited predictive performance of the current diagnostic tests together with the risks of an invasive procedure restricts the use of fetal lung maturity assessment. Objective The objective of this study was to evaluate the performance of quantitative ultrasound texture analysis of the fetal lung (quantusFLM) to predict neonatal respiratory morbidity in preterm and early-term (<39.0 weeks) deliveries. Study Design This was a prospective multicenter study conducted in 20 centers worldwide. Fetal lung ultrasound images were obtained at 25.0-38.6 weeks of gestation within 48 hours of delivery, stored in Digital Imaging and Communication in Medicine format, and analyzed with quantusFLM. Physicians were blinded to the analysis. At delivery, perinatal outcomes and the occurrence of neonatal respiratory morbidity, defined as either respiratory distress syndrome or transient tachypnea of the newborn, were registered. The performance of the ultrasound texture analysis test to predict neonatal respiratory morbidity was evaluated. Results A total of 883 images were collected, but 17.3% were discarded because of poor image quality or exclusion criteria, leaving 730 observations for the final analysis. The prevalence of neonatal respiratory morbidity was 13.8% (101/730). The quantusFLM predicted neonatal respiratory morbidity with a sensitivity, specificity, and positive and negative predictive values of 74.3% (75/101), 88.6% (557/629), 51.0% (75/147), and 95.5% (557/583), respectively. Accuracy was 86.5% (632/730), and the positive and negative likelihood ratios were 6.5 and 0.3, respectively. Conclusion The quantusFLM predicted neonatal respiratory morbidity with an accuracy similar to that previously reported for other tests with the advantage of being a non-invasive technique.

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Objective: To evaluate the feasibility and reproducibility of fetal lung texture analysis using a novel automatic quantitative ultrasound analysis and to assess its correlation with gestational age. Methods: Prospective cross-sectional observational study. To evaluate texture features, 957 left and right lung images in a 2D four-cardiac-chamber view plane were previously delineated from fetuses between 20 and 41 weeks of gestation. Quantification of lung texture was performed by the Automatic Quantitative Ultrasound Analysis (AQUA) software to extract image features. A standard learning approach composed of feature transformation and a regression model was used to evaluate the association between texture features and gestational age. Results: The association between weeks of gestation and fetal lung texture quantified by the AQUA software presented a Pearson correlation of 0.97. The association was not influenced by delineation parameters such as region of interest (ROI) localization, ROI size, right/left lung selected or sonographic parameters such as ultrasound equipment or transducer used. Conclusions: Fetal lung texture analysis measured by the AQUA software demonstrated a strong correlation with gestational age. This supports further research to explore the use of this technology to the noninvasive prediction of fetal lung maturity.

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