ArticleScientific reports2019
Evaluation of an improved tool for non-invasive prediction of neonatal respiratory morbidity based on fully automated fetal lung ultrasound analysis.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 49 citations in OpenAlex.
- Artificial intelligence and digital technologies transforming neonatal respiratory assessment: a scoping review.Journal of perinatology : official journal of the California Perinatal Association · 2026Review
- Artificial intelligence in preterm birth prediction: a narrative review of current approaches and clinical applicability.Obstetrics & gynecology science · 2026Review
- Quantitative assessment of fetal lung development using MRI-based radiomics: a comparative analysis of 3D and 2D approaches with external validation.Frontiers in physiology · 2026Article
- Review
- Fetal MRI radiomics: non-invasive and reproducible quantification of human lung maturity.European radiology · 2023Article
- Quantitative lung ultrasound detects dynamic changes in lung recruitment in the preterm lamb.Pediatric research · 2023Article
- Reproducibility of 2D versus 3D radiomics for quantitative assessment of fetal lung development: a retrospective fetal MRI study.Insights into imaging · 2023Article
- Applications of artificial intelligence in obstetrics.Ultrasonography (Seoul, Korea) · 2023Article
- Assessment of the fetal lungs in utero.American journal of obstetrics & gynecology MFM · 2022Review
- Prediction of Neonatal Respiratory Morbidity Assessed by Quantitative Ultrasound Lung Texture Analysis in Twin Pregnancies.Journal of clinical medicine · 2022Article
- Ultrasound-based radiomics technology in fetal lung texture analysis prediction of neonatal respiratory morbidity.Scientific reports · 2022Article
- Concordance of the risk of neonatal respiratory morbidity assessed by quantitative ultrasound lung texture analysis in fetuses of twin pregnancies.Scientific reports · 2022Article
- Application of ultrasonography in neonatal lung disease: An updated review.Frontiers in pediatrics · 2022Review
- A 10-Year Retrospective Review of Prenatal Applications, Current Challenges and Future Prospects of Three-Dimensional Sonoangiography.Diagnostics (Basel, Switzerland) · 2021Review
- Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video.Scientific reports · 2021Article
- Review
- Artificial Intelligence in Prenatal Ultrasound Diagnosis.Frontiers in medicine · 2021Review
- Article
- Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objective of this study was to evaluate the performance of a new version of quantusFLM®, a software tool for prediction of neonatal respiratory morbidity (NRM) by ultrasound, which incorporates a fully automated fetal lung delineation based on Deep Learning techniques. A set of 790 fetal lung ultrasound images obtained at 24 + 0-38 + 6 weeks' gestation was evaluated. Perinatal outcomes and the occurrence of NRM were recorded. quantusFLM® version 3.0 was applied to all images to automatically delineate the fetal lung and predict NRM risk. The test was compared with the same technology but using a manual delineation of the fetal lung, and with a scenario where only gestational age was available. The software predicted NRM with a sensitivity, specificity, and positive and negative predictive value of 71.0%, 94.7%, 67.9%, and 95.4%, respectively, with an accuracy of 91.5%. The accuracy for predicting NRM obtained with the same texture analysis but using a manual delineation of the lung was 90.3%, and using only gestational age was 75.6%. To sum up, automated and non-invasive software predicted NRM with a performance similar to that reported for tests based on amniotic fluid analysis and much greater than that of gestational age alone.
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