Quantitative ultrasound texture analysis of fetal lungs to predict neonatal respiratory morbidity
Open accessAbstract
Objective: To develop and evaluate the performance of a novel method for predicting neonatal respiratory morbidity based on quantitative analysis of the fetal lung by ultrasound. Methods: More than 13 000 non‐clinical images and 900 fetal lung images were used to develop a computerized method based on texture analysis and machine learning algorithms, trained to predict neonatal respiratory morbidity risk on fetal lung ultrasound images. The method, termed ‘quantitative ultrasound fetal lung maturity analysis’ (quantusFLM™), was then validated blindly in 144 neonates, delivered at 28 + 0 to 39 + 0 weeks' gestation. Lung ultrasound images in DICOM format were obtained within 48 h of delivery and the ability of the software to predict neonatal respiratory morbidity, defined as either respiratory distress syndrome or transient tachypnea of the newborn, was determined. Results: Mean (SD) gestational age at delivery was 36 + 1 (3 + 3) weeks. Among the 144 neonates, there were 29 (20.1%) cases of neonatal respiratory morbidity. Quantitative texture analysis predicted neonatal respiratory morbidity with a sensitivity, specificity, positive predictive value and negative predictive value of 86.2%, 87.0%, 62.5% and 96.2%, respectively. Conclusions: Quantitative ultrasound fetal lung maturity analysis predicted neonatal respiratory morbidity with an accuracy comparable to that of current tests using amniotic fluid. Copyright © 2014 ISUOG. Published by John Wiley & Sons Ltd.
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Ultrasound-based techniques, such as lung texture analysis and 3D volumetric quantification, achieve moderate accuracy (AUC = 0.82) in predicting RDS.
“Ultrasound-based techniques, such as lung texture analysis and 3D volumetric quantification, achieve moderate accuracy (AUC = 0.82) in predicting RDS (5, 6)”
From Placenta-pulmonary coupling-guided multimodal AI for fetal lung maturity staging and individualized glucocorticoid therapy (2026) · cites this paper as [5]
The Results report that fetal lung ultrasound texture analysis predicted neonatal respiratory morbidity, which included RDS and transient tachypnea, with 86.2% sensitivity and 87.0% specificity. The Methods describe analysis of a two-dimensional ultrasound image, not 3D volumetric quantification. The paper does not report an AUC of 0.82 or evaluate prediction of RDS alone. (LLM, conf=0.88)
Experimental — accuracy not yet measuredBonet-Carne et al. and Du et al. showed that automated fetal lung ultrasound was able to accurately predict neonatal respiratory morbidity.
“The study of Bonet-Carne et al. and a further study of Du et al. showed that automated fetal lung ultrasound was able to accurately predict neonatal respiratory morbidity [164,165].”
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Cites
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works
Textural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886 · see citing works
A theory for multiresolution signal decomposition: the wavelet representation
Mallat, S. (1989) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.192463 · Cited by 21,463 · see citing works
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472 · see citing works
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733 · see citing works
Orthonormal bases of compactly supported wavelets
Daubechies, I. C. (1988) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.3160410705 · Cited by 8,143 · see citing works
Robust regression using iteratively reweighted least-squares.
Holland, P. W., & Welsch, R. E. (1977) · Communications in Statistics - Theory and Methods · doi:10.1080/03610927708827533 · Cited by 2,115 · see citing works
Diagnosis of the respiratory distress syndrome by amniocentesis
Gluck, L., Kulovich, M. V., Borer, R. C., Brenner, P. H., Anderson, G. G., & Spellacy, W. N. (1971) · American Journal of Obstetrics and Gynecology · doi:10.1016/0002-9378(71)90342-5 · Cited by 904 · see citing works
Discriminant adaptive nearest neighbor classification
Hastie, T., & Tibshirani, R. J. (1996) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.506411 · Cited by 833 · see citing works
Timing of Elective Repeat Cesarean Delivery at Term and Neonatal Outcomes
Tita, A., Landon, M. B., Spong, C. Y., Lai, Y., Leveno, K. J., Varner, M. W., Moawad, A. H., Caritis, S. N., Meis, P. J., Wapner, R. J., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0803267 · Cited by 811 · see citing works
Outex - new framework for empirical evaluation of texture analysis algorithms
Ojala, T., Mäenpää, T., Pietikäinen, M., Viertola, J., Kyllönen, J., & Huovinen, S. (2003) · doi:10.1109/icpr.2002.1044854 · Cited by 706 · see citing works
Respiratory Morbidity in Late Preterm Births
Hibbard, J. U., Wilkins, I. A., Sun, L., Gregory, K. D., Haberman, S., Hoffman, M., Kominiarek, M. A., Reddy, U. M., Bailit, J. L., Branch, D. W., et al. (2010) · JAMA · doi:10.1001/jama.2010.1015 · Cited by 612 · see citing works
Timing of Indicated Late-Preterm and Early-Term Birth
Spong, C. Y., Mercer, B. M., D'Alton, M., Kilpatrick, S., Blackwell, S., & Saade, G. (2011) · Obstetrics & Gynecology · doi:10.1097/aog.0b013e3182255999 · Cited by 581 · see citing works
Lecithin/sphingomyelin ratios in amniotic fluid in normal and abnormal pregnancy
Gluck, L., & Kulovich, M. V. (1973) · American Journal of Obstetrics and Gynecology · doi:10.1016/0002-9378(73)90404-3 · Cited by 547 · see citing works
A systematic review of severe morbidity in infants born late preterm
Teune, M. J., Bakhuizen, S., Gyamfi Bannerman, C., Opmeer, B. C., van Kaam, A. H., van Wassenaer, A. G., Morris, J. M., & Mol, B. W. J. (2011) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2011.07.015 · Cited by 367 · see citing works
Neonatal and maternal outcomes associated with elective term delivery
Clark, S. L., Miller, D. D., Belfort, M. A., Dildy, G. A., Frye, D. K., & Meyers, J. A. (2008) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2008.08.068 · Cited by 317 · see citing works
Committee Opinion No. 560: Medically Indicated Late-Preterm and Early-Term Deliveries
(2013) · Obstetrics & Gynecology · doi:10.1097/01.aog.0000428648.75548.00 · Cited by 257 · see citing works
An iterative growing and pruning algorithm for classification tree design
Gelfand, S. B., Ravishankar, C., & Delp, E. J. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.67645 · Cited by 225 · see citing works
Adverse Neonatal Outcomes Associated With Early-Term Birth
Sengupta, S., Carrion, V., Shelton, J., Wynn, R. J., Ryan, R. M., Singhal, K. K., & Lakshminrusimha, S. (2013) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2013.2581 · Cited by 223 · see citing works
Delivery Indications at Late-Preterm Gestations and Infant Mortality Rates in the United States
Reddy, U. M., Ko, C., Raju, T. N. K., & Willinger, M. (2009) · PEDIATRICS · doi:10.1542/peds.2008-3232 · Cited by 183 · see citing works
Decision-based neural networks with signal/image classification applications
Kung, S., & Taur, J. S. (1995) · IEEE Transactions on Neural Networks · doi:10.1109/72.363439 · Cited by 165 · see citing works
Precursors for Late Preterm Birth in Singleton Gestations
Laughon, S. K., Reddy, U. M., Sun, L., & Zhang, J. (2010) · Obstetrics and Gynecology · doi:10.1097/aog.0b013e3181f73f97 · Cited by 100 · see citing works
ACOG Practice Bulletin No. 97: Fetal Lung Maturity
(2008) · Obstetrics and Gynecology · doi:10.1097/aog.0b013e318188d1c2 · Cited by 73 · see citing works
Lamellar body counts: a consensus on protocol
Neerhof, M. G. (2001) · Obstetrics and Gynecology · doi:10.1016/s0029-7844(00)01134-0 · Cited by 61 · see citing works
Ultrasonographic patterns of fetal breathing movements in normal pregnancy
Cosmi, E., Cosmi, E. V., Anceschi, M. M., et al. (2003) · International Journal of Gynecology & Obstetrics · doi:10.1016/s0020-7292(02)00384-3 · Cited by 59 · see citing works
Multicenter evaluation of TDx test for assessing fetal lung maturity
Russell, J. C., Cooper, C. M., Ketchum, C. H., et al. (1989) · Clinical Chemistry · doi:10.1093/clinchem/35.6.1005 · Cited by 57 · see citing works
Fetal lung maturity analysis using ultrasound image features
Prakash, B., Ramakrishnan, A. G., Suresh, S., & Chow, T. W. P. (2002) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/4233.992160 · Cited by 56 · see citing works
Theories of visual texture perception
Bergen, J. R. (1992) · Optical Society of America Annual Meeting · doi:10.1364/oam.1992.thz2 · Cited by 54 · see citing works
Karcher, R. E., Sykes, E., Batton, D. G., Uddin, Z., Ross, G. E., Hockman, E. M., & Shade, G. H. (2005) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2005.03.080 · Cited by 51 · see citing works
Wijnberger, L. D. E., Huisjes, A. J. M., Voorbij, H. A. M., Franx, A., Bruinse, H. W., & Mol, B. W. (2001) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/j.1471-0528.2001.00142.x · Cited by 51 · see citing works
Noninvasive Fetal Lung Maturity Prediction Based on Ultrasonic Gray Level Histogram Width
Serizawa, M., & Maeda, К. (2010) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2010.08.011 · Cited by 50 · see citing works
Lecithin/sphingomyelin ratio and lamellar body count for fetal lung maturity: a meta-analysis
Besnard, A. E., Wirjosoekarto, S. A. M., Broeze, K. A., Opmeer, B. C., & Mol, B. W. (2013) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2013.02.013 · Cited by 48 · see citing works
Cobo, T., Bonet-Carne, E., Martínez-Terrón, M., Perez-Moreno, A., Elías, N., Luque, J., Amat-Roldan, I., & Palacio, M. (2012) · Fetal Diagnosis and Therapy · doi:10.1159/000335349 · Cited by 43 · see citing works
Self-citationEchogenicity of fetal lung and liver quantified by the grey-level histogram width
Maeda, К., Utsu, M., Yamamoto, N., & Serizawa, M. (1999) · Ultrasound in Medicine & Biology · doi:10.1016/s0301-5629(98)00160-4 · Cited by 42 · see citing works
TEKESIN, I., Anderer, G., Hellmeyer, L., Stein, W., Kühnert, M., & Schmidt, S. (2004) · Prenatal Diagnosis · doi:10.1002/pd.951 · Cited by 42 · see citing works
Hagen, E. C., Jc, L., & Arias, F. (1993) · PubMed · Cited by 40 · see citing works
Palacio, M., Cobo, T., Martínez-Terrón, M., Ratta, G. A., Bonet-Carné, E., Amat‐Roldan, I., & Gratacós, E. (2012) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2012.09.027 · Cited by 37 · see citing works
Self-citationHaymond, S., Luzzi, V. I., Parvin, C. A., et al. (2006) · American Journal of Clinical Pathology · doi:10.1309/8vxn5em5l3831at2 · Cited by 28 · see citing works
Tenorio, V., Bonet-Carné, E., Botet, F., et al. (2011) · Journal of Ultrasound in Medicine · doi:10.7863/jum.2011.30.10.1365 · Cited by 21 · see citing works
Self-citationPreliminary report on a new and noninvasive method for the assessment of fetal lung maturity
Torre, R. L., Cosmi, E. V., Anceschi, M. M., Piazze, J., Piga, M., & Cosmi, E. V. (2003) · Journal of Perinatal Medicine · doi:10.1515/jpm.2003.067 · Cited by 18 · see citing works
The case for amniocentesis for fetal lung maturity in late-preterm and early-term gestations
Towers, C. V., Freeman, R. K., Nageotte, M. P., et al. (2013) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2013.10.004 · Cited by 17 · see citing works
Insana, M. F., Garra, B. S., Rosenthal, S. J., & Hall, T. J. (1989) · PubMed · Cited by 15 · see citing works
National trends in primary cesarean delivery, labor attempts, and labor success, 1990-2010
Simon, A. E., & Uddin, S. G. (2013) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2013.08.012 · Cited by 13 · see citing works
Flagged first, then by citations
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Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology
Drukker, L., Noble, J. A., & Papageorghiou, A. T. (2020) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.22122 · Cited by 240 · see citing works
Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects
Torrents-Barrena, J., Piella, G., Masoller, N., Gratacós, E., Eixarch, E., Ceresa, M., & Ballester, M. Á. G. (2019) · Medical Image Analysis · doi:10.1016/j.media.2018.10.003 · Cited by 97 · see citing works
Artificial intelligence in obstetric ultrasound: A scoping review.
Horgan, R., Nehme, L., & Abuhamad, A. (2023) · Prenatal Diagnosis · doi:10.1002/pd.6411 · Cited by 72 · see citing works
“112,128 Recent literature has suggested the need for a more judicious approach to administration of antenatal cortico-steroids due to growing concerns about potential long ‐ term neuro-developmental effects of in ‐ utero corticosteroid exposure.”
BackgroundArtificial Intelligence in Prenatal Ultrasound Diagnosis
He, F., Wang, Y., Xiu, Y., Zhang, Y., & Chen, L. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.729978 · Cited by 62 · see citing works
Jost, E., Kosian, P., Jimenez Cruz, J., Albarqouni, S., Gembruch, U., Strizek, B., & Recker, F. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12216833 · Cited by 44 · see citing works
Du, Y., Fang, Z., Jiao, J., Xi, G., Zhu, C., Ren, Y., Guo, Y., & Wang, Y. (2021) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.22037 · Cited by 29 · see citing works
Xia, T.-H., Tan, M., Li, J.-H., Wang, J.-J., Wu, Q.-Q., & Kong, D.-X. (2021) · Chinese Medical Journal · doi:10.1097/cm9.0000000000001547 · Cited by 24 · see citing works
Chen, P., Chen, Y., Deng, Y., Wang, Y., He, P., Lv, X., & Yu, J. (2020) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-020-02211-1 · Cited by 15 · see citing works
Arora, U., Sengupta, D., Kumar, M., Tirupathi, K., Sai, M. K., Hareesh, A., Sai Chaithanya, E. S., Nikhila, V., Bhavana, N., Vigneshwar, P., et al. (2023) · Placenta · doi:10.1016/j.placenta.2023.07.014 · Cited by 11 · see citing works
Du, Y., Jiao, J., Ji, C., Li, M., Guo, Y., Wang, Y., Zhou, J., & Ren, Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-17129-8 · Cited by 10 · see citing works
Ma, B., Ran, J., Pan, T., Yan, F., Yue, Z., Yao, Y., Li, Y., & Nie, F. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1791481
“Ultrasound-based techniques, such as lung texture analysis and 3D volumetric quantification, achieve moderate accuracy (AUC = 0.82) in predicting RDS (5, 6), while placental elastography reveals correlations between tissue stiffness and lung delay (7).”
Influential
- Authors
- Bonet‐Carne, E., Palacio, M., Cobo, T., Perez‐Moreno, A., Lopez, M., Piraquive, J. P., Ramirez, J. C., Botet, F., Marques, F., & Gratacos, E.
- Affiliations
- Transmural Biotech SL Barcelona Spain
- Centre for Biomedical Network Research on Rare Diseases (ES)
- Department of Maternal‐Fetal Medicine, ICGON, Hospital Clinic Barcelona University of Barcelona Spain
- Hospital Clínic de Barcelona (ES)
- Universitat Politecnica de Catalunya BarcelonaTECH Barcelona Spain
- Venue
- Ultrasound in Obstetrics & Gynecology
- Publisher
- Wiley
- Published
- 2015-04
- Volume
- 45
- Issue
- 4
- Pages
- 427-433
- ISSN
- 0960-7692 (ISSN-L)
- 1469-0705
- OpenAlex
- W2002484772
- Cited by
- 85
- References
- 44
- Citations by year
- 2026: 7 · 2025: 3 · 2024: 4 · 2023: 16 · 2022: 14 · 2021: 6 · 2020: 12 · 2019: 5 · 2018: 4 · 2017: 7 · 2016: 6 · 2015: 1
- FWCI
- 3.74
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Ultrasound in Obstetrics and Gynecology (publishedVersion)
- PubMed (publishedVersion)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (acceptedVersion, other-oa)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (acceptedVersion, cc-by-nc-nd)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc-nd)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Neonatal Respiratory Health Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- fetal lung ultrasound, texture analysis, neonatal respiratory morbidity, respiratory distress syndrome, transient tachypnea of the newborn, fetal lung maturity, preterm infants, machine learning prediction
- MeSH terms
- Adult, Algorithms, Amniotic Fluid, Evaluation Studies as Topic, Female, Fetal Organ Maturity, Humans, Infant, Newborn, Lung, Predictive Value of Tests, Pregnancy, Prospective Studies, Respiratory Distress Syndrome, Newborn, Ultrasonography, Prenatal, Delivery, Obstetric
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