Ultrasound-based radiomics technology in fetal lung texture analysis prediction of neonatal respiratory morbidity
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Abstract To develop a novel method for predicting neonatal respiratory morbidity (NRM) by ultrasound-based radiomics technology. In this retrospective study, 430 high-throughput features per fetal-lung image were extracted from 295 fetal lung ultrasound images (four-chamber view) in 295 single pregnancies. Images had been obtained between 28+3 and 37+6 weeks of gestation within 72 h before delivery. A machine-learning model built by RUSBoost (Random under-sampling with AdaBoost) architecture was created using 20 radiomics features extracted from the images and 2 clinical features (gestational age and pregnancy complications) to predict the possibility of NRM. Of the 295 standard fetal lung ultrasound images included, 210 in the training set and 85 in the testing set. The overall performance of the neonatal respiratory morbidity prediction model achieved AUC of 0.88 (95% CI 0.83–0.92) in the training set and 0.83 (95% CI 0.79–0.97) in the testing set, sensitivity of 84.31% (95% CI 79.06–89.44%) in the training set and 77.78% (95% CI 68.30–87.43%) in the testing set, specificity of 81.13% (95% CI 78.16–84.07%) in the training set and 82.09% (95% CI 77.65–86.62%) in the testing set, and accuracy of 81.90% (95% CI 79.34–84.41%) in the training set and 81.18% (95% CI 77.33–85.12%) in the testing set. Ultrasound-based radiomics technology can be used to predict NRM. The results of this study may provide a novel method for non-invasive approaches for the prenatal prediction of NRM.
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Bonet-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].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [164]
The Abstract reports that Du et al.’s machine-learning model predicted neonatal respiratory morbidity from fetal lung ultrasound images, with testing-set accuracy of 81.18%. But the Discussion says the lung regions were delineated manually and automatic identification was planned for the future, so the study does not support describing its ultrasound analysis as fully automated. (LLM, conf=0.86)
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“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].”
- Authors
- Du, Y., Jiao, J., Ji, C., Li, M., Guo, Y., Wang, Y., Zhou, J., & Ren, Y.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Fudan University (CN)
- Shanghai University of Traditional Chinese Medicine (CN)
- Obstetrics and Gynecology Hospital of Fudan University (CN)
- Corresponding author
- Guo, Yi
- Wang, Yuanyuan
- Zhou, Jianqiao
- Ren, Yunyun
- Venue
- Scientific Reports
- Published
- 2022-07-26
- Volume
- 12
- Issue
- 1
- Pages
- 12747-12747
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC9325724
- OpenAlex
- W4288180806
- Cited by
- 10
- References
- 32
- Citations by year
- 2026: 5 · 2025: 1 · 2024: 1 · 2023: 3
- FWCI
- 0.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Science and Technology Innovation Project of Health System in Putuo District, Shanghai (ptkwws202114)
- National Natural Science Foundation of China (61871135, 82071928)
- the Science and Technology Commission of Shanghai Municipality (20DZ1100104)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Neonatal Respiratory Health Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- neonatal respiratory morbidity, fetal lung ultrasound, radiomics, lung texture analysis, prenatal prediction, RUSBoost, four-chamber view, noninvasive prenatal diagnosis
- MeSH terms
- Female, Humans, Lung, Morbidity, Pregnancy, Retrospective Studies, Technology, Ultrasonography
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