Application of ultrasound-based radiomics technology in fetal-lung-texture analysis in pregnancies complicated by gestational diabetes and/or pre-eclampsia
Open accessAbstract
OBJECTIVES: To analyze and compare, using ultrasound-based radiomics technology, fetal-lung texture in pregnancies affected by gestational diabetes mellitus (GDM) and/or pre-eclampsia (PE) and in normal pregnancies, overall and at different gestational ages. METHODS: In this retrospective study, 430 high-throughput features per fetal-lung image were extracted from 548 fetal-lung ultrasound images (obtained at the level of the four-chamber view of the heart) in 548 pregnant women who delivered between July 2018 and August 2019 at the Obstetrics and Gynecology Hospital of Fudan University. Images had been obtained during ultrasound examinations between 28 and 41 weeks of gestation. The data were divided randomly into training set (80% of fetal-lung images) and independent test set (20% of images), and 20% of the images in the training set were then selected as the validation set. A standard machine-learning model based on ultrasound-based radiomics technology was created using features of fetal-lung texture extracted from the images, and a regression model was used to evaluate the relationship between lung-texture features, GDM and/or PE and gestational age. RESULTS: Of the 548 pregnancies included, 108 were affected by GDM alone, 71 by PE alone and 25 by both GDM and PE, and 344 were normal. The overall performance of the GDM and PE prediction model was superior to that of the gestational-age prediction model, with an area under the receiver-operating-characteristics curve of 0.95-0.99, sensitivity of 78.8-97.1% in the validation set and 74.5-91.3% in the independent test set, specificity of 79.8-94.3% in the validation set and 75.7-88.4% in the independent test set and accuracy of 81.0-95.3% in the validation set and 80.6-86.4% in the independent test set. CONCLUSIONS: Using ultrasound-based radiomics technology, fetal lungs from pregnancies grouped according to whether they were affected by GDM and/or PE could be distinguished from each other and from fetal lungs of normal pregnancies, and lungs from pregnancies at different gestational ages could be distinguished. These findings support further research to explore the use of this non-invasive technology to predict neonatal respiratory complications in women with PE, GDM or their combination. © 2020 International Society of Ultrasound in Obstetrics and Gynecology.
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Manual selection of the ROI is still a limiting aspect in AI performance for tissue analysis applications.
“In applications of tissue analysis such as assessment of the endometrium in gynecology [207,209] and fetal lung texture [162], or identification of the cervix [166] in obstetrics, manual selection of the ROI is still a limiting aspect in AI performance.”
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 [162]
The Ultrasound imaging and segmentation section says radiologists manually delineated fetal-lung ROIs, supporting that part of the citation. It does not identify manual selection as a limit on AI performance; the Discussion instead says the analysis was not influenced by differences in ROI location, size, or shape. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredDu et al. proposed an AI model to classify the lung textures in pregnancies affected by gestational diabetes or preeclampsia.
“Du et al. proposed an AI model to classify the lung textures in pregnancies affected by gestational diabetes or preeclampsia [162]”
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- Authors
- Du, Y., Fang, Z., Jiao, J., Xi, G., Zhu, C., Ren, Y., Guo, Y., & Wang, Y.
- Affiliations
- Shanghai Medical College of Fudan University (CN)
- Fudan University (CN)
- Corresponding author
- Ren, Y.
- Guo, Y.
- Wang, Y.
- Venue
- Ultrasound in Obstetrics & Gynecology
- Publisher
- Wiley
- Published
- 2021-05
- Volume
- 57
- Issue
- 5
- Pages
- 804-812
- ISSN
- 0960-7692 (ISSN-L)
- 1469-0705
- OpenAlex
- W3014972479
- Cited by
- 29
- References
- 29
- Citations by year
- 2026: 4 · 2025: 1 · 2024: 2 · 2023: 9 · 2022: 8 · 2021: 4 · 2020: 1
- FWCI
- 2.66
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Ultrasound in Obstetrics and Gynecology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (17411953400, 61771143, 61871135)
- Fudan University
- Science and Technology Commission of Shanghai Municipality (17411953400, 18511102904)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Obstetrics and Gynecology › Gestational Diabetes Research and Management
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- gestational diabetes mellitus, preeclampsia, gestational age estimation, fetal lung ultrasound, machine learning prediction models, pregnancy complications, fetal lung development
- MeSH terms
- Machine Learning, Adult, Female, Fetal Diseases, Fetus, Gestational Age, Humans, Lung, Male, Pre-Eclampsia, Predictive Value of Tests, Pregnancy, Retrospective Studies, ROC Curve, Sensitivity and Specificity, Ultrasonography, Prenatal, Diabetes, Gestational
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