A preliminary study to quantitatively evaluate the development of maturation degree for fetal lung based on transfer learning deep model from ultrasound images
Abstract
Purpose The evaluation of fetal lung maturity is critical for clinical practice since the lung immaturity is an important cause of neonatal morbidity and mortality. For the evaluation of the development of fetal lung maturation degree, our study established a deep model from ultrasound images of four-cardiac-chamber view plane.Methods A two-stage transfer learning approach is proposed for the purpose of the study. A specific U-net structure is designed for the applied deep model. In the first stage, the model is to first learn the recognition of fetal lung region in the ultrasound images. It is hypothesized in our study that the development of fetal lung maturation degree is generally proportional to the gestational age. Then, in the second stage, the pretrained deep model is trained to accurately estimate the gestational age from the fetal lung region of ultrasound images.Results Totally 332 patients were included in our study, while the first 206 patients were used for training and the subsequent 126 patients were used for the independent testing. The testing results of the established deep model have the imprecision as 1.56 ± 2.17 weeks on the gestational age estimation. Its correlation coefficient with the ground truth of gestational age achieves 0.7624 (95% CI 0.6779 to 0.8270, P value < 0.00001).Conclusion The hypothesis that the development of fetal lung maturation degree can be represented by the texture information from ultrasound images has been preliminarily validated. The fetal lung maturation degree can be considered as being represented by the deep model's output denoted by the estimated gestational age.
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Xia et al. and Chen et al. developed a lung maturation grading model that can be implemented for identifying abnormal development and evaluating the effectiveness of antenatal corticosteroid therapy.
“while Xia et al. and Chen et al. developed a lung maturation grading model that can be implemented for identifying abnormal development and evaluating the effectiveness of antenatal corticosteroid therapy [161,163].”
Xia et al. and Chen et al. were limited by the analysis of only healthy fetuses.
“the studies of Xia et al. and Chen et al. were limited by the analysis of only healthy fetuses [161,163].”
AI model performance in 2D and 3D US imaging can be limited due to imaging artefacts and noise, especially when automated tissue analysis is intended.
“AI model performance in 2D and 3D US imaging can be limited due to imaging artefacts and noise, especially when automated tissue analysis is intended, for example, for fetal lung assessment”
MRI seems future-oriented and promising for fetal lung assessment.
“whereas the method of MRI for this specific application seems future-oriented and promising [127,163].”
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Use of artificial intelligence and deep learning in fetal ultrasound imaging
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“…AI model performance in 2D and 3D US imaging can be limited due to imaging artefacts and noise, especially when automated tissue analysis is intended, for example, for fetal lung assessment, whereas the method of MRI for this specific application seems future-oriented and promising [127,163].”
Background
- Authors
- Chen, P., Chen, Y., Deng, Y., Wang, Y., He, P., Lv, X., & Yu, J.
- Affiliations
- Tongji University (CN)
- Fudan University (CN)
- Corresponding author
- Deng, Yinhui
- Yu, Jinhua
- Venue
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2020-06-15
- Volume
- 15
- Issue
- 8
- Pages
- 1407-1415
- ISSN
- 1861-6410 (ISSN-L)
- 1861-6429
- OpenAlex
- W3035944138
- Cited by
- 15
- References
- 18
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 5 · 2022: 4 · 2021: 2 · 2020: 1
- FWCI
- 1.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Computer Assisted Radiology and Surgery (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Shanghai Science and Technology Innovation Plan (19441903100)
- Shanghai municipal medical and health discipline construction projects (2017ZZ02015)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- 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
- Keywords
- fetal lung maturation, gestational age estimation, fetal lung ultrasound, transfer learning, U-Net, four-chamber view, texture features
- MeSH terms
- Machine Learning, Female, Gestational Age, Humans, Infant, Newborn, Lung, Pregnancy, Ultrasonography, Prenatal
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