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A preliminary study to quantitatively evaluate the development of maturation degree for fetal lung based on transfer learning deep model from ultrasound images

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

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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Funding

This study was funded by the Shanghai Science and Technology Innovation Plan (Grant No. 19441903100) and Shanghai municipal medical and health discipline construction projects (Grant No. 2017ZZ02015).

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Author notes

  1. Ping Chen and Yunqi Chen have equally contributed to this work.

Authors and Affiliations

  1. Ultrasound Department, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, 200040, China

    Ping Chen, Yunqi Chen, Ping He & Xiaoli Lv

  2. Department of Electronic Engineering, Fudan University, Shanghai, 200433, China

    Yinhui Deng, Yuanyuan Wang & Jinhua Yu

Authors
  1. Ping Chen
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  2. Yunqi Chen
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  3. Yinhui Deng
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  4. Yuanyuan Wang
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  5. Ping He
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  6. Xiaoli Lv
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  7. Jinhua Yu
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Corresponding authors

Correspondence to Yinhui Deng or Jinhua Yu.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

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Informed consent was obtained from all individual participants included in the study.

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Chen, P., Chen, Y., Deng, Y. et al. A preliminary study to quantitatively evaluate the development of maturation degree for fetal lung based on transfer learning deep model from ultrasound images. Int J CARS 15, 1407–1415 (2020). https://doi.org/10.1007/s11548-020-02211-1

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  • DOI: https://doi.org/10.1007/s11548-020-02211-1

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