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.

中文翻译：

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基于超声图像的转移学习深度模型，定量评估胎儿肺成熟度发展的初步研究。

目的

胎儿肺成熟度的评估对于临床实践至关重要，因为肺部不成熟是新生儿发病率和死亡率的重要原因。为了评估胎儿肺成熟度的发展，我们的研究从四心腔视平面的超声图像建立了一个深层模型。

方法

为了研究的目的，提出了一种两阶段的转移学习方法。针对所应用的深度模型设计了特定的U-net结构。在第一阶段，该模型是首先学习超声图像中胎儿肺区域的识别。在我们的研究中假设胎儿肺成熟度的发展通常与胎龄成正比。然后，在第二阶段，训练预训练的深度模型以从超声图像的胎儿肺区域准确估计胎龄。

结果

我们的研究总共包括332位患者，而前206位患者用于培训，随后的126位患者用于独立测试。建立的深度模型的测试结果对胎龄的估计不准确，为1.56±2.17周。其与胎龄事实的相关系数达到0.7624（95％CI为0.6779至0.8270，P值<0.00001）。

结论

初步验证了胎儿肺成熟程度的发展可以由超声图像中的纹理信息表示的假说。胎儿肺成熟度可以认为是由深模型的输出表示的，该输出由估计的胎龄表示。