Objective: To explore the value of the ultrasound image transfer learning depth model based on the four-chamber heart view to quantitatively evaluate the fetal lung development and maturity.Methods: Select singleton pregnancies who have undergone routine pregnancy checkups in our hospital from January 2019 to December 2020

and use the ultrasound imaging system to collect four-chamber heart image data of the fetal heart from the gestational age of 24 weeks to before birth. A two-stage transfer learning method is adopted to establish a deep learning model that designs a specific U-net structure. This study assumes that the development of fetal lung maturity is usually proportional to gestational age. In the first stage

the model learns the recognition of the fetal lung region in the ultrasound image; in the second stage

the pre-trained depth model is trained to accurately estimate the gestational age from the fetal lung region in the ultrasound image.Results: In this study

a total of 316 patients’ ultrasound image data were collected. The first 166 patients were used for the model training of the first stage of transfer learning

and the next 150 patients were used for independent testing. The inaccuracy of the established depth model on the gestational age estimation test results compared with the true value is 1.43±2.01 weeks

and its correlation coefficient with the true gestational age value is 0.773 (95%CI 0.6893-0.8314

P<0.05)

After transfer learning

the estimation result of the deep model is significantly better than the estimation result of the traditional method (P<0.05). Conclusion: This study preliminarily verified the hypothesis that the fetal lung texture information in ultrasound images can reflect the developmental maturity of fetal lungs. Fetal lung maturity can be represented by the output of the depth model after transfer learning represented by the estimated gestational age.