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Assessing fetal lung maturity: Integration of ultrasound radiomics and deep learning

Wanming Chen(1), Baohui Zeng(2), Xiaoyan Ling(3), Chen Chen(4), Jichuang Lai(5), Jianru Lin(6), Xihong Liu(7), Huien Zhou(8), Xinmin Guo(9),


(1) Department of Ultrasound, Guangzhou Red Cross Hospital (Guangzhou Red Cross Hospital of Jinan University), Guangzhou, Guangdong 510220, China
(2) Department of Ultrasound, Guangzhou Red Cross Hospital (Guangzhou Red Cross Hospital of Jinan University), Guangzhou, Guangdong 510220, China
(3) Department of Ultrasound, Guangzhou Red Cross Hospital (Guangzhou Red Cross Hospital of Jinan University), Guangzhou, Guangdong 510220, China
(4) Department of Ultrasound, Guangzhou Red Cross Hospital (Guangzhou Red Cross Hospital of Jinan University), Guangzhou, Guangdong 510220, China
(5) Department of Ultrasound, Guangzhou Red Cross Hospital (Guangzhou Red Cross Hospital of Jinan University), Guangzhou, Guangdong 510220, China
(6) Department of Ultrasound, Guangzhou Red Cross Hospital (Guangzhou Red Cross Hospital of Jinan University), Guangzhou, Guangdong 510220, China
(7) Department of Ultrasound, Guangzhou Red Cross Hospital (Guangzhou Red Cross Hospital of Jinan University), Guangzhou, Guangdong 510220, China
(8) Department of Ultrasound, Guangzhou Red Cross Hospital (Guangzhou Red Cross Hospital of Jinan University), Guangzhou, Guangdong 510220, China
(9) Department of Ultrasound, Guangzhou Red Cross Hospital (Guangzhou Red Cross Hospital of Jinan University), Guangzhou, Guangdong 510220, China
Corresponding Author

Abstract


This study built a model to forecast the maturity of lungs by blending radiomics and deep learning methods. We examined ultrasound images from 263 pregnancies in the pregnancy stages. Utilizing the GE VOLUSON E8 system we captured images to extract and analyze radiomic features. These features were integrated with clinical data by means of deep learning algorithms such as DenseNet121 to enhance the accuracy of assessing fetal lung maturity. This combined model was validated by receiver operating characteristic (ROC) curve, calibration diagram, as well as decision curve analysis (DCA). We discovered that the accuracy and reliability of the diagnosis indicated that this method significantly improves the level of prediction of fetal lung maturity. This novel non-invasive diagnostic technology highlights the potential advantages of integrating diverse data sources to enhance prenatal care and infant health. The study lays groundwork, for validation and refinement of the model across various healthcare settings.

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