Multimodal Convolutional Neural Network Model for Evaluating Development of Fetal Rabbit Lung Using B-mode and Shear Wave Elastography Ultrasound Images.

OBJECTIVE To evaluate the performance of convolutional neural network (CNN)-based models for predicting fetal rabbit lung development: unimodal models using B-mode and shear wave elastography (SWE) and a multimodal model combining B-mode and SWE.

METHODS A total of 1670 ultrasound fetal lung images (B-mode and SWE) were acquired from 167 fetal rabbits (23-30 d gestation). Post-cesarean, body weight, Apgar scores, interstitial lung ratios and alveolar lavage dipalmitoylphosphatidylcholine levels were measured. The developed CNN models, based on fetal lung histological classification (canalicular, saccular and alveolar stages), extracted features from B-mode or SWE images to predict fetal lung development.

RESULTS The SWE unimodal model (95.9%) showed superior total accuracy over the B-mode (86.5%) and multimodal models (87.9%) and outperformed them on most metrics for predicting the canalicular and saccular stages (p < 0.05). For the alveolar stage, SWE (96.2%, 96.4%, 93.5%, 0.890) and multimodal models (95.9%, 98.6%, 97.3%, 0.890) outperformed B-mode (86.8%, 79.6%, 72.7%, 0.670) in accuracy, specificity, positive predictive value (PPV) and area under the curve (AUC) (p < 0.05), with the multimodal model showing a slight advantage in specificity (98.6%) and PPV (97.3%).

CONCLUSION The SWE-based models can predict fetal rabbit lung development and may serve as a promising non-invasive tool for clinical assessment of fetal lung maturity.