Automatic diagnosis of early pregnancy fetal nasal bone development based on complex mid-sagittal section ultrasound imaging

Early prenatal screening of fetal nasal bone (FNB) development is crucial for detecting chromosomal abnormalities. Existing deep learning approaches primarily focus on detection rather than diagnosis of FNB. This paper introduces an early prenatal FNB development automated diagnostic system (FNB-ADS), which employs a cascaded hierarchical filtering method to reduce noise interference in mid-sagittal plane ultrasound images. Specifically, the system employs YOLOv8 for precise FNB localization, segments the nasal bone, tip, and prenasal skin using a specially designed lightweight segmentation network, and diagnoses developmental abnormalities using Resnet34 classification methods. Furthermore, this paper has collected and publicly released the FNB-UDV dataset, which includes a detection subset and a video subset. The detection subset contains 1,007 two-dimensional ultrasound images, while the video subset comprises 12 ultrasound videos. Upon a comprehensive evaluation, the diagnostic accuracy of FNB-ADS reaches 92.37% with a processing time of 0.14 s per image, and the video diagnostic accuracy is 98.69% with a per-frame inference speed of 0.37 s in the FNB-UDV dataset. Representing the first deep-learning approach tailored specifically for early pregnancy FNB ultrasound video diagnosis, FNB-ADS significantly enhances the standardization of diagnostic procedures and reduces the dependence on subjective clinical assessments. The dataset and code are available at https://github.com/SIGMACX/FNB-AD/tree/FNB-ADS.