Automatic Identification of Fetal Abdominal Planes from Ultrasound Images Based on Deep Learning.

Abstract

Fetal biometric assessments through ultrasound diagnostics are integral in obstetrics and gynecology, requiring considerable time investment. This study aimed to explore the potential of artificial intelligence (AI) architectures in automatically identifying fetal abdominal standard scanning planes and structures, particularly focusing on the abdominal circumference. Ultrasound images from a prospective cohort study were preprocessed using CV2 and Keras-OCR to eliminate textual elements and artifacts. Optical character recognition detected and removed textual components, followed by inpainting using adjacent pixels. Six deep learning neural networks, Xception and MobileNetV3Large, were employed to categorize fetal abdominal view planes. The dataset included nine classes, and the models were evaluated through a tenfold cross-validation cycle. The MobileNet3Large and EfficientV2S achieved accuracy rates of 79.89% and 79.19%, respectively. Data screening confirmed non-normal distribution, but the central limit theorem was applied for statistical analysis. ANOVA test revealed statistically significant differences between the models, while Tukey's post hoc tests showed no difference between MobileNet3Large and EfficientV2S, while outperforming the other networks. AI, specifically MobileNet3Large and EfficientV2S, demonstrated promise in identifying fetal abdominal view planes, showcasing potential benefits for prenatal ultrasound diagnostics. Further studies should compare these AI models with established methods for automatic abdominal circumference measurement to assess overall performance.