Development and temporal validation of a deep learning model for automatic fetal biometry from ultrasound videos

The objective was to develop an artificial intelligence (AI)-based system, using deep neural network (DNN) technology, to automatically detect standard fetal planes during video capture, measure fetal biometry parameters and estimate fetal weight.

A standard plane recognition DNN was trained to classify ultrasound images into four categories: head circumference (HC), abdominal circumference (AC), femur length (FL) standard planes, or 'other'. The recognized standard plane images were subsequently processed by three fetal biometry DNNs, automatically measuring HC, AC and FL. Fetal weight was then estimated with the Hadlock 3 formula. The training dataset consisted of 16,626 images. A prospective temporal validation was then conducted using an independent set of 281 ultrasound videos of healthy fetuses. Fetal weight and biometry measurements were compared against an expert sonographer. Two less experienced sonographers were used as controls.

The AI system obtained a significantly lower absolute relative measurement error in fetal weight estimation than the controls (AI vs. medium-level: p = 0.032, AI vs. beginner: p < 1e-8), so in AC measurements (AI vs. medium-level: p = 1.72e-04, AI vs. beginner: p < 1e-06). Average absolute relative measurement errors of AI versus expert were: 0.96 % (S.D. 0.79 %) for HC, 1.56 % (S.D. 1.39 %) for AC, 1.77 % (S.D. 1.46 %) for FL and 3.10 % (S.D. 2.74 %) for fetal weight estimation.

The AI system produced similar biometry measurements and fetal weight estimation to those of the expert sonographer. It is a promising tool to enhance non-expert sonographers' performance and reproducibility in fetal biometry measurements, and to reduce inter-operator variability.