To evaluate the clinical value of artificial intelligence model for automatic measurement of infant hip joint development.

A total of 231 infants who underwent hip joint ultrasonic examination (with 462 standard hip joint images) at the Ultrasound Diagnosis Department of Guangdong Women and Children Hospital from January to November 2019 were selected. Intraclass correlation coefficient (ICC) and Deming regression were used for consistency analysis of measurement data of hip joint between a senior sonographer and artificial intelligence. ICC was used for consistency analysis of measurement data between a senior sonographer, a junior sonographer, and artificial intelligence, and for intra-observer agreement of the senior sonographer and junior sonographer.

The ICCs of α angle and β angle measured by the senior sonographer and artificial intelligence were 0.823 and 0.745, respectively; Deming regression slopes were 0.856 and 1.205, respectively, and regression residual plots were relatively symmetric. The ICCs of α angle and β angle measured by the senior sonographer and junior sonographer were 0.77 and 0.70, the ICCs of α angle and β angle measured by the junior sonographer and artificial intelligence were 0.79 and 0.71, and the ICCs of α angle and β angle measured by the senior sonographer and artificial intelligence was 0.87 and 0.79. The ICC of α angle double measured by the senior sonographer was 0.89, and the ICC of β angle was 0.84. The ICC of α angle double measured by the junior sonographer was 0.88, and the ICC of β angle was 0.82.

There is good data consistency between the deep learning-based artificial intelligence model and sonographers in measuring infant hip joints. The accuracy of artificial intelligence is closer to that of senior sonographer, and it can be used to assist in clinical ultrasound screening and diagnosis.