To evaluate the effectiveness of an artificial intelligence (AI)-based quality control (QC) platform in improving the image quality of mid-trimester systematic ultrasound screening.

The "Prenatal Ultrasound AI Cloud Platform" was used to perform intelligent QC of ultrasound images obtained from 11559 mid-trimester systematic ultrasound screening examinations (766 233 images) conducted between January 2022 and December 2024. A total of 37 sonographers (including 3 senior, 7 associate senior, 20 intermediate, and 7 junior practitioners) participated in the study. The χ² test was applied to compare annual standard-plane acquisition rates and case qualification rates over the 3-year period, as well as differences in standard-plane acquisition rates among operators with different professional ranks. The effectiveness of the AI-based QC platform in improving image quality was subsequently assessed.

From 2022 to 2024, the overall standard-plane compliance rates increased from 70.81% to 73.65% and 74.10%, with significant differences across the three years (χ²=931.883, P=0.000). The case qualification rates also increased from 79.51% to 84.38% and 90.17% (χ²=181.479, P=0.000). Meanwhile, the rates of missing QC-required images decreased from 18.51% to 14.47% and 8.84%, respectively. Overall, image standardization and case qualification rates improved steadily over time, whereas missing-image rates showed a continuous decline. Among all operator groups, junior sonographers demonstrated the greatest improvement, with a 4.39% increase in the overall image standardization rate and a 2.17% reduction in the non-standard image rate over the three-year period.

The AI-based QC platform effectively enhances ultrasound image quality across operators of all skill levels, improves their recognition of standard sections, and strengthens adherence to standardized mid-trimester systematic ultrasound screening.