To probe into the clinical application value of artificial intelligence in the judgment of the quality standard of mid-sagittal plane ultrasound images for first trimester fetal crown-rump length (CRL).

A total of 1251 midsagittal plane ultrasound images of fetuese at 11-13⁺⁶ weeks of gestation were selected from the database of Shenzhen Maternity & Child Healthcare Hospital from January to December 2022. Using the unified judging results of the image quality standard by the Expert Committee of Prenatal Ultrasound as the golden standard, the performance of an artificial intelligent based quality control model, senior, middle, and junior sonographers in the judgment of the quality standard of mid-sagittal plane ultrasound images for first trimester CRL was assessed by calculating their coincidence rates with the golden standard. The coincidence rates were compared using the Mcnemar-Bowker tests, and weighted Kappa values were applied to analyse the difference and consistency among these results. Time of quality control for each set of images was recorded as one group per 100 images. The Wilcoxon's two samples signed-rank test was applied to compare the difference in the time spent among the four groups.

The coincidence rate of the intelligent quality control model with the golden standard was 90.5%, suggesting a strong consistency to the golden standard (Kappa=0.83, P<0.001), which was slightly lower than that of senior sonographers (90.5% vs 91.1%, χ²=16.40, P<0.001), but superior to that of middle and junior sonographers' (90.5% vs 78.7% vs 68.9%, χ²=100.25, 16.88, P<0.001 for all). The time spent by the intelligent model quality control was significantly less than that by ultrasound physicians [3.57 (3.55, 3.60) s vs 351 (309, 384) s vs 363 (351, 370) s vs 433 (407, 475) s; Z=-3.180, -3.181, and -3.180, respectively, P<0.001 for all].

The intelligent quality control model is accurate and efficient in the judgment of the quality standard of mid-sagittal plane ultrasound images for first trimester CRL.