AI to Assist in the Fetal Anomaly Ultrasound Scan: A Randomized Controlled Trial

Artificial intelligence (AI) has shown potential in improving the performance of screening fetal anomaly ultrasound scans. We aimed to assess the effect of AI on fetal ultrasound scanning in terms of diagnostic performance, biometry, scan duration, and sonographer cognitive load.

This was a randomized, single-center, open-label trial in a large teaching hospital. Pregnant participants with fetal congenital heart disease (CHD) and participants with healthy fetuses were recruited and scanned with both methods. Sonographers were recruited from regional hospitals and were randomly assigned to scan with either the AI tool or the standard method, blinded to the fetal CHD status. For the AI-assisted scans, the AI models identified and saved 13 standard image planes and measured four biometrics (but did not automate CHD diagnosis). The primary outcome was the diagnostic performance of the scan; secondary outcomes were scan duration and sonographer cognitive load, as well as biometry performance.

In total, 78 pregnant participants (26 with fetal CHD) and 58 sonographers were recruited. The sensitivity and specificity of the AI-assisted scan in detecting fetal malformation were 88.9% and 98.0%, respectively, with the standard scan achieving 81.5% and 92.2% (differences in proportions 7.4% for sensitivity (97.5% [CI] confidence interval, -15.9 to 30.7%) and 5.9% for specificity (97.5% CI, -3.8 to 15.5%)). AI-assisted scans were shorter in duration than standard scans (median 11.4 minutes vs. 19.7 minutes, 95% CI for mean difference 7.4 to 11.1). Sonographer cognitive load was lower in the AI-assisted group (median National Aeronautics and Space Administration - Task Load Index [NASA TLX] score 35.2 vs. 46.5, 95% CI for mean difference 4.6 to 15.4). For all biometrics, the AI repeatability and reproducibility were superior to manual measurements. No adverse events were noted during the trial.

AI assistance in the routine fetal anomaly ultrasound scan results in significant time savings, and a reduction in sonographer cognitive load, without a reduction in diagnostic performance. (The study was funded by an NIHR doctoral fellowship [NIHR301448] and others; ISRCTN number, 65824874.).