Artificial Intelligence to Assist in the Screening Fetal Anomaly Ultrasound Scan (PROMETHEUS): A Randomised Controlled Trial

Background 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. Methods This was a randomised, single centre, open label trial in a large teaching hospital. Pregnant participants with fetal congenital heart disease (CHD) and with healthy fetuses were recruited and scanned with both methods. Screening sonographers were recruited from regional hospitals and were randomised to scan with the AI tool or in the standard fashion, 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. Findings 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 was 88.9% and 98.0% respectively, with the standard scan achieving 81.5% and 92.2% (not significant). AI-assisted scans were significantly shorter than standard scans (median 11.4 min vs 19.7 min, p <0.001). Sonographer cognitive load was significantly lower in the AI-assisted group (median NASA TLX score 35.2 vs 46.5, p <0.001). For all biometrics, the AI repeatability and reproducibility was superior to manual measurements. Interpretation AI assistance in the routine fetal anomaly ultrasound scan results in a significant time saving, along with a reduction in sonographer cognitive load, without a reduction in diagnostic performance. Funding The study was funded by an NIHR doctoral fellowship (NIHR301448) and was supported by grants from the Wellcome Trust (IEH Award, 102431), by core funding from the Wellcome Trust/EPSRC Centre for Medical Engineering (WT203148/Z/16/Z), and the London AI Centre for Value Based Healthcare via funding from the Office for Life Sciences.