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

Abstract 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. Research in context Evidence before this study We undertook a systematic review exploring evidence for the use of artificial intelligence (AI) to assist in the performance of fetal anomaly ultrasound scans by automating anatomical standard plane detection, and / or automating fetal biometric measurements. We searched PubMed, the Cochrane Library, and clinicaltrials.gov databases using the following search terms: ((artificial intelligence) OR (AI) OR (machine learning) OR (neural network*) OR (deep learning)) AND ((fetal) OR (foetal) OR (fetus) OR (foetus) OR (obstetric) OR (antenatal) OR (prenatal) OR (pregnan*)) AND (ultrasound) AND ((biometr*) OR (measurement) OR (growth) OR (size) OR (plane) OR (view) OR (femur) OR (head) OR (biparietal) OR (abdom*)). We limited the results to the 10-year period September 2013 – September 2023. 770 papers were identified, of which 55, 39, and 33 were deemed relevant after title, abstract, and text screening respectively. Of the 33 papers, 14 focused on biometric measurements, 14 on plane detection, and 5 included both. Only one paper tested the AI models prospectively with real-time feedback to the sonographer, but this study did not include randomisation or fetal pathology. No randomised clinical trials comparing AI-assisted ultrasound scans with standard scans have been performed previously. Added value of this study To our knowledge, this is the first randomised controlled trial investigating the use of AI in fetal ultrasound screening. In this trial we assess the use of AI-assistance to automatically undertake some aspects of the scan (automatic anatomical standard plane detection and saving, and measurement of biometric parameters), and measure the effect on overall diagnostic performance, as well as scan duration, sonographer cognitive load, image quality, and repeatability and reproducibility of biometric measurements. AI assistance resulted in a significantly lower scan duration and sonographer cognitive load, whilst maintaining the quality of the scan in terms of diagnostic performance and biometric measurements. Implications of all the available evidence The results from this trial are encouraging and suggest that AI assistance may offer real clinical benefit to sonographers undertaking fetal ultrasound screening. The reduced scan duration means that sonographers may have more time to focus on other aspects of the scan, such as communication with parents. The automatically measured biometrics were both more repeatable and reproducible compared to manual measurements, which may improve the accuracy with which fetal growth and health can be assessed. Further studies combining this work with AI models that can directly detect fetal structural malformations will be important, to improve the overall antenatal detection of fetal anomalies.