EP01.24: Enhancing trainee performance in obstetrical ultrasound through an artificial intelligence system: a randomised controlled trial

To investigate the benefits of utilising an AI system to enhance the efficiency of obstetric scan training. A randomised controlled study was conducted at the First Affiliated Hospital of Sun Yat-sen University. Residents were recruited and randomly assigned to either a AI-assisted training group or a conventional training group from September 2022 to April 2023. Each participant underwent a four-cycle practice scan training program, performing scans on 20 pregnant volunteers at gestational weeks 18-32 in each cycle, focusing on acquiring and interpreting specific standard views. At the end of each cycle, a test evaluated trainees' ability to obtain standard views without AI assistance, and image quality was rated by both trainees themselves and an expert (in a blind manner) based on local expert consensus. The primary outcome measured the number of cycles required for each trainee to meet standards (expert ratings of image quality ≥80%). Secondary outcomes included expert rating of image quality, disparity between trainees and expert ratings. A total of 32 residents with no prior obstetric ultrasound experience and 2720 pregnant volunteers were recruited. The AI-assisted group required significantly fewer training cycles than the non-AI-assisted group to meet quality requirements (p = 0.037). When comparing mean score differences, the AI-assisted training group exhibited superior ability in acquiring standard views compared to the conventional training group in the third (p = 0.012) and fourth (p < 0.001) stages. The disparity between trainees' self-acquired image ratings and expert ratings decreased with increasing training time. There was a significant difference in total rating disparity between trainees and expert between the two groups from the first to the fourth stage (p < 0.05). The utilisation of an AI-assisted system has the potential to improve training effectiveness, particularly for trainees without prior experience in acquiring and interpreting standard views during obstetric scans.