Enhancing trainee performance in obstetric ultrasound through an artificial intelligence system: randomized controlled trial.

Objective: Performing obstetric ultrasound scans is challenging for inexperienced operators; therefore, the prenatal screening artificial intelligence system (PSAIS) software was developed to provide real-time feedback and guidance for trainees during their scanning procedures. The aim of this study was to investigate the potential benefits of utilizing such an artificial intelligence system to enhance the efficiency of obstetric ultrasound training in acquiring and interpreting standard basic views.

Methods: A prospective, single-center randomized controlled study was conducted at The First Affiliated Hospital of Sun Yat-sen University. From September 2022 to April 2023, residents with no prior obstetric ultrasound experience were recruited and assigned randomly to either a PSAIS-assisted training group or a conventional training group. Each trainee underwent a four-cycle practical scan training program, performing 20 scans in each cycle on pregnant volunteers at 18-32 gestational weeks, focusing on acquiring and interpreting standard basic views. At the end of each cycle, a test scan evaluated trainees' ability to obtain standard ultrasound views without PSAIS assistance, and image quality was rated by both the trainees themselves and an expert (in a blinded manner). The primary outcome was the number of training cycles required for each trainee to meet a certain standard of proficiency (i.e. end-of-cycle test scored by the expert at ≥ 80%). Secondary outcomes included the expert ratings of the image quality in each trainee's end-of-cycle test and the discordance between ratings by trainees and the expert.

Results: In total, 32 residents and 1809 pregnant women (2720 scans) were recruited for the study. The PSAIS-assisted trainee group required significantly fewer training cycles compared with the non-PSAIS-assisted group to meet quality requirements (P = 0.037). Based on the expert ratings of image quality, the PSAIS-assisted training group exhibited superior ability in acquiring standard imaging views compared with the conventional training group in the third (P = 0.012) and fourth (P < 0.001) cycles. In both groups, the discordance between trainees' ratings of the quality of their own images and the expert's ratings decreased with increasing training time. A statistically significant difference in overall trainee-expert rating discordance between the two groups emerged at the end of the first training cycle and remained at every cycle thereafter (P < 0.013).

Conclusion: By assisting inexperienced trainees in obtaining and interpreting standard basic obstetric scanning views, the use of artificial intelligence-assisted systems has the potential to improve training effectiveness. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.