Limited echocardiogram acquisition by novice clinicians aided with deep learning: A randomized controlled trial

The global shortage of sonographers has created significant barriers to timely ultrasound diagnostics across medical specialties. Deep learning (DL) algorithms have potential to enhance image acquisition by clinicians without formal sonography training, potentially expanding access to crucial diagnostic imaging in resource-limited settings. This study evaluates whether DL-enabled devices improve acquisition of multi-view limited echocardiograms by healthcare providers without previous cardiac ultrasound training. In this single-center randomized controlled trial (2023-2024), internal medicine residents (N = 38) without prior sonography training received a portable ultrasound device with (N = 19) or without (N = 19) DL capability for a two-week clinical integration period during regular patient care on hospital wards. The DL software provided real-time guidance for probe positioning and image quality assessment across five standard echocardiographic views. The primary outcome was total acquisition time for a comprehensive five-view limited echocardiogram (parasternal long axis, parasternal short axis, apical 4-chamber, subcostal, and inferior vena cava views). Assessments occurred at randomization and after two weeks using a standardized patient. Secondary outcomes included image quality using a validated assessment tool and participant attitudes toward the technology. Baseline scan times and image quality scores were comparable between groups. At two-week follow-up, participants using DL-equipped devices demonstrated significantly faster total scan times (152 s [IQR 115-195] versus 266 s [IQR 206-324]; P < 0.001; Cohen's D = 1.7) and superior image quality with higher modified RACE scores (15 [IQR 10-18] versus 11 [IQR 7-13.5]; P = 0.034; Cohen's D = 0.84). Performance improvements were most pronounced in technically challenging views. Both groups reported similar levels of trust in DL-functionality. Ultrasound devices incorporating deep learning algorithms significantly improve both acquisition speed and image quality of comprehensive echocardiographic examinations by novice users. These findings suggest DL-enhanced ultrasound may help address critical gaps in diagnostic imaging capacity by enabling non-specialists to acquire clinically useful cardiac images.