Use of Artificial Intelligence for Acquisition of Limited Echocardiograms: A Randomized Controlled Trial for Educational Outcomes
Baum, E.; Tandel, M. D.; Ren, C.; Weng, Y.; Pascucci, M.; Kugler, J.; Cardoza, K.; Kumar, A.
Show abstract
BackgroundPoint-of-care ultrasound (POCUS) machines may utilize artificial intelligence (AI) to enhance image interpretation and acquisition. This study investigates whether AI-enabled devices improve competency among POCUS novices. MethodsWe conducted a randomized controlled trial at a single academic institution from 2021-2022. Internal medicine trainees (N=43) with limited POCUS experience were randomized to receive a POCUS device with (Echonous, N=22) or without (Butterfly, N=21) AI-functionality for two weeks while on an inpatient rotation. The AI-device provided automatic labeling of cardiac structures, guidance for optimal probe placement to acquire cardiac views, and ejection fraction estimations. Participants were allowed to use the devices at their discretion for patient-related care. The primary outcome was the time to acquire an apical 4-chamber (A4C) image. Secondary outcomes included A4C image quality using the modified Rapid Assessment for Competency in Echocardiography (RACE) scale, correct identification of pathology, and participant attitudes. Measurements were performed at the time of randomization and at two-week follow-up. All scanning assessments were performed on the same standardized patient. ResultsBoth AI and non-AI groups had similar scan times and image quality scores at baseline. At follow-up, the AI group had faster scan times (72 seconds [IQR 38-85] vs. 85 seconds [IQR 54-166]; p=0.01), higher image quality scores (4.5 [IQR 2-5.5] vs. 2 [IQR 1-3]; p<0.01) and correctly identified reduced systolic function more often (85% vs 50%; p=0.02) compared to the non-AI group. Trust in the AI features did not differ between the groups pre- or post-intervention. The AI group did not report increased confidence in their abilities to obtain or interpret cardiac images. ConclusionsPOCUS devices with AI features may improve image acquisition and interpretation by novices. Future studies are needed to determine the extent that AI impacts POCUS learning.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Mechanical effects of MitraClip on leaflet stress and myocardial strain in functional mitral regurgitation: A finite element modeling study. 92%
- {-}CardiOvascular examination in awake Orangutans (Pongo pygmaeus pygmaeus): Low-stress Echocardiography including Speckle Tracking imaging (the COOLEST method) 92%
- ChatGPT Provides Inconsistent Risk-Stratification of Patients With Atraumatic Chest Pain 92%
Similar papers in this journal
- Team-Based Learning Versus Lecture-Based Instruction for Chest Radiograph Interpretation in Physician Associate Education: A Quasi-Experimental Study 90%
- Improving capacity for advanced training in obstetric surgery: Evaluation of a blended learning approach 89%
- Evaluation of Statistical Illiteracy in Latin American Clinicians and of the Efficacy of a 10-Hour Course 89%
Similar papers in this journal
- Performance of ChatGPT and GPT-4 on Neurosurgery Written Board Examinations 89%
- Performance of ChatGPT, GPT-4, and Google Bard on a Neurosurgery Oral Boards Preparation Question Bank 89%
- Low and Borderline Ankle Brachial Index is Associated with Intracranial Aneurysms – a Retrospective Cohort Study 86%
Similar papers in this journal
- Patient Screening for Self-Expanding Percutaneous Pulmonary Valves using Virtual Reality 95%
- Non-Invasive Scale Measurement of Cardiac Output Compared with the Gold-Standard Direct Fick Method: A Feasibility Study 93%
- Patient Risk-Benefit Preferences for Transcatheter versus Surgical Mitral Valve Repair 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.