Blinded, randomized trial of sonographer versus AI cardiac function assessment.

Journal: Nature
PMID:

Abstract

Artificial intelligence (AI) has been developed for echocardiography, although it has not yet been tested with blinding and randomization. Here we designed a blinded, randomized non-inferiority clinical trial (ClinicalTrials.gov ID: NCT05140642; no outside funding) of AI versus sonographer initial assessment of left ventricular ejection fraction (LVEF) to evaluate the impact of AI in the interpretation workflow. The primary end point was the change in the LVEF between initial AI or sonographer assessment and final cardiologist assessment, evaluated by the proportion of studies with substantial change (more than 5% change). From 3,769 echocardiographic studies screened, 274 studies were excluded owing to poor image quality. The proportion of studies substantially changed was 16.8% in the AI group and 27.2% in the sonographer group (difference of -10.4%, 95% confidence interval: -13.2% to -7.7%, P < 0.001 for non-inferiority, P < 0.001 for superiority). The mean absolute difference between final cardiologist assessment and independent previous cardiologist assessment was 6.29% in the AI group and 7.23% in the sonographer group (difference of -0.96%, 95% confidence interval: -1.34% to -0.54%, P < 0.001 for superiority). The AI-guided workflow saved time for both sonographers and cardiologists, and cardiologists were not able to distinguish between the initial assessments by AI versus the sonographer (blinding index of 0.088). For patients undergoing echocardiographic quantification of cardiac function, initial assessment of LVEF by AI was non-inferior to assessment by sonographers.

Authors

  • Bryan He
    Department of Computer Science, Stanford University, Stanford, California.
  • Alan C Kwan
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA.
  • Jae Hyung Cho
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Neal Yuan
    Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Charles Pollick
  • Takahiro Shiota
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Joseph Ebinger
    Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, United States of America.
  • Natalie A Bello
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Janet Wei
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Kiranbir Josan
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Grant Duffy
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd A3600, Los Angeles, CA 90048, United States.
  • Melvin Jujjavarapu
    Enterprise Information Services, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Robert Siegel
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Susan Cheng
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA.
  • James Y Zou
    Department of Computer Science, Stanford University, Stanford, CA, USA. jamesz@stanford.edu.
  • David Ouyang
    Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.