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

Autor: He B; Department of Computer Science, Stanford University, Palo Alto, CA, USA., Kwan AC; Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Cho JH; Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Yuan N; Department of Medicine, Division of Cardiology, San Francisco VA, UCSF, San Francisco, CA, USA., Pollick C; Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Shiota T; Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Ebinger J; Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Bello NA; Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Wei J; Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Josan K; Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Duffy G; Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Jujjavarapu M; Enterprise Information Services, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Siegel R; Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Cheng S; Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA. susan.cheng@cshs.org., Zou JY; Department of Computer Science, Stanford University, Palo Alto, CA, USA. jamesz@stanford.edu.; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA. jamesz@stanford.edu., Ouyang D; Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA. david.ouyang@cshs.org.; Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA. david.ouyang@cshs.org.
Jazyk: angličtina
Zdroj: Nature [Nature] 2023 Apr; Vol. 616 (7957), pp. 520-524. Date of Electronic Publication: 2023 Apr 05.
DOI: 10.1038/s41586-023-05947-3
Abstrakt: Artificial intelligence (AI) has been developed for echocardiography 1-3 , 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.
(© 2023. The Author(s).)
Databáze: MEDLINE
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