Blinded, randomized trial of sonographer versus AI cardiac function assessment

被引:118
作者
He, Bryan [1 ]
Kwan, Alan C. [2 ]
Cho, Jae Hyung [2 ]
Yuan, Neal [3 ]
Pollick, Charles [2 ]
Shiota, Takahiro [2 ]
Ebinger, Joseph [2 ]
Bello, Natalie A. [2 ]
Wei, Janet [2 ]
Josan, Kiranbir [2 ]
Duffy, Grant [2 ]
Jujjavarapu, Melvin [4 ]
Siegel, Robert [2 ]
Cheng, Susan [2 ]
Zou, James Y. [1 ,5 ]
Ouyang, David [2 ,6 ]
机构
[1] Stanford Univ, Dept Comp Sci, Palo Alto, CA 94305 USA
[2] Smidt Heart Inst, Cedars Sinai Med Ctr, Dept Cardiol, Los Angeles, CA 90048 USA
[3] UCSF, Dept Med, Div Cardiol, San Francisco VA, San Francisco, CA USA
[4] Cedars Sinai Med Ctr, Enterprise Informat Serv, Los Angeles, CA USA
[5] Stanford Univ, Dept Biomed Data Sci, Palo Alto, CA 94305 USA
[6] Cedars Sinai Med Ctr, Div Artificial Intelligence Med, Los Angeles, CA 90048 USA
关键词
EJECTION FRACTION; AMERICAN SOCIETY; ECHOCARDIOGRAPHY; QUANTIFICATION; ASSOCIATION;
D O I
10.1038/s41586-023-05947-3
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页码:520 / +
页数:9
相关论文
共 31 条
[1]  
Al-Khatib SM, 2018, CIRCULATION, V138, pE210, DOI [10.1161/CIR.0000000000000549, 10.1161/CIR.0000000000000548]
[2]   An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction [J].
Attia, Zachi, I ;
Noseworthy, Peter A. ;
Lopez-Jimenez, Francisco ;
Asirvatham, Samuel J. ;
Deshmukh, Abhishek J. ;
Gersh, Bernard J. ;
Carter, Rickey E. ;
Yao, Xiaoxi ;
Rabinstein, Alejandro A. ;
Erickson, Brad J. ;
Kapa, Suraj ;
Friedman, Paul A. .
LANCET, 2019, 394 (10201) :861-867
[3]   Assessment of blinding in clinical trials [J].
Bang, HJ ;
Ni, LY ;
Davis, CE .
CONTROLLED CLINICAL TRIALS, 2004, 25 (02) :143-156
[4]   The inclusion of augmented intelligence in medicine: A framework for successful implementation [J].
Bazoukis, George ;
Hall, Jennifer ;
Loscalzo, Joseph ;
Antman, Elliott Marshall ;
Fuster, Valentin ;
Armoundas, Antonis A. .
CELL REPORTS MEDICINE, 2022, 3 (01)
[5]  
Cheitlin MD, 1997, CIRCULATION, V95, P1686
[6]   Machine Learning and Prediction in Medicine - Beyond the Peak of Inflated Expectations [J].
Chen, Jonathan H. ;
Asch, Steven M. .
NEW ENGLAND JOURNAL OF MEDICINE, 2017, 376 (26) :2507-2509
[7]   Defining the real-world reproducibility of visual grading of left ventricular function and visual estimation of left ventricular ejection fraction: impact of image quality, experience and accreditation [J].
Cole, Graham D. ;
Dhutia, Niti M. ;
Shun-Shin, Matthew J. ;
Willson, Keith ;
Harrison, James ;
Raphael, Claire E. ;
Zolgharni, Massoud ;
Mayet, Jamil ;
Francis, Darrel P. .
INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING, 2015, 31 (07) :1303-1314
[8]   2019 ACC/AHA/ASE Key Data Elements and Definitions for Transthoracic Echocardiography: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Data Standards (Writing Committee to Develop Clinical Data Standards for Transthoracic Echocardiography) and the American Society of Echocardiography [J].
Douglas, Pamela S. ;
Carabello, Blase A. ;
Lang, Roberto M. ;
Lopez, Leo ;
Pellikka, Patricia A. ;
Picard, Michael H. ;
Thomas, James D. ;
Varghese, Paul ;
Wang, Tracy Y. ;
Weissman, Neil J. ;
Wilgus, Rebecca .
CIRCULATION-CARDIOVASCULAR IMAGING, 2019, 12 (07)
[9]   Echocardiographic Imaging in Clinical Trials: American Society of Echocardiography Standards for Echocardiography Core Laboratories Endorsed by the American College of Cardiology Foundation [J].
Douglas, Pamela S. ;
DeCara, Jeanne M. ;
Devereux, Richard B. ;
Duckworth, Shelly ;
Gardin, Julius M. ;
Jaber, Wael A. ;
Morehead, Annitta J. ;
Oh, Jae K. ;
Picard, Michael H. ;
Solomon, Scott D. ;
Wei, Kevin ;
Weissman, Neil J. .
JOURNAL OF THE AMERICAN SOCIETY OF ECHOCARDIOGRAPHY, 2009, 22 (07) :755-765
[10]   High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy With Cardiovascular Deep Learning [J].
Duffy, Grant ;
Cheng, Paul P. ;
Yuan, Neal ;
He, Bryan ;
Kwan, Alan C. ;
Shun-Shin, Matthew J. ;
Alexander, Kevin M. ;
Ebinger, Joseph ;
Lungren, Matthew P. ;
Rader, Florian ;
Liang, David H. ;
Schnittger, Ingela ;
Ashley, Euan A. ;
Zou, James Y. ;
Patel, Jignesh ;
Witteles, Ronald ;
Cheng, Susan ;
Ouyang, David .
JAMA CARDIOLOGY, 2022, 7 (04) :386-395