Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 May 5;2(3):164-170.
doi: 10.1016/j.cvdhj.2021.04.002. eCollection 2021 Jun.

An artificial intelligence-enabled ECG algorithm for comprehensive ECG interpretation: Can it pass the 'Turing test'?

Anthony H Kashou  1 , Siva K Mulpuru  2 , Abhishek J Deshmukh  2 , Wei-Yin Ko  2 , Zachi I Attia  2 , Rickey E Carter  3 , Paul A Friedman  2 , Peter A Noseworthy  2
Affiliations

An artificial intelligence-enabled ECG algorithm for comprehensive ECG interpretation: Can it pass the 'Turing test'?

Anthony H Kashou et al. Cardiovasc Digit Health J. .

Abstract

Objective: To develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) algorithm capable of comprehensive, human-like ECG interpretation and compare its diagnostic performance against conventional ECG interpretation methods.

Methods: We developed a novel AI-enabled ECG (AI-ECG) algorithm capable of complete 12-lead ECG interpretation. It was trained on nearly 2.5 million standard 12-lead ECGs from over 720,000 adult patients obtained at the Mayo Clinic ECG laboratory between 2007 and 2017. We then compared the need for human over-reading edits of the reports generated by the Marquette 12SL automated computer program, AI-ECG algorithm, and final clinical interpretations on 500 randomly selected ECGs from 500 patients. In a blinded fashion, 3 cardiac electrophysiologists adjudicated each interpretation as (1) ideal (ie, no changes needed), (2) acceptable (ie, minor edits needed), or (3) unacceptable (ie, major edits needed).

Results: Cardiologists determined that on average 202 (13.5%), 123 (8.2%), and 90 (6.0%) of the interpretations required major edits from the computer program, AI-ECG algorithm, and final clinical interpretations, respectively. They considered 958 (63.9%), 1058 (70.5%), and 1118 (74.5%) interpretations as ideal from the computer program, AI-ECG algorithm, and final clinical interpretations, respectively. They considered 340 (22.7%), 319 (21.3%), and 292 (19.5%) interpretations as acceptable from the computer program, AI-ECG algorithm, and final clinical interpretations, respectively.

Conclusion: An AI-ECG algorithm outperforms an existing standard automated computer program and better approximates expert over-read for comprehensive 12-lead ECG interpretation.

Keywords: Artificial intelligence; Convolutional neural network; ECG; ECG interpretation; Electrocardiogram; Electrocardiography.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Study design. AI-ECG = artificial intelligence–enabled electrocardiogram; ECG = electrocardiogram.
See this image and copyright information in PMC

References

    1. Taback L., Marden E., Mason H.L., Pipberger H.V. Digital recording of electrocardiographic data for analysis by a digital computer. IRE Trans Med Electron. 1959;6:167–171.
    1. Rikli A.E., Tolles W.E., Steinberg C.A., et al. Computer analysis of electrocardiographic measurements. Circulation. 1961;24:643–649. - PubMed
    1. Caceres C.A., Steinberg C.A., Abraham S., et al. Computer extraction of electrocardiographic parameters. Circulation. 1962;25:356–362. - PubMed
    1. Schlapfer J., Wellens H.J. Computer-interpreted electrocardiograms: benefits and limitations. J Am Coll Cardiol. 2017;70:1183–1192. - PubMed
    1. Shah A.P., Rubin S.A. Errors in the computerized electrocardiogram interpretation of cardiac rhythm. J Electrocardiol. 2007;40:385–390. - PubMed

LinkOut - more resources