An artificial intelligence–enabled ECG algorithm for comprehensive ECG interpretation: Can it pass the ‘Turing test’?

Anthony H. Kashou, Siva K. Mulpuru, Abhishek J. Deshmukh, Wei Yin Ko, Zachi I. Attia, Rickey E. Carter, Paul Andrew Friedman, Peter A. Noseworthy

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)164-170
Number of pages7
JournalCardiovascular Digital Health Journal
Volume2
Issue number3
DOIs
StatePublished - Jun 2021

Keywords

  • Artificial intelligence
  • Convolutional neural network
  • ECG
  • ECG interpretation
  • Electrocardiogram
  • Electrocardiography

ASJC Scopus subject areas

  • Biomedical Engineering
  • Critical Care and Intensive Care Medicine
  • Cardiology and Cardiovascular Medicine

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