» Articles » PMID: 35265905

An Artificial Intelligence-enabled ECG Algorithm for Comprehensive ECG Interpretation: Can It Pass the 'Turing Test'?

Overview
Authors
Affiliations
Soon will be listed here.
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, changes needed), (2) acceptable (ie, edits needed), or (3) unacceptable (ie, 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 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 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.

Citing Articles

Artificial Intelligence-Based Clinical Decision Support Systems in Cardiovascular Diseases.

Bozyel S, Simsek E, Burunkaya D, Guler A, Korkmaz Y, Seker M Anatol J Cardiol. 2024; .

PMID: 38168009 PMC: 10837676. DOI: 10.14744/AnatolJCardiol.2023.3685.


Impact of Computer-Interpreted ECGs on the Accuracy of Healthcare Professionals.

Kashou A, Noseworthy P, Beckman T, Anavekar N, Cullen M, Angstman K Curr Probl Cardiol. 2023; 48(11):101989.

PMID: 37482286 PMC: 10800643. DOI: 10.1016/j.cpcardiol.2023.101989.


Electrocardiogram to Determine Mitral and Aortic Valve Opening and Closure.

Aufan M, Jost Z, Miller N, Sharifov O, Gupta H, Perry G Cardiovasc Eng Technol. 2023; 14(3):447-456.

PMID: 36971975 DOI: 10.1007/s13239-023-00664-4.


Personalized LSTM Models for ECG Lead Transformations Led to Fewer Diagnostic Errors Than Generalized Models: Deriving 12-Lead ECG from Lead II, V2, and V6.

Shyam Kumar P, Ramasamy M, Kallur K, Rai P, Varadan V Sensors (Basel). 2023; 23(3).

PMID: 36772426 PMC: 9920327. DOI: 10.3390/s23031389.


Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review.

Ayano Y, Schwenker F, Dufera B, Debelee T Diagnostics (Basel). 2023; 13(1).

PMID: 36611403 PMC: 9818170. DOI: 10.3390/diagnostics13010111.


References
1.
Poon K, Okin P, Kligfield P . Diagnostic performance of a computer-based ECG rhythm algorithm. J Electrocardiol. 2005; 38(3):235-8. DOI: 10.1016/j.jelectrocard.2005.01.008. View

2.
Ribeiro A, Horta Ribeiro M, Paixao G, Oliveira D, Gomes P, Canazart J . Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun. 2020; 11(1):1760. PMC: 7145824. DOI: 10.1038/s41467-020-15432-4. View

3.
Zhu H, Cheng C, Yin H, Li X, Zuo P, Ding J . Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study. Lancet Digit Health. 2020; 2(7):e348-e357. DOI: 10.1016/S2589-7500(20)30107-2. View

4.
Kashou A, Ko W, Attia Z, Cohen M, Friedman P, Noseworthy P . A comprehensive artificial intelligence-enabled electrocardiogram interpretation program. Cardiovasc Digit Health J. 2022; 1(2):62-70. PMC: 8890098. DOI: 10.1016/j.cvdhj.2020.08.005. View

5.
Martinez-Losas P, Higueras J, Gomez-Polo J, Brabyn P, Ferrer J, Canadas V . The influence of computerized interpretation of an electrocardiogram reading. Am J Emerg Med. 2016; 34(10):2031-2032. DOI: 10.1016/j.ajem.2016.07.029. View