Literature DB >> 28347451

The role of computerized diagnostic proposals in the interpretation of the 12-lead electrocardiogram by cardiology and non-cardiology fellows.

Tomas Novotny1, Raymond Bond2, Irena Andrsova3, Lumir Koc3, Martina Sisakova3, Dewar Finlay2, Daniel Guldenring2, Jindrich Spinar3, Marek Malik4.   

Abstract

INTRODUCTION: Most contemporary 12-lead electrocardiogram (ECG) devices offer computerized diagnostic proposals. The reliability of these automated diagnoses is limited. It has been suggested that incorrect computer advice can influence physician decision-making. This study analyzed the role of diagnostic proposals in the decision process by a group of fellows of cardiology and other internal medicine subspecialties.
MATERIALS AND METHODS: A set of 100 clinical 12-lead ECG tracings was selected covering both normal cases and common abnormalities. A team of 15 junior Cardiology Fellows and 15 Non-Cardiology Fellows interpreted the ECGs in 3 phases: without any diagnostic proposal, with a single diagnostic proposal (half of them intentionally incorrect), and with four diagnostic proposals (only one of them being correct) for each ECG. Self-rated confidence of each interpretation was collected.
RESULTS: Availability of diagnostic proposals significantly increased the diagnostic accuracy (p<0.001). Nevertheless, in case of a single proposal (either correct or incorrect) the increase of accuracy was present in interpretations with correct diagnostic proposals, while the accuracy was substantially reduced with incorrect proposals. Confidence levels poorly correlated with interpretation scores (rho≈2, p<0.001). Logistic regression showed that an interpreter is most likely to be correct when the ECG offers a correct diagnostic proposal (OR=10.87) or multiple proposals (OR=4.43).
CONCLUSION: Diagnostic proposals affect the diagnostic accuracy of ECG interpretations. The accuracy is significantly influenced especially when a single diagnostic proposal (either correct or incorrect) is provided. The study suggests that the presentation of multiple computerized diagnoses is likely to improve the diagnostic accuracy of interpreters.
Copyright © 2017 Elsevier B.V. All rights reserved.

Keywords:  Computerized diagnostic proposals; Decision making; Electrocardiogram interpretations

Mesh:

Year:  2017        PMID: 28347451     DOI: 10.1016/j.ijmedinf.2017.02.007

Source DB:  PubMed          Journal:  Int J Med Inform        ISSN: 1386-5056            Impact factor:   4.046


  4 in total

1.  A deep neural network for 12-lead electrocardiogram interpretation outperforms a conventional algorithm, and its physician overread, in the diagnosis of atrial fibrillation.

Authors:  Stephen W Smith; Jeremy Rapin; Jia Li; Yann Fleureau; William Fennell; Brooks M Walsh; Arnaud Rosier; Laurent Fiorina; Christophe Gardella
Journal:  Int J Cardiol Heart Vasc       Date:  2019-09-08

2.  A comprehensive artificial intelligence-enabled electrocardiogram interpretation program.

Authors:  Anthony H Kashou; Wei-Yin Ko; Zachi I Attia; Michal S Cohen; Paul A Friedman; Peter A Noseworthy
Journal:  Cardiovasc Digit Health J       Date:  2020-09-08

3.  Analysis of the accuracy of automatic electrocardiogram interpretation in ST-segment elevation myocardial infarction.

Authors:  Seongsoo Kim; Wonhee Kim; Gu Hyun Kang; Yong Soo Jang; Hyun Young Choi; Jae Guk Kim; Yoonje Lee; Dong Geum Shin
Journal:  Clin Exp Emerg Med       Date:  2022-03-31

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

Authors:  Anthony H Kashou; Siva K Mulpuru; Abhishek J Deshmukh; Wei-Yin Ko; Zachi I Attia; Rickey E Carter; Paul A Friedman; Peter A Noseworthy
Journal:  Cardiovasc Digit Health J       Date:  2021-05-05
  4 in total

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