Automatic triage of twelve-lead electrocardiograms using deep convolutional neural networks: a first implementation study

Rutger R van de Leur; Meike T G M van Sleuwen; Peter-Paul M Zwetsloot; Pim van der Harst; Pieter A Doevendans; Rutger J Hassink; René van Es

doi:10.1093/ehjdh/ztad070

Automatic triage of twelve-lead electrocardiograms using deep convolutional neural networks: a first implementation study

Eur Heart J Digit Health. 2023 Nov 8;5(1):89-96. doi: 10.1093/ehjdh/ztad070. eCollection 2024 Jan.

Authors

Rutger R van de Leur¹, Meike T G M van Sleuwen¹, Peter-Paul M Zwetsloot¹, Pim van der Harst¹, Pieter A Doevendans^{1

2

3}, Rutger J Hassink¹, René van Es¹

Affiliations

¹ Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands.
² Netherlands Heart Institute, Utrecht, The Netherlands.
³ Central Military Hospital, Utrecht, The Netherlands.

Abstract

Aims: Expert knowledge to correctly interpret electrocardiograms (ECGs) is not always readily available. An artificial intelligence (AI)-based triage algorithm (DELTAnet), able to support physicians in ECG prioritization, could help reduce current logistic burden of overreading ECGs and improve time to treatment for acute and life-threatening disorders. However, the effect of clinical implementation of such AI algorithms is rarely investigated.

Methods and results: Adult patients at non-cardiology departments who underwent ECG testing as a part of routine clinical care were included in this prospective cohort study. DELTAnet was used to classify 12-lead ECGs into one of the following triage classes: normal, abnormal not acute, subacute, and acute. Performance was compared with triage classes based on the final clinical diagnosis. Moreover, the associations between predicted classes and clinical outcomes were investigated. A total of 1061 patients and ECGs were included. Performance was good with a mean concordance statistic of 0.96 (95% confidence interval 0.95-0.97) when comparing DELTAnet with the clinical triage classes. Moreover, zero ECGs that required a change in policy or referral to the cardiologist were missed and there was a limited number of cases predicted as acute that did not require follow-up (2.6%).

Conclusion: This study is the first to prospectively investigate the impact of clinical implementation of an ECG-based AI triage algorithm. It shows that DELTAnet is efficacious and safe to be used in clinical practice for triage of 12-lead ECGs in non-cardiology hospital departments.

Keywords: Deep learning; Electrocardiography; Implementation; Triage.