The influence of computerized interpretation of an electrocardiogram reading

Martínez-Losas, Pedro; Higueras, Javier; Gómez-Polo, Juan Carlos; Brabyn, Philip; Ferrer, Juan Manuel Fuentes; Cañadas, Victoria; Pérez-Villacastín, Julián

doi:10.1016/j.ajem.2016.07.029

Cited by 18 publications

(12 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, these technologies remain inherently prone to error. [4][5][6][7][8] Furthermore, their influence on the over-reading provider's final interpretation is also profound, [9][10][11][12] which carries the risk of perpetuating incorrect interpretations and consequent patient harm. 8 Simultaneous advances in computing power and digitized data availability has catalyzed the application of artificial intelligence (AI) to the ECG.…”

Section: Introductionmentioning

confidence: 99%

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

Kashou

Mulpuru

Deshmukh

et al. 2021

Cardiovascular Digital Health Journal

View full text Add to dashboard Cite

OBJECTIVE To develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) algorithm capable of comprehensive, humanlike ECG interpretation and compare its diagnostic performance against conventional ECG interpretation methods. METHODSWe 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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Kashou

Mulpuru

Deshmukh

et al. 2021

Cardiovascular Digital Health Journal

View full text Add to dashboard Cite

show abstract

“…When they are accurate, they have been shown to increase the accuracy of physician overread, but when inaccurate, they have been shown to lead physicians astray. This has been shown for cardiology fellows [15], for emergency physicians [16], and also for fully trained cardiologists, for whom the presence of an automated interpretation resulted in lower accuracy because automated errors were not corrected [17].…”

Section: Discussionmentioning

confidence: 95%

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

Smith

Rapin²,

Li³

et al. 2019

IJC Heart & Vasculature

View full text Add to dashboard Cite

BackgroundAutomated electrocardiogram (ECG) interpretations may be erroneous, and lead to erroneous overreads, including for atrial fibrillation (AF). We compared the accuracy of the first version of a new deep neural network 12-Lead ECG algorithm (Cardiologs®) to the conventional Veritas algorithm in interpretation of AF.Methods24,123 consecutive 12-lead ECGs recorded over 6 months were interpreted by 1) the Veritas® algorithm, 2) physicians who overread Veritas® (Veritas® + physician), and 3) Cardiologs® algorithm. We randomly selected 500 out of 858 ECGs with a diagnosis of AF according to either algorithm, then compared the algorithms' interpretations, and Veritas® + physician, with expert interpretation. To assess sensitivity for AF, we analyzed a separate database of 1473 randomly selected ECGs interpreted by both algorithms and by blinded experts.ResultsAmong the 500 ECGs selected, 399 had a final classification of AF; 101 (20.2%) had ≥1 false positive automated interpretation. Accuracy of Cardiologs® (91.2%; CI: 82.4–94.4) was higher than Veritas® (80.2%; CI: 76.5–83.5) (p < 0.0001), and equal to Veritas® + physician (90.0%, CI:87.1–92.3) (p = 0.12). When Veritas® was incorrect, accuracy of Veritas® + physician was only 62% (CI 52–71); among those ECGs, Cardiologs® accuracy was 90% (CI: 82–94; p < 0.0001). The second database had 39 AF cases; sensitivity was 92% vs. 87% (p = 0.46) and specificity was 99.5% vs. 98.7% (p = 0.03) for Cardiologs® and Veritas® respectively.ConclusionCardiologs® 12-lead ECG algorithm improves the interpretation of atrial fibrillation.

show abstract

“…This requires interpreting the ECG in the context of the patient's symptoms, sometimes taking the time to compare the ECG with a previous one, repeating serial ECGs, or measuring a troponin level. The clinical decision of calling a code STEMI is complicated by patients presenting without classic chest pain, ECGs with other causes of ST elevation, errors in automated interpretation, concerns about false-positive cath lab activation, and evolving criteria for cath lab activation (34)(35)(36)(37)(38)(39)(40)(41)(42)(43). ETA time measures this overall diagnostic time and can suggest and assess strategies to reduce it, from shifting the activation of the cath lab to emergency physician to improving ECG diagnosis of acute coronary occlusion.…”

Section: Eta Time: a Quality Metric For Emergency Physiciansmentioning

confidence: 99%

Using ECG-To-Activation Time to Assess Emergency Physicians’ Diagnostic Time for Acute Coronary Occlusion

McLaren

Kapoor

et al. 2021

The Journal of Emergency Medicine

View full text Add to dashboard Cite

Background: There is no quality metric for emergency physicians' diagnostic time for acute coronary occlusion. Objective: We sought to quantify diagnostic time associated with automated interpretation, classic STelevation myocardial infarction (STEMI) criteria, STEMIequivalents, and subtle occlusions, using electrocardiogram (ECG)-to-activation of catheterization laboratory time. Methods: This multicenter retrospective study reviewed all code STEMI patients from the emergency department (ED) with confirmed culprit lesions from January 2016 to December 2018. We measured door-to-ECG (DTE) time and ECG-to-activation (ETA) time. We examined the first ED ECGs to determine whether automated interpretation labeled ''STEMI,'' and they met classic STEMI criteria, STEMI-equivalents, or rules for subtle occlusion. ECG analysis was performed by two emergency physicians blinded to clinical scenario, automated interpretation, and angiographic outcome. Results: There were 177 code STEMIs with culprit lesions, with a median DTE time of 9.0 min and a median ETA time of 16.0 min. Automated interpretation labeled 55.4% of first ECGs ''STEMI'' (ETA 6.5 min) and 44.6% not ''STEMI'' (ETA 66 min, p < 0.0001). Of first ECGs, 63.8% met classic STEMI criteria (ETA 8.0 min), 8.5% had STEMI-equivalents (ETA 32.0 min, p = 0.0026), 16.4% had subtle occlusions (ETA 89.0 min, p = 0.045), and 11.3% had no diagnostic sign of occlusion (ETA 68.0 min, p = 0.20). Conclusions: STEMI criteria missed more than one-third of occlusions on first ECG, but most had STEMI-equivalents or rules for subtle occlusion. ETA time can serve as a quality metric for emergency physicians to promote new ECG insights and assess quality improvement initiatives.

show abstract

The influence of computerized interpretation of an electrocardiogram reading

Cited by 18 publications

References 5 publications

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

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

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

Using ECG-To-Activation Time to Assess Emergency Physicians’ Diagnostic Time for Acute Coronary Occlusion

Contact Info

Product

Resources

About