A comprehensive artificial intelligence–enabled electrocardiogram interpretation program

Abstract

BACKGROUND Automated computerized electrocardiogram (ECG) interpretation algorithms are designed to enhance physician ECG interpretation, minimize medical error, and expedite clinical workflow. However, the performance of current computer algorithms is notoriously inconsistent. We aimed to develop and validate an artificial intelligence-enabled ECG (AI-ECG) algorithm capable of comprehensive 12-lead ECG interpretation with accuracy comparable to practicing cardiologists. METHODSWe developed an AI-ECG algorithm using a convolutional neural network as a multilabel classifier capable of assessing 66 discrete, structured diagnostic ECG codes using the cardiologist's final annotation as the gold-standard interpretation. We included 2,499,522 ECGs from 720,978 patients 18 years of age with a standard 12-lead ECG obtained at the Mayo Clinic ECG laboratory between 1993 and 2017. The total sample was randomly divided into training (n 5 1,749,654), validation (n 5 249,951), and testing (n 5 499,917) datasets with a similar distribution of codes. We compared the AI-ECG algorithm's performance to the cardiologist's interpretation in the testing dataset using receiver operating characteristic (ROC) and precision recall (PR) curves. RESULTSThe model performed well for various rhythm, conduction, ischemia, waveform morphology, and secondary diagnoses codes with an area under the ROC curve of 0.98 for 62 of the 66 codes. PR metrics were used to assess model performance accounting for category imbalance and demonstrated a sensitivity 95% for all codes.CONCLUSIONS An AI-ECG algorithm demonstrates high diagnostic performance in comparison to reference cardiologist interpretation of a standard 12-lead ECG. The use of AI-ECG reading tools may permit scalability as ECG acquisition becomes more ubiquitous.

Machine Learning for Electrocardiographic Features to Identify Left Atrial Enlargement in Young Adults: CHIEF Heart Study

“…The study was the first report to show a better performance of machine learning to predict echocardiographic LAE compared to the traditional ECG criterion of P wave duration in young male adults who had a healthy status and without multiple comorbidities. Prior studies ( 29 – 31 ) have revealed that machine learning for ECG features could detect most of the LAE cases from hospitalized patients, probably due to those patients with LAE who were likely to have other cardiac comorbidities, such as heart failure, that were easily reflected by ECG features; thus, the results might not be appropriate for healthy individuals.…”

“…The study was the first report to show a better performance of machine learning to predict echocardiographic LAE compared to the traditional ECG criterion of P wave duration in young male adults who had a healthy status and without multiple comorbidities. Prior studies (29)(30)(31) have revealed that machine learning for ECG features could detect most of the LAE cases from hospitalized patients, probably due to those patients with LAE who were likely to have other cardiac comorbidities, such as heart failure, that were easily reflected by ECG features; thus, the results might not be appropriate for healthy individuals. Some studies have shown that, in young adults, particularly physically fit people, an enlarged cardiac chamber is likely, and the typical ECG features for LAE might not be the same as those in middle-aged individuals and elderly individuals who had several cardiovascular comorbidities, i.e., hypertension.…”

“…The P wave duration was also a predictor of atrial fibrillation, cardiovascular death, and early vascular aging (27,28). Over the past 5 years, there were only some hospital-based studies utilizing machine learning for ECG features to detect the presence of LAE, in which the area under the curve (AUC) of the receiver operating characteristic curve (ROC) varied much from 0.62 to 0.98 (29)(30)(31). However, there were no previous reports performed in the general population.…”

Remote Cardiac Rhythm Monitoring in the Era of Smart Wearables: Present Assets and Future Perspectives

“…Further improvement of the diagnostic accuracy has the potential to transform wearables, such as smartwatches, from screening and pre-diagnostic tools to diagnostic modalities. The integration of artificial intelligence algorithms in basic medical tools, such as the 12-lead ECG, has demonstrated promising results, regarding the early and accurate detection of structural heart disorders, thus extending beyond the field of arrhythmia diagnosis ( 121 , 122 ). The combination of this technological advancement with wearable devices could improve the reliability of their measurements and provide prognostic features for the detection of subclinical cardiac conditions ( 123 ).…”

Automated ECG Interpretation—A Brief History from High Expectations to Deepest Networks

“…Publications on the use of AI for 12 lead ECG interpretation are already appearing [60,61]. A recent study by Kashou et al states that their AI-based approach 'outperforms an existing standard automated computer program' and also 'better approximates expert over-read for comprehensive 12 lead ECG interpretation' [62].…”