An Artificial Intelligence-Based Alarm Strategy Facilitates Management of Acute Myocardial Infarction

Abstract

(1) Background: While an artificial intelligence (AI)-based, cardiologist-level, deep-learning model for detecting acute myocardial infarction (AMI), based on a 12-lead electrocardiogram (ECG), has been established to have extraordinary capabilities, its real-world performance and clinical applications are currently unknown. (2) Methods and Results: To set up an artificial intelligence-based alarm strategy (AI-S) for detecting AMI, we assembled a strategy development cohort including 25,002 visits from August 2019 to April 2020 and a prospective validation cohort including 14,296 visits from May to August 2020 at an emergency department. The components of AI-S consisted of chest pain symptoms, a 12-lead ECG, and high-sensitivity troponin I. The primary endpoint was to assess the performance of AI-S in the prospective validation cohort by evaluating F-measure, precision, and recall. The secondary endpoint was to evaluate the impact on door-to-balloon (DtoB) time before and after AI-S implementation in STEMI patients treated with primary percutaneous coronary intervention (PPCI). Patients with STEMI were alerted precisely by AI-S (F-measure = 0.932, precision of 93.2%, recall of 93.2%). Strikingly, in comparison with pre-AI-S (N = 57) and post-AI-S (N = 32) implantation in STEMI protocol, the median ECG-to-cardiac catheterization laboratory activation (EtoCCLA) time was significantly reduced from 6.0 (IQR, 5.0–8.0 min) to 4.0 min (IQR, 3.0–5.0 min) (p < 0.01). The median DtoB time was shortened from 69 (IQR, 61.0–82.0 min) to 61 min (IQR, 56.8–73.2 min) (p = 0.037). (3) Conclusions: AI-S offers front-line physicians a timely and reliable diagnostic decision-support system, thereby significantly reducing EtoCCLA and DtoB time, and facilitating the PPCI process. Nevertheless, large-scale, multi-institute, prospective, or randomized control studies are necessary to further confirm its real-world performance.

Artificial intelligence-enabled electrocardiogram screens low left ventricular ejection fraction with a degree of confidence

“…According to the actual EF, patients were classified as having left ventricular dysfunction (≤ 40%) and controls (> 40%) based on the European Society of Cardiology Guidelines. We acquired the following patient data by accessing the EMR system: (1) demographic information, including sex, age, and body mass index (BMI); (2) disease history before the index date of ECG corresponding to International Classification of Disease, Ninth Revision, and Tenth Revision [23][24][25] ; and (3) TTE parameters, including interventricular septum (IVS) diameter, left ventricular posterior wall (LVPW) diameter, left atrium (LA) size, aortic root (AO) diameter, right ventricular (RV) diameter, pulmonary artery systolic pressure (PASP), and pericardial effusion (PE). Patients with an initial normal EF (≥50%) in the internal and external validation sets followed the new-onset left ventricular dysfunction (LVD, EF ≤ 40%).…”

Electrocardiogram-Based Heart Age Estimation by a Deep Learning Model Provides More Information on the Incidence of Cardiovascular Disorders

“…A deep learning model (DLM) is a technique used to learn useful features and provide an opportunity to speed up the process of converting unstructured data for analysis, which can also provide better accuracy in ECG interpretation ( 7 ). Previous studies have also developed a series of ECG-based DLMs on arrhythmia ( 8 ), acute myocardial infarction ( 9 , 10 ), aortic dissection ( 11 ), dyskalemia ( 12 – 14 ), left ventricular dysfunction ( 15 , 16 ), mitral regurgitation ( 17 ), aortic stenosis ( 18 ), glycemic profile ( 19 , 20 ), etc. Moreover, the ECGs can even be used to predict the atrial fibrillation after a month ( 21 ).…”

Artificial Intelligence-Enabled Electrocardiography Predicts Left Ventricular Dysfunction and Future Cardiovascular Outcomes: A Retrospective Analysis

“…Electrocardiography (ECG) is an inexpensive, noninvasive and widely used tool for multiple chronic cardiac disease screenings and evaluations. With the rapid progression of deep learning models (DLMs) on ECG [ 19 ], these models have expanded to multiple applications and achieved human-level performance, effectively detecting cardiac diseases with large annotated ECG datasets, including arrhythmia detection [ 20 ], dyskalemia [ 21 , 22 , 23 ], myocardial infarction [ 24 , 25 , 26 ], aortic dissection [ 27 ], thyrotoxic periodic paralysis [ 28 ], and digoxin toxicity [ 29 ]. Interestingly, current studies have started to use DLM to interpret chronic changes in ECGs, such as anemia [ 30 ], diabetes [ 31 ], conduction abnormality [ 32 ], future atrial fibrillation [ 33 ], and mortality prediction [ 34 ].…”