Artificial Intelligence for the early prediction of Atrial Fibrillation
Definitions and epidemiology
Atrial fibrillation (AF) is a supraventricular tachyarrhythmia characterized by an uncoordinated electrical activation of the heart atria (the upper chambers of the heart). This irregular atrial electrical activity leads to an ineffective atrial contraction. AF can be classified based on its duration as paroxysmal (intermittent episodes of arrhythmia), persistent or permanent. AF is the most common arrhythmia in adults globally, thus it is a major cause of morbidity and mortality. It is estimated that AF’s prevalence is about 2-4% in the adult population. Increasing age is the most prominent risk factor for the development of AF along with several other comorbid risk factors such as diabetes mellitus, hypertension, coronary artery disease, chronic kidney disease and more. Modifiable risk factors such as smoking, physical inactivity and obesity also play an important role in the risk of AF development.
Clinical Characteristics and Outcomes
AF can have various clinical presentations from being asymptomatic or silent ,to symptomatic with variable severity of symptoms from palpitations and chest discomfort to more severe symptoms such as syncope and cardiogenic shock. Regardless of its clinical presentation the main cause of AF morbidity is its deleterious outcomes. AF is a major cause of stroke since it causes about 20-30% of all ischemic strokes. Moreover, a significant portion of AF patients (about 20-30%) will develop heart failure. Furthermore, AF predisposes to cognitive decline, depression and impaired quality of life. Finally, AF patients will have an increased annual hospitalization rate and, more importantly, an increased mortality rate since they are at a 1,5 to 3,5 fold increased risk of death.
Diagnosis and screening for Atrial Fibrillation
The diagnosis of AF requires rhythm documentation. An electrocardiogram (ECG) showing atrial fibrillation for at least 30 seconds is diagnostic for clinical AF. Since AF can be asymptomatic, however equally hazardous regarding its outcomes (eg stroke), screening and early diagnosis could be of great importance in reducing its associated morbidity and mortality. Various wearable devices ( wearable monitors, smartwatches, smartphones etc) can provide important data in the screening of AF.
AINIGMA technologies is developing AI-based applications for the prediction of the development of Atrial Fibrillation (AF). We use demographics, medical history, physical activity, simple clinical and laboratory data and also ECG recordings to train our algorithms. We aim to provide a sensitive and user friendly application that will be able to predict which patients are at high risk of developing AF in the near future, and unmask which of them could already have subclinical AF. Early risk assessment of AF will lead to closer medical attention and more aggressive screening, thus early diagnosis and treatment of AF.