Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation

Abstract

AF is the most common clinically relevant cardiac arrhythmia associated with multiple comorbidities, cardiovascular complications (e.g. stroke) and increased mortality. As artificial intelligence (AI) continues to transform the practice of medicine, this review article highlights specific applications of AI for the screening, diagnosis and treatment of AF. Routinely used digital devices and diagnostic technology have been significantly enhanced by these AI algorithms, increasing the potential for large-scale population-based screening and improved diagnostic assessments. These technologies have similarly impacted the treatment pathway of AF, identifying patients who may benefit from specific therapeutic interventions. While the application of AI to the diagnostic and therapeutic pathway of AF has been tremendously successful, the pitfalls and limitations of these algorithms must be thoroughly considered. Overall, the multifaceted applications of AI for AF are a hallmark of this emerging era of medicine.

Robust Artificial Intelligence Tool for Atrial Fibrillation Diagnosis: Novel Development Approach Incorporating Both Atrial Electrograms and Surface ECG and Evaluation by Head‐to‐Head Comparison With Hospital‐Based Physician ECG Readers

“…Finally, given the multiple risks associated with unrecognized or untreated AF, AI development using techniques described herein is important since diagnosis of AF and the ability to quantify AF burden are crucial factors in ensuring timely and cost‐efficient assessment and treatment of at‐risk populations. 10 , 13 , 14 …”

“…AI development in general requires as large and accurately annotated or labeled structured data as possible. 1 , 10 , 11 , 12 For the structuring purpose, we divided continuous surface ECG into 30‐second epochs based on European Society of Cardiology (ESC) guidance, which recommends single‐lead surface ECG strip with no less than 30 seconds’ duration for AF diagnosis. 1 The data acquisition plan was to collect data continuously on each patient for 7 days; our goal was to record a number of patients comparable to 328 patients by Hannun et al.…”

“…Studies have shown that screening for AF can be cost‐saving by identifying those individuals with prolonged AF burden warranting anticoagulation and permitting early initiation of AF treatment (whether by rhythm or rate control), thereby reducing hospital length of stay for postoperative patients. 1 , 5 , 6 , 7 , 8 , 9 , 10 However, due to the paroxysmal and often asymptomatic nature of AF, diagnostic screening using current techniques is often inadequate. 5 In particular, effective AF detection necessitates labor‐intensive ECG monitoring; the latter is a challenging task given the substantial magnitude of ECG data that must be reviewed.…”

“…Artificial intelligence (AI) systems may prove helpful in this regard, but their development demands a well‐curated and structured AF database for AI‐based tool training and testing. 10 …”

Machine learning in the prediction and detection of new-onset atrial fibrillation in ICU: a systematic review

“…ML capabilities extend to the development of procedures that autonomously learn from prior experiences, enhancing knowledge in specific domains. ML-based algorithms represent emerging and promising techniques for early AF detection [ 17 ]. These algorithms exhibit the capacity to identify patterns, make predictions, and propose actions [ 18 ].…”