Artificial intelligence for a personalized diagnosis and treatment of atrial fibrillation

Abstract

Although Atrial Fibrillation (AF) is the most common cardiac arrhythmia, its early identification, diagnosis, and treatment is still challenging. Due to its heterogeneous mechanisms and risk-factors, targeting an individualized treatment of AF demands a large amount of patient data to identify specific patterns. Artificial Intelligence (AI) algorithms are particularly well suited for treating high-dimensional data, predicting outcomes and, eventually, optimizing strategies for patient management. The analysis of large patient samples combining different sources of information such as blood biomarkers, electrical signals and medical images opens a new paradigm for improving diagnostic algorithms. In this review, we summarize suitable AI techniques for this purpose. In particular, we describe potential applications for understanding the structural and functional bases of the disease, as well as for improving early noninvasive diagnosis, developing more efficient therapies, and predicting long-term clinical outcomes of AF patients.

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Artificial Intelligence-Driven Algorithm for Drug Effect Prediction on Atrial Fibrillation: An in silico Population of Models Approach

“…As the number of simulations reached a significant number of samples with an increasing information volume, AI algorithms were applied to reveal patterns in the data. AI application in clinical environments is exponentially increasing and leading toward new diagnostic and treatment techniques ( Feeny et al, 2020 ; Sánchez de la Nava et al, 2021 ). This trend has also been implemented in the electrophysiology field ( Muffoletto et al, 2021 ; Siontis and Friedman, 2021 ), in which the use of algorithms has been used for detecting or evaluating proarrhythmicity ( Shao et al, 2018 ; Halfar et al, 2021 ), classifying different rhythms ( Wasserlauf et al, 2019 ) or automatizing tasks as segmentation ( Yang et al, 2017 ).…”

“…Moreover, its use in safety pharmacology could be applied to analyze all the data produced by in silico simulations. Particularly, the Random Forest algorithm grouped similar profiles with the same outcome, therefore implementing an AI-driven algorithm able to predict, based on the ionic combinations of each profile, the probability of AF inducibility with excellent predictive values ( Sánchez de la Nava et al, 2021 ).…”

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