Role of Artificial Intelligence and Machine Learning in Interventional Cardiology

Advancements in artificial intelligence-driven techniques for interventional cardiology

“…They are exceptionally effective in deciphering complicated patterns contained in extensive data sets, making them indispensable tools in modern medical analysis and decision-making processes [ 39 ]. By processing vast amounts of medical data, including diagnostic images and patient records, DNNs can uncover subtle patterns and indicators that might be missed by traditional analysis methods [ 40 ]. The most significant advantages of DNNs include their ability to model complex relationships thanks to their structural depth, enabling efficient pattern and feature recognition in data.…”

Artificial intelligence in heart failure improving the efficiency or dependency on it? Letter regarding the article ‘Artificial intelligence and heart failure: A state‐of‐the‐art review’

“…Although AI has been around for decades, it is only recently that we have started to fully understand the potential of its applications, in the form of machine learning (ML) in cardiovascular medicine. 2 The quality of an AI model depends on the training data used. ML, which represents techniques for solving complicated problems with big data by identifying interaction patterns among variables, can be divided into three learning types: supervised, unsupervised, and reinforcement.…”

Predicting stroke and mortality in mitral stenosis with atrial flutter: A machine learning approach

“…Similarly, AFL carries a lower risk of cerebrovascular events when compared with AF; however; the annual incidence of TIA/stroke in patients with AFL is 1.38% (95% confidence interval [CI] 1.22%–1.57%) compared with 2.02% (95% CI 1.99%–2.05%) with AF (Al‐Kawaz et al, 2018 ). Recently, the use of complex machine learning (ML) algorithms are being used for risk stratification of disease states and their association with comorbidities, clinical status, and disease progression (Subhan et al, 2023 ). ML algorithms can be trained to recognize patterns in clinical data such as demographics, lab results, and imaging studies.…”

“…They can then use these patterns to stratify patients into subgroups based on disease severity, prognosis, and response to treatment. For example, ML algorithms have been used to stratify patients with lung cancer into different subgroups based on genetic mutations and other molecular characteristics, which can help guide targeted therapies (Subhan et al, 2023 ). One of the main advantages of using ML algorithms in disease stratification is that they can identify new patterns and relationships that may not be apparent using traditional statistical approaches.…”

“…One of the main advantages of using ML algorithms in disease stratification is that they can identify new patterns and relationships that may not be apparent using traditional statistical approaches. ML algorithms can also continuously learn and adapt as new data becomes available, which can lead to more accurate predictions over time (Subhan et al, 2023 ). However, there are also potential limitations to using ML algorithms in disease stratification.…”