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MedTech Outlook | Tuesday, October 11, 2022
Artificial intelligence in interventional cardiology can help with intraprocedural guidance and intravascular imaging and provide the operator with additional data.
FREMONT, CA: Artificial intelligence (AI) is a broad phrase that refers to the process by which machines can replicate human behavior and execute a variety of tasks with little human interaction. Machine learning (ML), a subfield of AI, can analyze data and identify hidden patterns.
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Over the last two decades, interventional cardiology has made significant advancements in the treatment of cardiovascular disease. However, coronary artery disease (CAD), valvular heart disease, cardiac arrhythmias, pericardial disease, myocardial disease, congenital heart disease, and heart failure can all be treated in the catheterization laboratory.
The advent of transcatheter treatments significantly extended the clinical scope of interventional cardiology. Non-invasive imaging is an essential gatekeeper in evaluating cardiovascular disorders before intervention. In imaging, AI technologies exhibit their ability to do picture interpretation, quality control, diagnostics, and workflow optimization. AI and machine learning can aid in the discovery of novel variations or phenotypes within massive datasets in cardiovascular imaging, thereby improving the understanding and paving the way for new therapeutic strategies in CAD. Additionally, they can aid in interventional cardiology by facilitating clinical decision-making, streamlining workflow in the catheterization laboratory, facilitating catheter-based intervention via robotic application, and predicting optimal placement to minimize or eliminate paravalvular leakage.
Over the last few years, AI has significantly altered the landscape of clinical medicine by enabling new insights and potential for therapy improvement. While AI continues to advance in other spheres of human life, from self-driving cars to automated voice recognition systems, machine learning is broadening clinical paths and establishing new frontiers in cardiovascular medicine. By contrast, the applicability of machine learning (ML) in interventional cardiology (IC) has been less prominent. While it is clear that AI advancements in IC lag behind its competitors, interest in the field continue to grow. AI will be advantageous if stent technology progresses and transcatheter aortic valve replacement (TAVR) or transcatheter mitral valve replacement (TMVR) procedures become more sophisticated.
AI possesses incredible capabilities with transformational potential and can perform a wide variety of tasks. These abilities include pattern recognition, problem-solving, object and sound identification, and language understanding. AI can make data-driven decisions about illness progression and treatment selection in the simplest terms. Although AI has provided significant breakthroughs in cardiovascular imaging and electrophysiology, its relevance in IC is still in its infancy. At the moment, AI practice in IC can be broadly classified into two distinct disciplines: virtual and physical. While the virtual branch encompasses machine learning techniques, natural language processing (NLP), cognitive computing, and automated clinical decision support systems, the physical branch is mainly focused on robotic interventional treatments.
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