Artificial intelligence (AI) is useful for several cardiological applications, including image analysis, risk assessment, and patient monitoring. The treatment of cardiovascular disease has significantly evolved in interventional cardiology over the last 2 decades
Artificial intelligence (AI) is a broad term that uses technology to mimic human behaviour and perform various actions with minimal human intervention. Artificial intelligence (AI) is useful for several cardiological applications, including image analysis, risk assessment, and patient monitoring. The treatment of cardiovascular disease has significantly evolved in interventional cardiology over the last 2 decades. With the emergence of transcatheter therapies, the clinical arena of interventional cardiology has greatly expanded.
Heart diseases, mainly cardiovascular diseases, are among the most common causes of mortality worldwide. AI can be useful in patients with a heart attack or ST-segment elevation myocardial infarction in particular. It can help develop quantitative and automated tools to help assess myocardial perfusion. It can also help in developing standardised algorithms for not only managing high-risk patients with heart attacks but also predicting individuals at high risk for future heart attacks. AI can give a better-personalised approach to applying effective prevention. As compared to ST elevation MI which has simplified criteria to know occluded arteries, AI can help in generating algorithms for detecting culprit vessels in patients with non-ST elevation MI. AI or machine learning could be used in the ER to triage patients by predicting the likelihood of mortality or the severity of the referral case. In non-invasive cardiology, AI has a special role in generating algorithms in echocardiography, cardiac CT, and MRI by innovative applications for easy diagnosis of various cardiac diseases.
In interventional Cardiology, AI algorithms can be utilised to identify abnormalities in images and aid in the diagnosis and treatment. AI cannot replace but can act as a physician assistant, technician, or co-worker. AI can recommend stent size and type based on the analysis of angiography images in coronary, peripheral, and carotid interventions. In patients with congenital heart diseases, it can provide information regarding the sizing and positioning of occluder devices when closing atrial septal defect (ASD), ventricular septal defect (VSD), or patent ductus arteriosus (PDA). AI can have a special place in diagnosing and managing valvular heart diseases. During the balloon mitral valvuloplasty procedure, AI helps identify the location of the puncture site on angiography images. Even for advanced percutaneous treatments like MitraClip (a device to repair mitral regurgitation) device and closing the atrial appendix for atrial fibrillation, it can play an important role. In transcatheter aortic valve implantation, AI can make recommendations for valve placement and size. Providing the necessary information and recommendations can reduce procedural time, radiation exposure, and complication rates.
AI algorithms could benefit the interventionist, streamline the workflow, and minimise error. Although AI and machine learning can provide the desired accuracy, it is important to inform the user of how well the AI system is expected to perform and help them understand if the AI system makes different types of mistakes than humans for a given scenario.
