Artificial Intelligence In Cardiovascular Imaging

• Cardiovascular disease continues to be the world’s most common cause of morbidity and mortality.
• The importance of artificial intelligence methods including cognitive computing, deep learning, and machine learning, show their uses in cardiology.
• The incorporation into daily decision-making of artificial intelligence tools in the field of cardiology will improve care.
• Artificial intelligence is showing great promise in the field of cardiovascular imaging.

Cardiovascular disease continues to be the world’s most common cause of morbidity and mortality, and is therefore a major focus for medical imaging and medical research. Notwithstanding continuous advances in modalities of cardiac imaging, including cardiac computed tomography, cardiovascular magnetic resonance and echocardiography, the heart remains a challenging organ to photograph, particularly because of its perpetual motion(de Marvao et al., 2020). Artificial intelligence (AI) defines a computational program capable of performing tasks which are typical human intelligence characteristics including problem solving, sound, recognizing objects, understanding language, planning and pattern identification and recognition. Practically AI described as a device or machine ability to make decisions autonomously based on it received data.

There are four main areas of IA implementation including 1. Computer-aided detection 2. Quantitative analysis tools 3. Clinical decision support and 4. Computer-aided diagnosis.In medical field, the AI used for choose the best treatment option, new disease identification, being used to forecast a possible diagnosis, etc. (Darcy et al., 2016) and (Dey et al., 2019). In the management of cardiovascular disease, the cardiovascular imaging is paying an important role. When new imaging techniques are being continually implemented this has only been reinforced.

More and more studies in cardiac imaging are being carried out each year. This is informed by various factors such as increased imaging recognition, which has played an incremental role in the monitoring, management and diagnosis of patient outcomes over the years. Furthermore, imaging was broader accessible, and not only has the imaging equipment become more accurate, but also cheaper and faster. The improved interpretability and quality of imaging studies not only resulted in increased patient satisfaction, but could also lead to enhanced legal and clinical reassurance for the doctor.

From an economic point of view, the global rise in costs of healthcare is partly related to the imaging units enhancing number in the hospital and hence the enhanced number of imaging studies carried out (Papanicolas et al., 2018). The extension of imaging techniques and subsequent analyzes nevertheless exceeds the average imaging specialist’s efficiency limits. The solution for the systematic assessment of the rising number of medical images is medical artificial intelligence (AI). The smart computers using AI can provide assistance and guidance during image evaluation and acquisition has begun to show scientific literatures. This may have influences significantly on the workload of the physician (Siegersma et al., 2019).

The incorporation of various AI technologies will support cardiac imaging. Physicians will then be able to concentrate on activities best accomplished by human intelligence without being disturbed by tasks that can be automated. This can lead to improved health equality, improved healthcare delivery quality, improved patient-physician relationships, higher job satisfaction for physicians and access to healthcare while limiting the unsustainable increase in healthcare expenditure in an aging population.