The Benefits of AI in Cardiovascular Imaging
Artificial intelligence (AI) has rapidly transformed imaging modalities such as echocardiography, computed tomography (CT), and magnetic resonance imaging (MRI). With the advent of large datasets, AI has significantly enhanced our ability to improve patient care throughout the imaging chain.
In this article, we will focus on AI’s role in cardiac imaging, its impact on key imaging modalities, and its essential role in future developments. How far has AI come, and what challenges lie ahead?
With the capacity to analyze large datasets for patterns and correlations, AI can learn to identify, describe, and predict. The three cognitive skills involved in AI are:
- Learning – the acquisition of data and creation of an algorithm (rules system) that turns the data into actionable information.
- Reasoning – programming that ensures the selection of the correct algorithm to achieve the desired outcome.
- Self-correction – the ongoing fine-tuning of algorithms in order to obtain the most accurate results.
Benefits of Artificial Intelligence
Enhanced Efficiency
AI technology allows tasks to be completed faster, increasing efficiency. Whether it’s getting through small tasks more quickly or completing complex tasks in a shorter time than the human mind is capable of, AI is pushing back the boundaries of what is possible and freeing up human time.
24/7 Availability
Humans need to sleep, so working for 24 hours a day is impossible. Not only does AI mimic and sometimes surpass the human mind, but it is also able to work around the clock, with no breaks required.
Reduced Error Rates
Because AI systems can work 24/7 without getting tired, they can also process more information without losing concentration, giving them the upper hand over humans. AI cuts out human error.
Faster Decision-Making
When used with the right technology and programming, AI can enable machines to make faster decisions than humans, with no emotional influence. This delivers accurate results faster.
AI’s Role in Cardiovascular Imaging
AI has excelled in cardiovascular imaging, and its potential is still being realized. AI has reduced human error in cardiovascular imaging applications and saved time in the clinical workflow. A major development has been AI’s ability to automatically segment cardiac structures, with the expectation that AI will support and enable disease detection, prognosis, and decision-making by expanding the value of diagnostic images.
AI in Echocardiography
AI’s role in echocardiography is particularly noteworthy. As the most accessible imaging modality in cardiology, echocardiography depends largely on the human interpretation of data by operators. AI has demonstrated its potential to replace human operators to some extent and reduce the intensive training required.
One study showed that a deep learning (DL) algorithm was able to perform view classification with the same accuracy as a board-certified echocardiographer. Other research used a DL algorithm to detect wall motion abnormalities with similar accuracy to that produced by cardiologists and sonographers.
AI in Cardiac CT
It is expected that AI will increasingly be integrated into the workflow of cardiac CT, including coronary computed tomography angiography (CCTA). AI can benefit cardiac CT by offering increased accuracy to coronary artery disease management with a quantitative-based approach. Wider implementation will depend on the validation of algorithms in good medical practice.
A DL method to obtain CT images with reduced radiation doses has been validated, and a DL approach has also been used to calculate calcium scores from regular coronary CT angiography (CTA), enabling a reduction of radiation exposure.
AI in Cardiac MRI (CMR)
Machine learning (ML) algorithms have been implemented in all aspects of the cardiac MRI imaging workflow, including undersampled image acquisition, automated analysis, and post-processing and development of predictive models.
AI has been used for cardiac structure and infarct tissue segmentation, including the segmentation of the right and left ventricular endo- and epicardium to calculate cardiac mass and function parameters from a number of datasets. Another study used a convolutional neural network to automate left ventricular segmentation in all short-axis slices and phases in publicly available datasets. Texture analysis has also been helped by ML algorithms, selecting the most important cine-image-derived texture features to distinguish between patients with myocardial infarction and control subjects.
Challenges of AI in Cardiovascular Imaging
While the benefits of AI in cardiovascular imaging are substantial, there are potential drawbacks and challenges. Demonstrating that AI can enable higher quality care that improves patient outcomes in a cost-effective manner is essential. With the significant volumes of data being used in AI applications, securing patient information is paramount. Additionally, improving the comparability of various imaging modes could enhance diagnosis and prognosis with AI.
Conclusion
While the technology’s potential is still being explored, AI has already proven important for cardiac imaging, demonstrating that it can equal, if not exceed, a human operator in some roles. AI is being used in almost all imaging modalities and throughout healthcare. Despite the ongoing challenges of cost-effectiveness and standardization, the expanding possibilities of AI’s benefits to clinical decision-making are exciting.
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