Artificial intelligence is showing reassuring results in cardiology, especially in the area of cardiovascular imaging. Ever wondered how it works?
Machine learning algorithms (a subdivision of AI) are making it possible for cardiologists to find out new opportunities and delve into new discoveries difficult to be noticed using conventional techniques. This offers newer gateways helpful in medical decision-making.
Key features of AI in Cardiovascular Imaging:
- AI can help improve performance at low cost thereby facilitating decision making, interpretation, and precise image acquisition of anatomical structures as well as diagnosis.
- The big data obtained using imaging will be helpful in personalizing medical treatments and keeping an electronic record of patient data, health records, and outcome data.
- It is believed that this will help physicians work more efficiently on core issues while computers will handle the technical part.
Different kinds of imagining possibilities using AI
Echocardiography – This is the most commonly used imaging technique in cardiology, but it is highly user-dependent. It is also important to undergo serious training in order to interpret the results accurately. AI can be a low-cost alternative to provide a standardized analysis of echocardiographic images. It has already shown great success in this area.
Computed Tomography – AI has been greatly appreciated in the field of cardiac CT as it has helped in noise reduction, and image optimization thereby preventing invasive coronary angiography (ICA) in the identification of severe stenosis.
Magnetic Resonance Imaging – This includes anatomical images of various aspects of the heart including flow imaging, contractile function, perfusion imaging, and myocardial characterization. As is the case with Echocardiography, MRI is also highly user-dependent. Reports have shown that implementing computer-aided detection in the clinical setup can increase accuracy and simplify the analysis.
Nuclear Imaging of the heart – This is performed to assess any perfusion defects within the myocardial lining. AI-based models can improve the clinical value of the results obtained. AI-based models have been highly successful at detecting abnormal myocardium in CAD, and their efficiency is at par with manual analysis images received.
Conclusion
Technology is not new to humans. We’re getting more and more comfortable with the idea of relying on machines for safer and more accurate conduct of our day to day lives. In the case of cardiovascular imaging, AI has proved itself promising in various ways. Here are the reasons why the medical industry is ready to adopt computer-aided detection and diagnosis in Cardiology:
- Detection and diagnosis of disease
- Interpretation of data
- Collection and comparison of data for future studies
- Clinical decision making
- Accurate image acquisition of images
- Reducing health care expenditure
- Reducing the workload for physicians
AI seems to have a lot of pros for the medical industry, but it needs to be made perfect with more testing and re-testing. It will definitely be a great tool for cardiologists as it is capable of recognizing patterns that are otherwise difficult to assess for the human brain.
