What is Artificial Intelligence?
AI tells computers to do jobs that people would normally do. For example, AI can read x-rays. Machine learning (ML) uses AI to improve the results of these jobs.
ML using AI is now better than a human at working out if a mole is likely to be melanoma or not.
Medical specialists, like vascular surgeons, rely on a lot of X-rays and scans to make decisions. As a result, ML and AI are good for solving all sorts of artery and vein problems.
Working out a patient’s outcome from treatment
AI can work out the chances of a good or not so good outcome from minimally invasive treatment or surgery. A mix of statistical models can be used to work out the risks of treatment. This information is often available on a smartphone.
All of the information from a patient can be compared to information from a large number of patients with the same condition to get a better result. Every time this happens, the AI can learn a little bit more about the condition. All of this is similar to the way humans learn from experience.
What vascular conditions currently use AI?
Abdominal Aneurysms (AAA)
AI is beginning to be useful in arterial vascular conditions, such as AAA and carotid artery disease.
In AAA, it can gather information about the growth, shape and make up of an aneurysm and, based on this, it can then give an accurate prognosis and the risk of rupture profile. Once a patient’s risk is known, better treatment decisions are available, such as ongoing observation or repair.
AI is also used in designing the best-fit stent treatment for aneurysms, based on multiple biometric factors. As well, AI can predict how the stent will function over time and help predict any future problems.
3D printers are coming to the fore in creating body parts that mimic a patient’s own. For use in hip replacements and implants and also custom made stents for aortic aneurysms.
Carotid Artery Disease
The risk of having a stroke from a narrowing in the carotid artery increases as the narrowing closes more. There are certain features of carotid disease, also called plaque, that increase the risk of a stroke. Recent bleeding inside the plaque, and dimples on the surface lining of the artery, are examples of this.
Using all of this information, AI is proving useful in predicting whether someone is at increased risk of having a stroke. Once this risk is known, there can be better decisions about carotid treatment. For example, whether to just keep keep a close eye on the artery or whether to operate.
As well, if a patient has already had a stroke caused by the carotid artery, AI can improve decision- making about whether urgent surgery is needed or not.
