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Artificial intelligence technology has many aspects that can change the world we live in, including medical imaging which can detect the warning signs of the second cause of global death, the stroke.

Artificial Intelligence technology in stroke imaging

Those who are facing high blood pressure (or any other cause of a stroke) are aware of the enormous concern that comes along with the disease that is also known as “The Silent Killer”. Whether it’s you or one of your relatives, you would need to find medical treatment to lower the risks of getting a stroke. The use of artificial intelligence in stroke imaging prevent patients from having a stroke, and alert before a blockage of large blood vessel occurs.

Viz.ai is one of the leading companies in the world to develop artificial intelligence technology that can detect and alert the early signs of a stroke. Last year, the company received approval from the Food and Drug Administration (FDA) in the United States to sell its software for the public. Viz.ai’s software compares the CT scan to hundreds of thousands of other scans and identifies patterns. Then, the software alerts doctors if a patient is showing signs of a stroke by sending an alert to a brain specialist’s mobile device to review the scan, enabling them to make the most correct decisions as quickly as possible. 

Another firm that is offering the use of artificial intelligence to detect stroke is Brainomix, a University of Oxford start-up company. Both companies are offering an online demo.

AI can greatly improve the accuracy of image detection for a stroke, reducing the casualties of the disease. Researchers confirm the benefits of AI in detecting a stroke, and though the industry has a long way to go, artificial intelligence and machine learning can be a game-changer.

How artificial intelligence can diagnose cancer?

The benefits of artificial intelligence can help doctors to also detect other life-taking diseases such as cancer. Cancer is a race against time and one of the main keys to preventing cancer is to catch it early.

Doctors have limited resources to detect cancer such as radiological imaging which is not entirely accurate and can miss the discovery of cancer or provide false results (20%-30% of cases).

And so, one of AI’s qualities is to collect enough data in order to train neural networks. The Artificial intelligence technology is able to process complicated information in a short amount of time, and identify patterns in determining patients’ probability to get cancer and their response to a certain treatment.

So far, the most successful results have been tested on lung and breast cancer. Among the top cancer detection companies, you can find Grail, Oncimmune, Lunit, Quantgene, Aidence, and Early Diagnostics.

Can machine learning predict cancer?

Machine learning is being used by most big technology firms such as Amazon, Netflix, Google, etc. in order to analyze and diagnose human behavior and predict the future by collecting large data. But how can machine learning predict cancer? The same way – by collecting big data and identify patterns.

Machine learning has many advantages over humans – ML can do tasks much faster than professional doctors and collect big data. For example, a biopsy usually takes a Pathologist 10 days to conduct while a computer can do thousands of biopsies in a matter of seconds.

The process of machine learning to detect cancer works similarly to the way researchers collect data, with one difference – it’s faster, more accurate and collects big data. First, machine learning must distinguish between benign and malignant tumors. Once the machine learning collects enough data, a computing system called ‘Artificial Neural Networks (ANN)’ imports the data, analyze it and can identify cancer.

Bottom line

There is no question about the growing involvement of machine learning and artificial intelligence in the future of cancer prediction. The results are hard to ignore – Google has released research the shows how AI can detect lung cancer faster and reliably, Viz.ai has published results that demonstrate the ability to detect large-vessel occlusion stroke in non-contrast CT head scans, and another by NVIDIA shows that deep learning drops error rate for breast cancer diagnoses by 85%.

There’s a long way until patients and doctors can obtain reliability in the technology. However, stroke and cancer are among the commonest causes of death and any new technology can be a useful resource to identify the disease.

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