Machine learning used to predict risk of heart disease
Zebra Medical Vision, which raised $12m in funding earlier this year, has created two algorithms that, when combined, are said to be particularly effective in fighting heart disease. The first algorithm quantifies the amount of calcification or plaque in coronary arteries, while the second examines fatty liver, which is associated with a two- to four-times elevated risks of coronary artery plaque that can lead to heart attacks. "We believe that these tools… will help radiologists deal with the continuous pressure they face to increase output and maintain high quality of care," Zebra's CEO Elad Benjamin said in a statement.
It is hard to define what artificial intelligence really is, as even authors on this matter have difficulty discussing it and often fail to reach a consensus. Still, AI is used as an important tool to solve a wide array of problems, and one of its most important fields is machine learning (ML).

Its algorithms look for patterns within a data set. These algorithms have been around for a long time, but there was never such a large amount of digital data available to feed these algorithms as today, thanks to three factors: mass computerization, big data and the rise of the internet.

These ML algorithms are used and applied in many different areas and fields, such as customer relationship management (CRM) and interaction, or health. Now, a deep learning imaging analytics company, Zebra Medical Vision, is announcing two new software algorithms that analyze and predict patients' risk of suffering from cardiovascular diseases and events.

Those algorithms automatically quantify the amount of calcified plaque in coronary arteries and detect presence of fatty liver in patients' CT scans. They also inform medical staff about the cardiovascular and metabolic state of their patients, which altogether provide even stronger predictors for risk of heart attack and stroke.

Cardiovascular diseases are one of the leading causes of death around the world, causing more than 600,000 deaths every year in the United States alone and over 17.8 million annual deaths in the entire globe, according to data from the World Heart Federation.

The build up of calcium in the walls of the arteries that supply our heart muscle can be seen and quantified on routine CT scans of the chest. The degree of that calcification is a strong predictor for cardiovascular events such as heart attack or strokes.

Fatty liver is also a condition with a high incidence, affecting up to 11% of the population (with numbers growing every year). This condition is believed to be a strong indicator of metabolic changes, seen in people who have or come to develop insulin resistant diabetes. Fatty liver, also known as non-alcoholic steatohepatitis or NASH, is a preventable cause of chronic liver disease, cirrhosis and liver cancer. It also represents a 2x to 4x higher risk of heart attack and cardiac death.

Applying those two indicators can greatly assist physicians in early identification of the aforementioned conditions, and self-insured large employers or insurance companies can better assess risk using existing imaging data. Elad Benjamin, the CEO of Zebra Medical Vision, commented on the merits of his company's innovations:

“This is yet another step in our mission to help provide faster, more accurate radiology services at lower cost, by teaching software to read and identify key clinical conditions in imaging. We believe that these tools, as well as new algorithms which we continuously release, will help Radiologists deal with the continuous pressure they face to increase output and maintain high quality of care.”

Zebra Medical Vision is being praised and expanded, as the company has recently secured a partnership with Dell Services and has received financial backing from Intermountain Healthcare, one of the leading healthcare organizations in the US. Further agreements, with ACOs, HMOs and other providers, are also on the works, which will undoubtedly lead to a more comprehensive and impressive medical community.