Artificial Intelligience Detects Breast Cancer Survivors’ Lymphedema

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A research team, Rory Myers College of Nursing, published a study showing the relative accuracy of five machine learning algorithms in detecting lymphedema in breast cancer surgery survivors. I know you may be wondering what all these jargon means; "Machine Learning", "Algorithms", "Artificial Intelligence or AI", "Lymphedema", and whatnot! Don't Worry, we'll explain further! ;)

Basically, an Algorithm is a sequence of steps, plans or formulas that solve a mathematical or computational problem. When a computer is programmed to build upon its pre-programmed basic steps or formulas (or algorithm) in order to by itself create more complex formulas that solve complex problems, its called machine learning as the computer learns from past solutions on how-to or how-not-to solve problems. A machine is said to be Artificially Intelligent (AI) when it can design algorithms and techniques that allow it to learn autonomously.

On the other hand, Lymphedema is a condition in which impaired lymph fluid flow causes swelling, usually in the arms or legs. Breast cancer treatment is the most common cause of lymphedema. Lymphedema occurs in 6% to 70% of breast cancer surgery survivors, the variation depending on the type of cancer and type of treatment. Lymphedema may occur anytime from shortly after treatment to as long as 20 years later. Lymphedema is one of the most feared side effects of breast cancer treatment, with more than 20 symptoms that range from a mild feeling of heaviness to swelling that is both disfiguring and disabling.

The researchers used five different machine learning algorithms to analyze data from 355 patients from 45 different regions; the Data analysed included, demographic and clinical information, lymphedema status, and symptoms. Of the five machine learning systems, the artificial neural network (ANN) produced the best results; it accurately detected lymphedema in 93.75% of the cases.

The implications of the Rory Myers College of Nursing test for detecting lymphedema in breast cancer survivors are clear, though with some reservations. More studies are needed to test the validity and reliability of the artificial neural networks. Also, the ANN system that works well at detecting lymphedema may not be as accurate with other breast cancer side effects or other types of cancer. The prospect of testing a multitude of conditions and treatments against a panel of machine learning algorithms may reveal a single strongest approach for many or most diseases.


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