Researchers from the University of Waterloo and the Sunnybrook Research Institute in Toronto have developed machine learning software capable of detecting melanoma skin cancer. This early detection method aims to provide tools necessary to catch and treat skin cancer in its early and most treatable stages.

When caught early, melanoma skin cancer is highly treatable, but current methods relying on visual examinations of skin lesions are often inaccurate in identifying the cancer. To provide an effective tool to identify patients at risk for developing advanced melanoma skin cancer, researchers utilized artificial learning software.

"This could be a very powerful tool for skin cancer clinical decision support," said Alexander Wong, a professor of systems design engineering at Waterloo. "The more interpretable information there is, the better the decisions are."

The research was recently presented at the 14th International Conference on Image Analysis and Recognition and outlines the development of the AI system. Using thousands of images of skin, hemoglobin and eumelanin, the AI was able to analyze patient skin lesions and provide physicians with data on the biomarkers of melanoma. Physicians are then able to analyze biomarkers and treat patients accordingly.

"There can be a huge lag time before doctors even figure out what is going on with the patient," said Wong, who is also the Canada Research Chair in Medical Imaging Systems. "Our goal is to shorten that process."