How Artificial Intelligence helps early detection of skin cancer

Technology and its output definitely a big help to mankind.

This year, Artificial Intelligence (AI) has been in headlines online and even offline, because it helps a lot of people with its salient tool in different situations.

And now, AI can now helps with earlier detection of skin cancer.

Researchers from the University of Waterloo and the Sunnybrook Research Institute developed a new technology using artificial intelligence (AI) to help detect melanoma skin cancer earlier.

This new technology employs machine-learning software to analyze images of skin lesions and provide doctors with objective data on telltale biomarkers of melanoma, which is deadly if detected too late, but highly treatable if caught early.

Also, the AI system is utilizing tens of thousands of skin images and their corresponding eumelanin and hemoglobin levels–could initially reduce the number of unnecessary biopsies, a significant health-care cost.

Furthermore it also gives doctors objective information on lesion characteristics to help them rule out melanoma before taking more invasive action.

According to some report, this new technology could be available to doctors as early as next year.

“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,” Wong added.

As of now, dermatologists largely rely on subjective visual examinations of skin lesions such as moles to decide if patients should undergo biopsies to diagnose the disease.

Wong said that the technology would definitely help not just the patient but also the doctors in realizing the disease in much earlier time.

“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,” he concluded.

The research findings were presented at the 14th International Conference on Image Analysis and Recognition held in Montreal.