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This article was published on February 15, 2021


This ‘AI doctor’ can assess skin melanoma as accurately as human dermatologists

The algorithm analyzes images of skin cancer to assess the severity

This ‘AI doctor’ can assess skin melanoma as accurately as human dermatologists
Thomas Macaulay
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Thomas Macaulay

Writer at Neural by TNW — Thomas covers AI in all its iterations. Likes Werner Herzog films and Arsenal FC. Writer at Neural by TNW — Thomas covers AI in all its iterations. Likes Werner Herzog films and Arsenal FC.

Scientists from the University of Gothenburg have developed an algorithm that assesses the severity of skin melanoma as accurately as dermatologists.

The system was developed to help doctors determine the stage that a skin cancer has reached.

While patients often independently find melanomas by spotting a new mole or a change in an existing one, even dermatologists can struggle to decide whether it’s invasive or not.

The researchers suspected that AI could assist them with the task.

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They classified the melanomas with a convolutional neural network (CNN), a powerful method of analyzing images that’s proven adept at identifying different skin lesions.

The researchers trained and validated the CNN on 937 images of melanomas collected through a dermascope, a handheld instrument used to examine the skin.

They then tested the algorithm’s evaluations on 200 cases that had been diagnosed by a dermatopathologist.

When they compared its performance to the analysis of seven independent dermatologists, the result was a draw.

“None of the dermatologists significantly outperformed the ML algorithm,” said study author Sam Polesie.

The researchers acknowledge that the algorithm still needs further refinement and longer-term evaluation in a clinical setting.

However, their study shows that AI could help assess the severity of melanoma before surgery, which affects how extensive an operation needs to be.

You can read a pre-proof of the study paper in the Journal of the American Academy of Dermatology.

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