Tuesday, February 7, 2017
Artificial intelligence can spot skin cancer.
Computers can classify skin cancers as successfully as human experts, according to the latest research attempting to apply artificial intelligence to health. The US-based researchers say the new system, which is based on image recognition, could be developed for smartphones, increasing access to screening and providing a low-cost way to check whether skin lesions are cause for concern. According to the World Health Organisation, skin cancer accounts for one in every three cancers diagnosed worldwide, with global incidence on the rise. In the UK alone, 131,772 cases of non-melanoma skin cancer were recorded in 2014. In the same year there were 15,419 new cases of the deadliest skin cancer, melanoma, making it the fifth most common cancer, according to Cancer Research UK. As the disease is often initially spotted by a visual examination, Esteva teamed up with colleagues in fields ranging from dermatology to artificial intelligence to create a computer system that would aid screening. Their approach, described in the journal Nature, is based on deep learning – a class of algorithms used for artificial intelligence. When fed with a large set of ready-sorted data these algorithms pick out and “learn” patterns and relationships. Once trained, the algorithms can then be used to categorize new, unsorted data. To create the system, the team harnessed a deep learning algorithm built by Google that had already been presented with 1.28 million images of objects such as cats, dogs and cups.The researchers then fed the system more than 127,000 clinical images of skin lesions, each already labelled, encompassing many different skin diseases. Once trained, the team then tested the system’s ability to classify skin cancer by presenting it with just under 2,000 previously unseen images of skin lesions, whose nature had previously been determined by biopsy, and further compared the results for nearly 400 of the images against the judgement of at least 21 dermatologists. The results reveal that the system predicted the outcomes like the experts in telling apart carcinomas from common benign skin growths and melanomas from moles.