The system from a team at National Taiwan University outperformed experienced radiologists in testing.

A team of researchers at the National Taiwan University has trained an AI system on hundreds of examples of tumours in the pancreas to teach it to recognise pancreatic cancer, one of the most deadly types of the disease after lung and colorectal cancers. According to the American Cancer Society, pancreatic cancer could kill over 49,000 people in 2022. Significantly, annually, only about a fifth of patients live for a year after diagnosis. (Inverse.com)

A new study of this AI tool published in the journal Radiology says that the technology would prove superior to current imaging techniques using CT scans which miss about 40% of pancreatic tumours that are smaller than 2 cm – the size beyond which the tumour spreads rapidly to other organs in the body.

Because these tumours usually do not have clear borders that demarcate them from the surrounding tissue they are hard to detect and many patients lose their chance at treatment before the tumour becomes rampant.

Once trained, the researchers tested the system on 546 scans of patients with pancreatic cancer, and 733 without, that is, a total of 1,279 patients. Significantly, the system was successful in detecting 90% of cancer cases. It also exhibited 96% efficiency at detecting the absence of cancer thus eliminating the chance of false positives.

In another test, which sourced data from 1,473 people in hospitals across Taiwan, the AI model achieved a 90% sensitivity and 93% specificity. It was accurate 75% of the time at detecting tumours which were smaller than 2 cm, a much better performance compared to radiologists who were only able to achieve 60% accuracy.

“I think AI can do two things: the first is to help doctors do what they can, but with less time and energy,” says Wei-Chi Liao, professor of internal medicine at the National Taiwan University and one of the lead study authors. “And the second is to help doctors do what they cannot always do; for example, in our study, detect cancers that are not very visible to humans.”

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