“Clinical risk models depend on gathering information from different sources, which isn’t always available or collected. Recent advances in AI deep learning provide us with the ability to extract hundreds to thousands of additional mammographic features,” said Arasu in an
The five-year study period was divided into three time periods: “interval cancer risk, or incident cancers diagnosed between 0 and 1 years; future cancer risk, or incident cancers diagnosed from between one and five years; and all cancer risk, or incident cancers diagnosed between 0 and 5 years.”algorithms. The risk scores were then compared to the BCSC clinical risk score and with each AI model.
Patients with a high risk of interval cancer were accurately predicted by some of the AI algorithms. AI predicted up to 28 percent of cancers when evaluating women at the highest risk, whereas the BCSC's scored 21 percent.
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