AI algorithms can screen for pancreatic cancer and predict whether patients will develop the disease up to three years before a human doctor can make the same diagnosis, according to research published in Nature on Monday.
Pancreatic cancer is deadly; the five-year survival rate averages 12 percent. Academics working in Denmark and the US believe AI could help clinicians by detecting pancreatic cancer at earlier stages, if the software can reliably predict which patients are at higher risk of developing the disease.
The researchers trained AI algorithms on millions of medical records obtained in the Danish National Patient Registry and the US Veterans Affairs Corporate Data Warehouse. The models were trained to correlate diagnosis codes – labels used by hospitals describing different medical conditions – to pancreatic cancer.
> The most effective model, based on a transformer-based architecture, showed that out of the top 1,000 highest-risk patients over 50, about 320 would go on to develop pancreatic cancer. The model is less accurate when trying to predict pancreatic cancer over longer time intervals compared to shorter ones, and for patients younger than 50.
“Future AI-based screening tools for pancreatic cancer will have to be trained on specific local population data, the study found. A model trained on data from Danish patients, for example, was not as accurate when applied to US patients.”
That’s probsbly because it only works when training data are extracted from the “test” data set.
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[ 3.3 ms ] story [ 18.0 ms ] threadPancreatic cancer is deadly; the five-year survival rate averages 12 percent. Academics working in Denmark and the US believe AI could help clinicians by detecting pancreatic cancer at earlier stages, if the software can reliably predict which patients are at higher risk of developing the disease.
The researchers trained AI algorithms on millions of medical records obtained in the Danish National Patient Registry and the US Veterans Affairs Corporate Data Warehouse. The models were trained to correlate diagnosis codes – labels used by hospitals describing different medical conditions – to pancreatic cancer.
questions abound about model overfitting.
“Future AI-based screening tools for pancreatic cancer will have to be trained on specific local population data, the study found. A model trained on data from Danish patients, for example, was not as accurate when applied to US patients.”
That’s probsbly because it only works when training data are extracted from the “test” data set.