Researchers at MIT’s CSAIL division, which focuses on computer engineering and AI development, built two machine learning algorithms that can detect pancreatic cancer at a higher threshold than current diagnostic standards. The two models together formed to create the “PRISM” neural network. It is designed to specifically detect pancreatic ductal adenocarcinoma (PDAC), the most prevalent form of pancreatic cancer.
The current standard PDAC screening criteria catches about 10 percent of cases in patients examined by professionals. In comparison, MIT’s PRISM was able to identify PDAC cases 35 percent of the time.
While using AI in the field of diagnostics is not an entirely new feat, MIT’s PRISM stands out because of how it was developed. The neural network was programmed based on access to diverse sets of real electronic health records from health institutions across the US. It was fed the data of over 5 million patient’s electronic health records, which researchers from the team said “surpassed the scale” of information fed to an AI model in this particular area of research. “The model uses routine clinical and lab data to make its predictions, and the diversity of the US population is a significant advancement over other PDAC models, which are usually confined to specific geographic regions like a few healthcare centers in the US,” Kai Jia, MIT CSAIL PhD senior author of the paper said.
