This AI tool helps identifies breast cancer with 90% accuracy rate

Topic: This AI tool helps identifies breast cancer with 90% accuracy rate

An artificial intelligence tool—trained on roughly a million screening mammography images—can identify breast cancer with approximately 90% accuracy when combined with radiologist analysis, a new study finds.

The study examined the ability of a type of artificial intelligence (AI), a machine learning computer program, to add value to the diagnoses a group of 14 radiologists reached as they reviewed 720 mammogram images.

“Our study found that AI identified cancer-related patterns in the data that radiologists could not, and vice versa,” says senior study author Krzysztof Geras, assistant professor in the radiology department at New York University’s Grossman School of Medicine.

“AI detected pixel-level changes in tissue invisible to the human eye, while humans used forms of reasoning not available to AI,” adds Geras, also an affiliated faculty member at the Center for Data Science. “The ultimate goal of our work is to augment, not replace, human radiologists.”

In 2014, women (without symptoms) in the United States got more than 39 million mammography exams to screen for breast cancer and determine the need for closer follow-up. Women whose test results yield abnormal mammography findings are referred for biopsy, a procedure that removes a small sample of breast tissue for laboratory testing.

In the new study, the research team designed statistical techniques that let their program “learn” how to get better at a task without being told exactly how. Such programs build mathematical models that enable decision-making based on data examples fed into them, with the program getting “smarter” as it reviews more and more data.

Modern AI approaches, which take inspiration from the human brain, use complex circuits to process information in layers, with each step feeding information into the next, and assigning more or less importance to each piece of information along the way.

The authors of the current study trained their AI tool on many images matched with the results of biopsies performed in the past. Their goal was to enable the tool to help radiologists reduce the number of biopsies needed moving forward. This can only be achieved, says Geras, by increasing the confidence that physicians have in the accuracy of assessments made for screening exams (for example, reducing false-positive and false-negative results).

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