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Chicago (United States), Jan 2 (Canadian-Media): Proof of Google artificial intelligence system to be as good as expert radiologists at detecting which women had breast cancer based on screening mammograms with reduced errors, was reported by a study published in the journal Nature on Wednesday by a team of researchers at Imperial College London and Britain's National Health Service in the United States and Britain.
A yellow box indicates where an artificial intelligence (AI) system found cancer hiding inside breast tissue, in an undated photo released by Northwestern University in Chicago on Jan. 1, 2020/Twitter
The findings of the study by the team of researchers, who trained the AI system to identify breast cancers on tens of thousands of mammograms were developed with Alphabet Inc.'s DeepMind AI unit -- which merged with Google Health in September -- represent a major advance in the potential for the early detection of breast cancer, said Mozziyar Etemadi, one of its co-authors from Northwestern Medicine in Chicago.
About 20 percent of breast cancers in mammograms, which affects one in eight women globally, are missed out by the radiologists, the American Cancer Society says, and half of all women who get the screenings over a 10-year period have a false positive result.
System's performance with the actual results from a set of 25,856 mammograms in the United Kingdom and 3,097 from the United States were then compared by them.
The study showed the AI system could identify cancers with a similar degree of accuracy to expert radiologists, while reducing the number of false positive results by 5.7 percent in the U.S.-based group and by 1.2 percent in the British-based group.
It also cut the number of false negatives, where tests are wrongly classified as normal, by 9.4 per cent in the U.S. group, and by 2.7 percent in the British group.
These differences reflect the ways in which mammograms are read.
Although decades old, and computer-aided detection (CAD) systems are commonplace in mammography clinics, yet CAD programs have not improved performance in clinical practice.
AI can use cues that humans can perceive because current CAD programs were trained to identify things human radiologists can see, whereas with AI, computers learn to spot cancers based on the actual results of thousands of mammograms.
The limitations of the study is that most of the tests were done using the same type of imaging equipment, and the U.S. group contained a lot of patients with confirmed breast cancers.
The team has yet to show the tool improves patient care, said Dr. Lisa Watanabe, chief medical officer of CureMetrix, whose AI mammogram program won U.S. approval last year.
"AI software is only helpful if it actually moves the dial for the radiologist," she said.
Etemadi agreed that those studies are needed, as is regulatory approval, a process that could take several years.