A stage for Future Applications
On 1st Jan 2020, Google blog post said, in turn, the inaccuracies in breast cancer scanning can lead to delays in detection and treatment, unnecessary stress for patients and a higher workload for radiologists who are already in short supply. An Artificial Intelligence (AI)-based Google model has left radiologists behind in spotting breast cancer by just scanning the X-ray results. In a ray of hope for those who have to go for breast cancer screening and even for healthy women who get false alarms during digital mammography. Reading mammograms is a difficult task, even for experts, and can often result in both false positives and false negatives.
Google informed, “In this evaluation, our system produced a 5.7 percent reduction of false positives in the US and a 1.2 percent reduction in the UK. It produced a 9.4 percent reduction in false negatives in the US and a 2.7 percent reduction in the UK.” Shravya Shetty, Technical Lead, Google Health said, “This sets the stage for future applications where the model could potentially support radiologists performing breast cancer screenings.”
Finding Regarding Breast Cancer
With over 42 million exams performed each year in the US and the UK combined. Digital mammography or X-ray imaging of the breast is the most common method to screen for breast cancer. Further, enlighten the situation, Daniel Tse, Product Manager, Google Health, said, “But despite the wide usage of digital mammography, spotting and diagnosing breast cancer early remains a challenge.” Google is constantly working colleagues at DeepMind, Cancer Research UK Imperial Centre, Northwestern University and Royal Surrey County Hospital to see if AI could support radiologists to spot the signs of breast cancer more accurately. The journal Nature published the findings regarding the same showed that AI could improve the detection of breast cancer.
Google’s AI Model Stands for the Solution
Google developed an AI model that was trained to see if it could learn to spot signs of breast cancer in the scans and tuned on a representative data set comprised of de-identified mammograms from more than 76,000 women in the UK and more than 15,000 women in the US. The model was then evaluated on a separate de-identified data set of more than 25,000 women in the UK and over 3,000 women in the US. Google informed, “In this evaluation, our system produced a 5.7 percent reduction of false positives in the US and a 1.2 percent reduction in the UK. It produced a 9.4 percent reduction in false negatives in the US and a 2.7 percent reduction in the UK.” The researchers then trained the AI model only on the data from the women in the UK and then evaluated it on the data set from women in the US.