There are still doubts as to when and how often women should have mammograms to prevent breast cancer? Studies clearly show that screening can lead to early diagnosis of the disease when it is most curable.
This is why improving the effectiveness of mammograms that can detect potentially cancerous abnormalities is a top priority. And this is where Google's AI comes in.
Artificial intelligence could play a crucial role in this field. AI machine learning could help doctors read mammograms more accurately.
In a study published January 1 in NatureGoogle Health and University and US and UK researchers illustrate an AI model that reads mammograms with fewer false positives and false negatives than human experts.
The algorithm, based on mammograms taken from more than 76.000 women in the UK and more than 15.000 in the US, reduced false positive rates by almost 6% in the US, where women are screened every 1-2 years. In the UK, where women are screened every three years, the improvement was “only” 1,2%.
The artificial intelligence model also reduced false negatives in mammograms by more than 9% in the USA and almost 3% in the UK.
“Reading mammograms is a perfect task to entrust to machine learning and artificial intelligence”, says dr. Mozziyar Etemadi, assistant researcher professor of anesthesia and biomedical engineering at Northwestern University, one of the co-authors of the study.
“AI excels when it has to do the same task over and over again and has to find the one thing that might appear once in 10.000. But I honestly didn't expect that it already worked much better than doctors. I was surprised."
It's an improvement over other smaller studies on artificial intelligence and mammograms
In another study, a machine had beaten over 101 radiologists in reading the scans. This recent study is one of the most statistically significant to date given its large dataset and the fact that the artificial intelligence model has surpassed doctors.
Once the team knew that the AI could be trained to effectively read mammograms on both the US and UK datasets, they ran another test. He trained the algorithm on US data and then applied it on UK cases and vice versa. Again, the results were better than those of the doctors. “It's encouraging because in real-world situations where you employ these models, that's exactly what will happen. It will be used on populations for which it may not necessarily have been trained.", he claims Shravya Shetty, Google Health technical manager.
More and more good
An advantage of the Google platform is its processing power. As the resolution of mammography images has improved in recent years, they have become so full of data that the human eye (even one belonging to a highly trained radiologist) may not be able to fully process them. Google's computing power allowed the algorithm to process almost all available pixels.
In order for an artificial intelligence algorithm to recognize abnormal lesions in breast tissue, the model must be trained with a huge number of mammographic images. The more we have, the better.
For now, experts see (as they should) AI as a support for radiologists reading mammography images, rather than as a replacement.
For example, artificial intelligence models could perform the first pass of assessments, leaving the experts, who have other valuable information such as a woman's family history of cancer, the task of interpreting more difficult cases.
“Health care is being compressed as the number of patients increases and the time doctors have to observe to evaluate patients is reduced. This is why tools like these are what every doctor waits for", says Etemadi.
