We founded DeepMind Health to develop technologies that could help address some of society’s toughest challenges. So we’re very excited to announce that our latest research partnership will focus on breast cancer.
We’ll be working with a group of leading research institutions, led by the Cancer Research UK Centre at Imperial College London, and alongside the AI health research team at Google, to determine if cutting-edge machine learning technology could help improve the detection of breast cancer.
Breast cancer is a significant global health problem. Every single year, over 1.6 million people are diagnosed with the disease, and while advances in early detection and treatment have improved survival rates, breast cancer still claims the lives of 500,000 people around the world every year, around 11,000 of whom are here in the UK.
That’s partly because accurately detecting and diagnosing breast cancer still remains a huge challenge.
Currently, clinicians use mammograms (an X-ray of the breasts) to spot cancers early and determine the correct treatment, but this process is far from perfect. Thousands of cancer cases are not picked up by mammograms every year, including around 30% of “interval” cancers, which are cancers that are diagnosed between screenings. At the other end of the spectrum, false alarms and cases of overdiagnosis are also still a challenge, creating a great deal of unnecessary stress for patients.
Working alongside leading breast cancer experts, clinicians and academics, we’ll be exploring whether machine learning could help address this.
We’ll be using the latest machine learning technology to carefully analyse historic de-identified mammograms from around 7,500 women, provided by the Cancer Research UK-funded OPTIMAM mammography database at the Royal Surrey County Hospital NHS Foundation Trust. These digital images have been stripped of any information which could be used to identify patients, and have been available to research groups around the world for a number of years. We hope to use these images to investigate whether machine learning tools can spot signs of cancerous tissue on these X-rays and alert expert radiologists and oncologists more effectively than current screening techniques allow.
Our partners in this project wanted researchers at both DeepMind and Google involved in this research so that the project could take advantage of the AI expertise in both teams, as well as Google’s supercomputing infrastructure - widely regarded as one of the best in the world, and the same global infrastructure that powered DeepMind’s victory over the world champion at the ancient game of Go. We hope that this combination of partners will achieve more impactful results for patients, which is everyone’s priority.
As with all of our research work, DeepMind is committed to treating the data for this project with the utmost care and respect. As is standard practice, the data being used in the research remains in the full control of our partners, and is being stored to world-class standards of security and encryption. Additionally, all medical information has been thoroughly de-identified, with any information that could identify an individual being removed before researchers can conduct their analysis. You can read more about our approach to information governance here.
It’s early days, and the work we’re currently conducting is exploratory, but we’re optimistic about the long term potential for machine learning technology to help in this area. As the research progresses and any potential benefits become clearer, we commit to working with the NHS leadership to ensure that any technology we build following this research benefits the nation - whether through discounts on any new technology used in the national screening programme, as some have suggested, or another mechanism that provides value back to the NHS.
We also hope that in time other international research partners will join the project to make any findings more globally generalisable.
It’s a hugely exciting opportunity to make a difference and we will keep you updated as we make progress.