DeepMind is furthering its cancer research efforts with a newly announced partnership. Today, the London-based Google subsidiary said it has been given access to mammograms from roughly 30,000 women that were taken at Jikei University Hospital in Tokyo, Japan between 2007 and 2018. It'll use that data to refine its artificially intelligent (AI) breast cancer detection algorithms. Over the course of the next five years, DeepMind researchers will review the 30,000 images, along with 3,500 images from magnetic resonance imaging (MRI) scans and historical mammograms provided by the U.K.'s Optimam (an image database of over 80,000 scans extracted from the NHS' National Breast Screening System), to investigate whether its AI systems can accurately spot signs of cancerous tissue. The collaboration builds on DeepMind's work with the Cancer Research UK Imperial Center at Imperial College London, where it has already analyzed roughly 7,500 mammograms.
A newly developed artificial intelligence (AI) model is able to spot breast cancer better than a clinician, new research has suggested. Google DeepMind, in partnership with Cancer Research UK Imperial Centre, Northwestern University and Royal Surrey County Hospital, has developed the model which can spot cancer in breast screening mammograms in a bid to improve health outcomes and ease pressure on overstretched radiology services. Initial findings, published by the technology giant in the journal Nature, suggest the AI can identify the disease with greater accuracy, fewer false positives and fewer false negatives. The model, trained on de-identified data of 76,000 women in the UK and more than 15,000 women in the US, reportedly lowered false positive results by 1.2% and false negatives by 2.7% in the UK, but is yet to be tested in clinical studies. When tested, the AI system processed only the latest available mammogram of a patient, whereas clinicians had access to patient histories and prior mammograms to make an informed screening decision.
CHICAGO – A Google artificial intelligence system proved as good as expert radiologists at predicting which women would develop breast cancer based on screening mammograms and showed promise at reducing errors, researchers in the United States and Britain reported. The study, published in the journal Nature on Wednesday, is the latest to show that artificial intelligence (AI) has the potential to improve the accuracy of screening for breast cancer, which affects one in eight women globally. Radiologists miss about 20 percent of breast cancers in mammograms, the American Cancer Society says, and half of all women who get the screenings over a 10-year period have a false positive result. The findings of the study, developed with Alphabet'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. The team, which included researchers at Imperial College London and Britain's National Health Service, trained the system to identify breast cancers on tens of thousands of mammograms.
DeepMind has attracted mixed headlines since Google paid $50 million for the U.K.-based AI startup in 2014. The awe inspired by DeepMind's AlphaGo system defeating Go world champion Lee Sedol was soon tempered by criticisms of its controversial access to personal health records, which the ICO ruled had breached the Data Protection Act, and the concerns grew when Google announced it would be taking control of DeepMind Health. Trust has wavered ever since, but the AI developed in the DeepMind lab in King's Cross, London, continues to lead the world and is finding its way into some intriguing applications. DeepMind is collaborating with Google's AOI health research team and a group of research institutions, led by the Cancer Research U.K. Centre at Imperial College London to improve the detection of breast cancer. The disease kills 500,000 people around the world every year, partly due to the challenges of detection and diagnosis.