1 in 8 women globally is diagnosed with breast cancer at least once in their lifetime. 685,000 of them will lose their lives to the disease each year.
Too many women are enduring invasive treatments, from chemotherapies to mastectomies. They face physical and mental pain on the path to surviving the disease, and their lives, post-diagnosis, are irrevocably and assuredly changed forever.
Yet it doesn’t need to be this way.
Research has consistently shown that the earlier a breast cancer diagnosis is made, the higher a woman’s chances of survival. Early-stage cancers detected in screening often have over a 90% survival rate, versus the 30% survival rate seen by advanced cancers. The gap between these percentages rises further in low and middle-income countries. Early-stage cancers can easily, affordably, and effectively be treated. Advanced cancers cannot.
The solution is therefore clear: to significantly reduce the number of breast cancer fatalities worldwide, we need more screening to catch these cancers before they even get a chance to take hold. So what’s stopping us?
Artificial intelligence (AI) has been viewed as one of the leading solutions to catching these cancers early. It’s almost the ideal use case for increasing the efficiency and accuracy of mammography screening, because of the way screening is typically managed. Women at risk, generally those around 50+, are advised to have their breasts examined regularly through mammograms, even if they don’t have symptoms. Specialised screening physicians then search these scans for subtle, early signs of developing cancer. Screening is a very repetitive task. Only 0.7% of all women in an asymptomatic screening population have breast cancer, but every image needs to be meticulously analysed and reported on in order to make sure no cancers slip through the net.
This abundance of structured data and the repetitive nature of the processes involved lend themselves perfectly to deep learning algorithms and AI.
Yet, more often than not, the AI solutions put forward fail.
At Vara, we are approaching this problem differently from the AI systems that have come, and gone before. As deep tech entrepreneurs, we started from scratch; with a fresh perspective and a bigger picture in mind.
Instead of a standalone AI tool, we’ve built an end-to-end platform that re-imagines the entire clinical workflow with the goal not to replace radiologists, but to supercharge them.
The Vara platform captures real-world data while in clinical use — an invaluable asset to transparently measure real-world health outcomes driven by Vara, create a real-time feedback loop for clinicians, and improve our models over time.
Most AI solutions promise to reduce costs for providers who already run screening at high volume. While that can be a good starting point, it’s a zero-sum game. We are convinced the real opportunity lies in increasing access to effective breast cancer screening to women worldwide in the first place. Women that otherwise wouldn’t be screened. That’s when AI doesn’t play the zero-sum game but instead drives value to everyone.
Today, more than 20% of all screening centres in Germany are on the Vara platform. Vara’s performance has been developed and validated on more than 6 million exams. And we have already started to launch AI-based screening centres in areas women weren’t screened at scale before.
But we’re getting ahead of ourselves. In our next articles, we’ll explain our fresh, new approach in detail.
At the rate we’re seeing, and if something significant is not done now, the World Health Organisation forecasts that deaths from breast cancer will grow to 1.04 million by 2040. We can’t and won’t rest until we bring this down to zero.