Breast cancer survivors rally in a support group.
Every two minutes, a woman is diagnosed with breast cancer. The question is, can those at high risk be identified early enough to survive?
New AI software has predicted risk equally well in both white and black women for the first time.
The current standard practice in medicine is not exactly precise. It relies on age, family history of cancer, and breast density, among other factors, to determine risk. But these factors do not always tell the whole story, leaving many women to slip through the cracks. In addition, a racial gap persists in breast cancer treatment and survival. African-American women are 42 percent more likely to die from the disease despite relatively equal rates of diagnosis.
But now those grim statistics could be changing. A team of researchers from MIT's Computer Science and Artificial Intelligence Laboratory have developed a deep learning model that can more accurately predict a patient's breast cancer risk compared to established clinical guidelines – and it has predicted risk equally well in both white and black women for the first time.
The Lowdown
Study results published in Radiology described how the AI software read mammogram images from more than 60,000 patients at Massachusetts General Hospital to identify subtle differences in breast tissue that pointed to potential risk factors, even in their earliest stages. The team accessed the patients' actual diagnoses and determined that the AI model was able to correctly place 31 percent of all cancer patients in the highest-risk category of developing breast cancer within five years of the examination, compared to just 18 percent for existing models.
"Each image has hundreds of thousands of pixels identifying something that may not necessarily be detected by the human eye," said MIT professor Regina Barzilay, one of the study's lead authors. "We all have limited visual capacities so it seems some machines trained on hundreds of thousands of images with a known outcome can capture correlations the human eye might not notice."
Barzilay, a breast cancer survivor herself, had abnormal tissue patterns on mammograms in 2012 and 2013, but wasn't diagnosed until after a 2014 image reading, illustrating the limitations of human processing alone.
MIT professor Regina Barzilay, a lead author on the new study and a breast cancer survivor herself.
(Courtesy MIT)
Next up: The MIT team is looking at training the model to detect other cancers and health risks. Barzilay recalls how a cardiologist told her during a conference that women with heart diseases had a different pattern of calcification on their mammograms, demonstrating how already existing images can be used to extract other pieces of information about a person's health status.
Integration of the AI model in standard care could help doctors better tailor screening and prevention programs based on actual instead of perceived risk. Patients who might register as higher risk by current guidelines could be identified as lower risk, helping resolve conflicting opinions about how early and how often women should receive mammograms.
Open Questions: While the results were promising, it's unknown how well the model will work on a larger scale, as the study looked at data from just one institution and used mammograms supplied by just one hospital. Some risk factor information was also unavailable for certain patients during the study, leaving researchers unable to fully compare the AI model's performance to that of the traditional standard.
One incentive to wider implementation and study, however, is the bonus that no new hardware is required to use the AI model. With other institutions now showing interest, this software could lead to earlier routine detection and treatment of breast cancer — resulting in more lives saved.
A robot server, controlled remotely by a disabled worker, delivers drinks to patrons at the DAWN cafe in Tokyo.
A sleek, four-foot tall white robot glides across a cafe storefront in Tokyo’s Nihonbashi district, holding a two-tiered serving tray full of tea sandwiches and pastries. The cafe’s patrons smile and say thanks as they take the tray—but it’s not the robot they’re thanking. Instead, the patrons are talking to the person controlling the robot—a restaurant employee who operates the avatar from the comfort of their home.
It’s a typical scene at DAWN, short for Diverse Avatar Working Network—a cafe that launched in Tokyo six years ago as an experimental pop-up and quickly became an overnight success. Today, the cafe is a permanent fixture in Nihonbashi, staffing roughly 60 remote workers who control the robots remotely and communicate to customers via a built-in microphone.
More than just a creative idea, however, DAWN is being hailed as a life-changing opportunity. The workers who control the robots remotely (known as “pilots”) all have disabilities that limit their ability to move around freely and travel outside their homes. Worldwide, an estimated 16 percent of the global population lives with a significant disability—and according to the World Health Organization, these disabilities give rise to other problems, such as exclusion from education, unemployment, and poverty.
These are all problems that Kentaro Yoshifuji, founder and CEO of Ory Laboratory, which supplies the robot servers at DAWN, is looking to correct. Yoshifuji, who was bedridden for several years in high school due to an undisclosed health problem, launched the company to help enable people who are house-bound or bedridden to more fully participate in society, as well as end the loneliness, isolation, and feelings of worthlessness that can sometimes go hand-in-hand with being disabled.
“It’s heartbreaking to think that [people with disabilities] feel they are a burden to society, or that they fear their families suffer by caring for them,” said Yoshifuji in an interview in 2020. “We are dedicating ourselves to providing workable, technology-based solutions. That is our purpose.”
Shota Kuwahara, a DAWN employee with muscular dystrophy. Ory Labs, Inc.
Shota, Kuwahara, a DAWN employee with muscular dystrophy, agrees. "There are many difficulties in my daily life, but I believe my life has a purpose and is not being wasted," he says. "Being useful, able to help other people, even feeling needed by others, is so motivational."
