Pittsburgh Health Data Alliance developing new AI models for oncology, mental health
More than a year since they announced a partnership to advance machine learning advancement in areas such as oncology, precision medicine and imaging, the researchers from the Pittsburgh Health Data Alliance and AWS are unveiling new AI-based techniques to assess breast cancer risk, understand tumor growth and better spot markers of depression.
WHY IT MATTERS
The PHDA members – UPMC, the University of Pittsburgh and Carnegie Mellon University – with funding from Amazon Research Awards, are tackling multiple areas where machine learning could lead to new medical innovation.
In one project, a team in the radiology department at the University of Pittsburgh are using deep-learning systems to analyze mammograms in order to predict the short‐term risk of developing breast cancer and develop a more personalized approach for patients undergoing screening.
Researchers gathered more than 450 de-identified normal screening mammogram images from 226 patients, half of whom later developed breast cancer and half of whom did not.
With help from AWS tools, they developed two different machine learning models to analyze the images for characteristics that could help predict breast cancer risk. Both outperformed the simple measure of breast density, which today is the primary imaging marker for breast cancer risk. The team’s models demonstrated between 33% and 35% improvement over these existing models, based on metrics that incorporate sensitivity and specificity.
"This preliminary work demonstrates the feasibility and promise of applying deep-learning methodologies for in-depth interpretation of mammogram images to enhance breast cancer risk assessment," said Shandong Wu, an associate professor in the University of Pittsburgh Department of Radiology, who is leading the team of breast cancer researchers.
"Identifying additional risk factors for breast cancer, including those that can lead to a more personalized approach to screening, may help patients and providers take more appropriate preventive measures to reduce the likelihood of developing the disease or catching it early on when interventions are most effective."
In another initiative, researchers at Pitt are developing new sensors that are able to detect and measure subtle biomarkers and changes in behavior that might indicate depression.
This research involves training natural language processing and visual recognition algorithms using examples of speech and facial expressions, and running experiments in parallel on multiple high-powered AWS services at once.
The goal is to help clinicians more efficiently assess depression patients and identify those who would otherwise go undiagnosed.
"Depression is a disease that affects more than 17 million adults in the United States, up to two-thirds of all depression cases are left undiagnosed and therefore untreated," said Louis-Philippe Morency, associate professor of computer science at Carnegie Mellon, which is also working on the project. "New insights to increase the accuracy, efficiency, and adoption of depression screening have the potential to impact millions of patients, their families, and the healthcare system as a whole."
Other ongoing research from PHDA members includes ways machine learning can assess risk of aneurysms and predict how cancer cells progress and how electronic health record usability can be improved.
THE LARGER TREND
The Pittsburgh Health Data Alliance was launched in 2015 as a way to capitalize on the data proliferating across healthcare, drive innovations in AI-powered healthcare analytics, and sometimes turn those ideas into new, for-profit companies via UPMC Enterprises (the commercialization arm of UPMC).
The partnership with AWS was announced in August 2019.
"We believe that machine learning can significantly accelerate the progress of medical research and help translate those advances into treatments and improved experiences for patients," said Swami Sivasubramanian, vice president of machine learning for AWS, at the time. "We are excited to bring our machine learning services and cloud computing resources to support the high-impact work being done at the PHDA."
ON THE RECORD
"Amazon is excited and encouraged by the progress these researchers are making and how machine learning is central to their work," said An Luo, senior technical program manager for academic programs, Amazon AI, in a statement. "We look forward to continuing to share how this unique collaboration between the PHDA and AWS is enabling new discoveries to help patients on a global scale."