13 Dec, 2020, 16:00 ET
CHICAGO, Dec. 13, 2020 /PRNewswire/ -- Artificial intelligence (AI) has the potential to revolutionize healthcare, but integrating AI-based techniques into routine medical practice has proven to be a significant challenge. A plenary session at the virtual 2020 AACC Annual Scientific Meeting & Clinical Lab Expo will explore how one clinical lab overcame this challenge to implement a machine learning-based test, while a second session will take a big picture look at what machine learning is and how it could transform medicine.
Machine learning is a type of AI that uses statistics to find patterns in massive amounts of data. It could launch healthcare into a new era by mining medical data to find cures for diseases, identify vulnerable patients before they become ill, and better personalize testing and treatments. In spite of this technology's promise, though, the medical community continues to grapple with numerous barriers to adoption, and in the field of laboratory medicine in particular, very few machine learning tests are currently offered as part of regular care.
A 10-year machine learning project undertaken by Ulysses G.J. Balis, MD, and his colleagues at the University of Michigan in Ann Arbor could help to change this by providing a blueprint for other healthcare institutions looking to harness AI. As Dr. Balis will discuss in his plenary session, his institute developed and implemented a machine learning test called ThioMon to guide treatment of inflammatory bowel disease (IBD) with azathioprine. With an approximate cost of only $20 a month, azathioprine is much cheaper than other IBD medications (which can cost thousands of dollars a month), but its dosage needs to be finetuned for each patient, making it difficult to prescribe. ThioMon solves this issue by analyzing a patient's routine lab test results to determine if a particular dose of azathioprine is working or not.
Balis's team found that the test performs just as well as a colonoscopy, which is the current gold standard for assessing IBD patient response to medication. Even more exciting is that clinical labs could use ThioMon's general approach—analyzing routine lab test results with machine learning algorithms—to solve any number of other patient care challenges.
"There are dozens, if not hundreds of additional diagnoses that we can extract from the routine lab values that we've been generating for decades," said Dr. Balis. "This lab data is, in essence, a gold mine, and the development of these machine learning tools marks the start of a new gold rush."
One of the additional conditions that this machine learning approach can diagnose is, in fact, COVID-19. In the session, "How Clinical Laboratory Data Is Impacting the Future of Healthcare?" Jonathan Chen, MD, PhD, of Stanford University, and Christopher McCudden, PhD, of the Eastern Ontario Regional Laboratory Association, will touch on a new machine learning test that analyzes routine lab test results to determine if patients have COVID-19 even before their SARS-CoV-2 test results come back. As COVID-19 cases in the U.S. reach record highs, this test could enable labs to diagnose COVID-19 patients quickly even if SARS-CoV-2 test supply shortages worsen or if SARS-CoV-2 test results become backlogged due to demand.
Beyond this, Drs. Chen and McCudden plan to give a bird's eye view of what machine learning is, how it works, and how it can improve efficiency, reduce costs, and improve patient outcomes—particularly by democratizing patient access to medical expertise.
"Medical expertise is the scarcest resource in the healthcare system," said Dr. Chen, "and computational, automated tools will allow us to reach the tens of millions of people in the U.S.—and the billions of people worldwide—who currently don't have access to it."
Machine Learning Sessions at the 2020 AACC Annual Scientific Meeting
AACC Annual Scientific Meeting registration is free for members of the media. Reporters can register online here: https://www.xpressreg.net/register/aacc0720/media/landing.asp
Session 14001: Between Scylla and Charybdis: Navigating the Complex Waters of Machine Learning in Laboratory Medicine
Session 34104: How Clinical Laboratory Data Is Impacting the Future of Healthcare?
Abstract A-005: Machine Learning Outperforms Traditional Screening and Diagnostic Tools for the Detection of Familial Hypercholesterolemia
About the 2020 AACC Annual Scientific Meeting & Clinical Lab Expo
The AACC Annual Scientific Meeting offers 5 days packed with opportunities to learn about exciting science from December 13-17, all available on an online platform. This year, there is a concerted focus on the latest updates on testing for COVID-19, including a talk with current White House Coronavirus Task Force testing czar, Admiral Brett Giroir. Plenary sessions include discussions on using artificial intelligence and machine learning to improve patient outcomes, new therapies for cancer, creating cross-functional diagnostic management teams, and accelerating health research and medical breakthroughs through the use of precision medicine.
At the virtual AACC Clinical Lab Expo, more than 170 exhibitors will fill the digital floor with displays and vital information about the latest diagnostic technology, including but not limited to SARS-CoV-2 testing, mobile health, molecular diagnostics, mass spectrometry, point-of-care, and automation.
Dedicated to achieving better health through laboratory medicine, AACC brings together more than 50,000 clinical laboratory professionals, physicians, research scientists, and business leaders from around the world focused on clinical chemistry, molecular diagnostics, mass spectrometry, translational medicine, lab management, and other areas of progressing laboratory science. Since 1948, AACC has worked to advance the common interests of the field, providing programs that advance scientific collaboration, knowledge, expertise, and innovation. For more information, visit www.aacc.org.
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