In a new study published in Nature Medicine, Mayo Clinic and several research collaborators from across the U.S., describe a successful new artificial intelligence, or AI, -enabled tool to identify idiopathic pulmonary fibrosis, also called IPF, before patients have recognizable symptoms. This tool could alert a patient’s primary care team of a probable IPF diagnosis based on comorbidities and risk scores. The tool should prompt earlier referral to pulmonary specialty care to confirm the diagnosis with CT scanning and lab testing.
In the paper, the team describes how they were able to use “pattern discovery algorithms [to] identify subtle comorbidity incidence, timing and sequence characteristics presaging IPF.”
“IPF is a debilitating and ultimately fatal disease that is often difficult to diagnose,” says study co-author Andrew Limper, M.D., a pulmonary and critical care specialist at Mayo Clinic and leading IPF researcher. “Until now, that diagnosis required a barrage of tests. Furthermore, by the time IPF is identified, it’s often long after the patient has been struggling with advancing symptoms.”
According to the National Institutes of Health, people with this disease usually survive 3 to 5 years after diagnosis.
“We hoped to find an AI solution for earlier identification, because reaching the IPF diagnosis can often take as long as three years after symptoms start,” explains Dr. Limper. “Although there is currently no cure for IPF, earlier diagnosis gives patients more options to stall disease progression and maintain an optimal quality of life.”
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