Diagnosing emphysema and classifying its severity have long been more art than science.
"Everybody has a different trigger threshold for what they would call normal and what they would call disease," said U. Joseph Schoepf, M.D., director of cardiovascular imaging for MUSC Health and assistant dean for clinical research in the Medical University of South Carolina College of Medicine. And until recently, scans of damaged lungs have been a moot point, he said.
"In the past, if you lost lung tissue, that was it. The lung tissue was gone, and there was very little you could do in terms of therapy to help patients," he said.
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But with advancements in treatment in recent years has come an increased interest in objectively classifying the disease, Schoepf said. That's where artificial intelligence and imaging could come into play.
Schoepf was principal investigator in a study looking at the results of Siemens Healthineers' AI-Rad Companion as compared with traditional lung function tests. The study, published online in the American Journal of Roentgenology in March, showed that the algorithm within AI-Rad Companion, which examines chest scans, provides results comparable with lung function tests, which measure how forcefully a person can exhale. Showing that the artificial intelligence software works is the first step toward possibly using chest scans to quantify the severity of the lung disease and track the progress of treatment.
In the study, researchers went back and looked at the chest scans and lung function tests of 141 people. Chest scans aren't currently part of the guidelines for diagnosing chronic obstructive pulmonary disease, an umbrella term that includes emphysema, chronic bronchitis and other lung diseases, Schoepf said, because there hasn't been an objective means to evaluate scans.
However, he anticipates a role for imaging scans if it can be shown that they offer a benefit in terms of objectivity and quantification.
Philipp Hoelzer, customer engagement manager with Siemens Healthineers, said having an objective measurement could help in assessing the value of new treatments or drugs. The Siemens Healthineers team sees the program as a way for artificial intelligence to work in tandem with the clinical expertise of radiologists, he said.
"Taking away manual, repetitive tasks, like those that require a lot of measurement, is of great benefit to a radiologist, especially when reading cases that may have 20 or more nodules," he said. "Interpreting the images, and the abstract thinking that goes along with it, will remain with the radiologist."
