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.

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.

"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. But with advancements in treatment in recent years has come an increased interest in objectively classifying the disease, Schoepf said.

Artificial intelligence could improve the interpretation of lung function tests for the diagnosis of long-term lung diseases, according to the findings of a new study. The results, presented today (04 September, 2016) at the European Respiratory Society's International Congress, are the first to explore the potential use of artificial intelligence for improving the accuracy of the diagnosis of lung diseases. Current testing requires a series of methods including a spirometry test, which measures the amount (volume) and the speed (flow) of air during breathing, followed by a body plethysmography test measuring static lung volumes and airways resistance and finally a diffusion test, which measures the amount of oxygen and other gases that cross the lungs' air sacs. Analysis of the results of these tests is largely based on expert opinion and international guidelines, attempting to detect a pattern in the findings. In this new study, researchers included data from 968 people who were undergoing complete lung function testing for the first time.

London, UK: Artificial intelligence could improve the interpretation of lung function tests for the diagnosis of long-term lung diseases, according to the findings of a new study. The results, presented today (04 September, 2016) at the European Respiratory Society's International Congress, are the first to explore the potential use of artificial intelligence for improving the accuracy of the diagnosis of lung diseases. Current testing requires a series of methods including a spirometry test, which measures the amount (volume) and the speed (flow) of air during breathing, followed by a body plethysmography test measuring static lung volumes and airways resistance and finally a diffusion test, which measures the amount of oxygen and other gases that cross the lungs' air sacs. Analysis of the results of these tests is largely based on expert opinion and international guidelines, attempting to detect a pattern in the findings. In this new study, researchers included data from 968 people who were undergoing complete lung function testing for the first time.

Artificial intelligence could improve the interpretation of lung function tests for the diagnosis of long-term lung diseases, according to the findings of a new study. The results, presented today (04 September, 2016) at the European Respiratory Society's International Congress, are the first to explore the potential use of artificial intelligence for improving the accuracy of the diagnosis of lung diseases. Current testing requires a series of methods including a spirometry test, which measures the amount (volume) and the speed (flow) of air during breathing, followed by a body plethysmography test measuring static lung volumes and airways resistance and finally a diffusion test, which measures the amount of oxygen and other gases that cross the lungs' air sacs. Analysis of the results of these tests is largely based on expert opinion and international guidelines, attempting to detect a pattern in the findings. In this new study, researchers included data from 968 people who were undergoing complete lung function testing for the first time.