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 at the European Respiratory Society’s International Congress, explored 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. All participants received a first clinical diagnosis based on lung function tests and all other necessary additional tests (such as CT scans, electrocardiogram, etc.). The final diagnosis was validated by the consensus of the large group of expert clinicians.
The researchers subsequently investigated whether a concept known as ‘machine learning’ could be used to analyse the complete lung function tests. Machine learning utilises algorithms that can learn from and perform predictive data analysis.
The team developed an algorithm process in addition to the routine lung function parameters and clinical variables of smoking history, body mass index, and age. Based on the pattern of both the clinical and lung function data, the algorithm makes a suggestion for the most likely diagnosis.
EH News Bureau
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