The results of a study presented at the European Respiratory Society’s International Congress indicate that artificial intelligence could improve the interpretation of lung function tests for the diagnosis of long-term lung diseases. According to a European Lung Foundation news release, the study is the first to explore the potential use of artificial intelligence for improving the accuracy of the diagnosis of lung diseases. In the study, researchers included data from 968 people who were undergoing complete lung function testing for the first time, and all participants received a first clinical diagnosis based on lung function tests and other needed tests, such as CT scans.

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 analyze the complete lung function tests, according to the European Lung Foundation news release, which is a type of learning that utilizes algorithms that can learn from and perform predictive data analysis. The researchers developed an algorithm process in addition to the routine lung function parameters and clinical variables of smoking history, age, and body mass index.

Based on the pattern of both the clinical and lung function data, the algorithm makes a suggestion for the most likely diagnosis. Senior author of the study Wim Janssens says, “We have demonstrated that artificial intelligence can provide us with a more accurate diagnosis in this new study. The beauty of our development is that the algorithm can simulate the complex reasoning that a clinician uses to give their diagnosis, but in a more standardized and objective way so it removes any bias.”

Currently, clinicians must rely on analyzing the results using population-based parameters; however, with artificial intelligence, the machine can observe a combination of patterns at one time to help produce a more accurate diagnosis. The European Lung Foundation news release notes that this has previously happened in other fields of health with an automated interpretation of results from an electrocardiogram being routinely used in clinical practice as a decision support system.

“The benefit of this method is a more accurate and automated interpretation of pulmonary function tests, and thus better disease detection,” explains Marko Topalovic, the first author of the study. “Not only can this help non-experienced clinicians, but it also has many benefits for healthcare overall as it is time saving in achieving a final diagnosis as it could decrease the number of redundant additional tests clinicians are taking to confirm the diagnosis.”

For their next step, the researchers will test the algorithm in different populations and increase the decision power of the system with continuous updates on lung function data with a validated clinical diagnosis.

Source: European Lung Foundation