Artificial intelligence can significantly improve the diagnosis of lung disease, suggests a new study. Artificial intelligence (AI) can improve the diagnosis of lung disease by helping doctors interpret respiratory symptoms more accurately, according to new research. An AI computer algorithm using high quality data proved more consistent and accurate in interpreting respiratory test results and suggesting diagnoses than lung specialists, revealed recent research presented at the European Respiratory Society International Congress in Paris, France. "Pulmonary function tests provide an extensive series of numerical outputs and their patterns can be hard for the human eye to perceive and recognise; however, it is easy for computers to manage large quantities of data like these and so we thought AI could be useful for pulmonologists," said Dr Marko Topalovic, a postdoctoral researcher at the Laboratory for Respiratory Diseases, Catholic University of Leuven, Belgium. The study included 120 pulmonologists from 16 hospitals and researchers used historical data from 1,430 patients from 33 Belgian hospitals.

Dr Marko Topalovic (PhD), a postdoctoral researcher at the Laboratory for Respiratory Diseases, Catholic University of Leuven (KU Leuven), Belgium, told the meeting that after training an AI computer algorithm using good quality data, it proved to be more consistent and accurate in interpreting respiratory test results and suggesting diagnoses than lung specialists. "Pulmonary function tests provide an extensive series of numerical outputs and their patterns can be hard for the human eye to perceive and recognise; however, it is easy for computers to manage large quantities of data like these and so we thought AI could be useful for pulmonologists. We explored if this was true with 120 pulmonologists from 16 hospitals. We found that diagnosis by AI was more accurate in twice as many cases as diagnosis by pulmonologists. These results show how AI can serve as a second opinion for pulmonologists when they are assessing and diagnosing their patients," he said.

Artificial Intelligence (AI) or machine learning can be used to help improve the accuracy of the diagnosis in lung diseases, finds a study. 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 (BMI) and age. Based on the pattern of both the clinical and lung function data, the algorithm makes a suggestion for the most likely diagnosis. "We have demonstrated that AI can provide us with a more accurate diagnosis. The algorithm can simulate the complex reasoning that a clinician uses to give their diagnosis, but in a more standardised and objective way so it removes any bias," said Wim Janssens from the University of Leuven in Belgium.

Artificial Intelligence (AI) or machine learning can be used to help improve the accuracy of the diagnosis in lung diseases, finds a study. 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 (BMI) and age. Based on the pattern of both the clinical and lung function data, the algorithm makes a suggestion for the most likely diagnosis. "We have demonstrated that AI can provide us with a more accurate diagnosis. The algorithm can simulate the complex reasoning that a clinician uses to give their diagnosis, but in a more standardised and objective way so it removes any bias," said Wim Janssens from the University of Leuven in Belgium.

Artificial Intelligence (AI) or machine learning can be used to help improve the accuracy of the diagnosis in lung diseases, finds a study. Machine learning utilizes 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 (BMI) and age. Based on the pattern of both the clinical and lung function data, the algorithm makes a suggestion for the most likely diagnosis. "We have demonstrated that AI can provide us with a more accurate diagnosis. 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," said Wim Janssens from the University of Leuven in Belgium.

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.