Pattern recognition of forced oscillation technique measurement results using deep learning can identify asthmatic patients more accurately than setting reference ranges.

No official clinical reference values have been established for MostGraph, which measures total respiratory resistance and reactance using the forced oscillation technique, complicating result interpretation. This study aimed to establish a reference range for MostGraph measurements and examine its usefulness in discriminating participants with asthma from controls (participants without any respiratory diseases). The study also aimed to investigate the effectiveness of deep learning in discriminating between the two aforementioned groups. To establish reference ranges, the MostGraph measurements of healthy controls (n = 215) were power-transformed to distribute the data more normally. After inverse transformation, the mean ± standard deviation × 2 of the transformed values were used to establish the reference ranges. The number of measured items outside the reference ranges was evaluated to discriminate patients with asthma (n = 941) from controls. Additionally, MostGraph measurements were evaluated using deep learning. Although reference ranges were established, patients with asthma could not be discriminated from controls. However, with deep learning, we could discriminate between the two groups with 78% accuracy. Therefore, deep learning, which considers multiple measurements as a whole, was more effective in interpreting MostGraph measurement results than use of reference ranges, which considers each result individually.