Application of Artificial Intelligence in the Interpretation of Pulmonary Function Tests

Background As per the Global Burden of Disease Study (GBD) 2019, chronic obstructive pulmonary disease (COPD) and asthma had a significant global burden. COPD is the fourth leading cause of death in the world and the second leading cause of death and disability-adjusted life years (DALYs) in India. Pulmonary function tests (PFTs) are commonly used diagnostic tools. They include spirometry, body plethysmography, and diffusion capacity. In regions with limited resources, pulmonologists often only have access to spirometry. Additionally, PFT pattern interpretation is usually unreliable and subjective. Recent rapid advances in artificial intelligence (AI) algorithms can bridge the gaps. Objectives This study aims to compare the accuracy of the predictions made by AI algorithms with pulmonologists using limited clinical data and spirometry. It also examines the consistency and accuracy of pulmonologists' predictions based on the same information. Methodology Different AI algorithms were trained, and their accuracy was evaluated. Spirometry and limited clinical data from 440 patients were interpreted by an AI algorithm and eight senior pulmonologists. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the different patterns. Results Approximately 60% of the cases involved male patients, and about 70% were between the ages of 21 and 60. The Fleiss's kappa was 0.46. While the accuracy of pulmonologists against the gold standard was 65.82%, the accuracy of the AI was 86.59%. Conclusions PFTs, when interpreted by pulmonologists with limited clinical and spirometry data, have lower accuracy and higher variability. AI algorithms can consistently produce high accuracy. Adopting such technology among clinicians, especially in resource-constrained regions, could be pivotal for offering quality healthcare. In addition, it will also help in getting rid of inter-observer variability.