Project Summary Mucus plugging has long been implicated in acute and fatal respiratory events in severe asthma, but we have recently shown that chronic mucus plugging is common in asthmatic patients and appears mechanistically linked with both impaired airflow and worsening disease severity. In particular, in analyses of baseline computed tomography (CT) lung scans in asthmatic patients, we found that airway mucus plugs are highly prevalent, persist for many years, frequently occur without cough and sputum symptoms, and are strongly associated with airflow obstruction. However, it is unknown what radiographic characteristics of mucus plugging cause severe airflow obstruction, in part because detailed characterization of mucus plugs on CT scan is extremely labor intensive and requires highly trained thoracic radiologists for assessment. In Aim 1 of this application, we propose to test the hypotheses, informed by our preliminary data, that three radiographic features of mucus plugs— mucus plug volume, number of proximal plugs, and fraction of airway tree occluded —all predict worsening airflow obstruction. We additionally propose that the airway tree can be converted into a network of resistive elements in which the effective resistance of the entire tree is computed with and without mucus plugs, and the relative contribution of mucus plugs to airway resistance can be determined. In Aim 2, we aim to substantially lower the barrier to quantification of mucus plugging on CT scans by developing an automated, convolutional neural network-based algorithm for mucus plug segmentation. We believe that our findings will allow the identification of a large subset of patients with chronic severe “mucushigh” asthma and raise possibilities for novel mucus-targeted treatments to improve airflow and other disease outcomes in this subset of patients.