Assessment of proximal and peripheral airway dysfunction by computed tomography and respiratory impedance in asthma and COPD patients with fixed airflow obstruction

OBJECTIVE: To ascertain: (i) if elderly patients with fixed airflow obstruction (FAO) due to asthma and chronic obstructive pulmonary disease (COPD) have distinct airway morphologic and physiologic changes; (ii) the correlation between the morphology of proximal/peripheral airways and respiratory impedance. METHODS: Twenty-five asthma cases with FAO and 22 COPD patients were enrolled. High-resolution computed tomography was used to measure the wall area (WA) and lumen area (LA) of the proximal airway at the apical segmental bronchus of the right upper lobe (RB1) adjusted by body surface area (BSA) and bronchial wall thickening (BWTr) of the peripheral airways and extent of expiratory air trapping (ATexp). Respiratory impedance included resistance at 5 Hz (R5) and 20 Hz (R20) and resonant frequency (Fres). Total lung capacity (TLC) and residual volume (RV) were measured. RESULTS: Asthma patients had smaller RB1-LA/BSA than COPD patients (10.5 ± 3.4 vs. 13.3 ± 5.0 mm2/m2, P = 0.037). R5 (5.5 ± 2.0 vs. 3.4 ± 1.0 cmH2O/L/s, P = 0.02) and R20(4.2 ± 1.7 vs. 2.6 ± 0.7 cmH2O/L/s, P = 0.001) were higher in asthma cases. ATexp and BWTr were similar in both groups. Regression analysis in asthma showed that forced expiratory volume in one second (FEV1) and Fres were associated with RB1-WA/BSA (R2 = 0.34, P = 0.005) and BWTr (0.5, 0.012), whereas RV/TLC was associated with ATexp (0.38, 0.001). CONCLUSIONS: Asthma patients with FAO had a smaller LA and higher resistance of the proximal airways than COPD patients. FEV1 and respiratory impedance correlated with airway morphology

Deep learning in chest radiography: Detection of findings and presence of change.

BACKGROUND:Deep learning (DL) based solutions have been proposed for interpretation of several imaging modalities including radiography, CT, and MR. For chest radiographs, DL algorithms have found success in the evaluation of abnormalities such as lung nodules, pulmonary tuberculosis, cystic fibrosis, pneumoconiosis, and location of peripherally inserted central catheters. Chest radiography represents the most commonly performed radiological test for a multitude of non-emergent and emergent clinical indications. This study aims to assess accuracy of deep learning (DL) algorithm for detection of abnormalities on routine frontal chest radiographs (CXR), and assessment of stability or change in findings over serial radiographs. METHODS AND FINDINGS:We processed 874 de-identified frontal CXR from 724 adult patients (> 18 years) with DL (Qure AI). Scores and prediction statistics from DL were generated and recorded for the presence of pulmonary opacities, pleural effusions, hilar prominence, and enlarged cardiac silhouette. To establish a standard of reference (SOR), two thoracic radiologists assessed all CXR for these abnormalities. Four other radiologists (test radiologists), unaware of SOR and DL findings, independently assessed the presence of radiographic abnormalities. A total 724 radiographs were assessed for detection of findings. A subset of 150 radiographs with follow up examinations was used to asses change over time. Data were analyzed with receiver operating characteristics analyses and post-hoc power analysis. RESULTS:About 42% (305/ 724) CXR had no findings according to SOR; single and multiple abnormalities were seen in 23% (168/724) and 35% (251/724) of CXR. There was no statistical difference between DL and SOR for all abnormalities (p = 0.2-0.8). The area under the curve (AUC) for DL and test radiologists ranged between 0.837-0.929 and 0.693-0.923, respectively. DL had lowest AUC (0.758) for assessing changes in pulmonary opacities over follow up CXR. Presence of chest wall implanted devices negatively affected the accuracy of DL algorithm for evaluation of pulmonary and hilar abnormalities. CONCLUSIONS:DL algorithm can aid in interpretation of CXR findings and their stability over follow up CXR. However, in its present version, it is unlikely to replace radiologists due to its limited specificity for categorizing specific findings