A n estimated 12 million adults in the United States are diagnosed with chronic obstructive pulmonary disease (COPD) and an additional 12 million are thought to have undiagnosed COPD (1,2). CT captures the presence, pattern, and extent of phenotypic abnormalities associated with COPD. Both visual and quantitative CT assessments have been extensively validated and are considered complementary methods for assessment of COPD (3,4).The Fleischner Society proposed a structured system for visual classification of parenchymal emphysema, the prototypical pattern of emphysema seen in cigarette smokers (3). The system uses a six-point ordinal scale to grade parenchymal emphysema as absent, trace, mild, moderate, confluent, or advanced destructive. Visual assessment of emphysema by using the Fleischner system provides a valid and reproducible index of severity that is associated with impaired function and higher risk of mortality, genetic loci associated with COPD, and lung cancer (5-7). However, visual analysis by using a structured scoring system is time consuming, subjective, and requires substantial training, making it difficult to perform in routine practice (5,8,9). A validated automatic technique to classify emphysema patterns could be useful for risk stratification in clinical practice and lung cancer screening programs. In addition, such a technique could permit selection of participants with specific grades of emphysema (or with no emphysema) for future COPD clinical trials.Deep learning has provided dramatic advances in a wide range of challenging image analysis tasks including

Background Computed tomography (CT) plays a key role in evaluation of paranasal sinus inflammation, but improved, and standardized, objective assessment is needed. Computerized volumetric analysis has benefits over visual scoring, but typically relies on manual image segmentation, which is difficult and time‐consuming, limiting practical applicability. We hypothesized that a convolutional neural network (CNN) algorithm could perform automatic, volumetric segmentation of the paranasal sinuses on CT, enabling efficient, objective measurement of sinus opacification. In this study we performed initial clinical testing of a CNN for fully automatic quantitation of paranasal sinus opacification in the diagnostic workup of patients with chronic upper and lower airway disease. Methods Sinus CT scans were collected on 690 patients who underwent imaging as part of multidisciplinary clinical workup at a tertiary care respiratory hospital between April 2016 and November 2017. A CNN was trained to perform automatic segmentation using a subset of CTs (n = 180) that were segmented manually. A nonoverlapping set (n = 510) was used for testing. CNN opacification scores were compared with Lund‐MacKay (LM) visual scores, pulmonary function test results, and other clinical variables using Spearman correlation and linear regression. Results CNN scores were correlated with LM scores (rho = 0.82, p < 0.001) and with forced expiratory volume in 1 second (FEV1) percent predicted (rho = −0.21, p < 0.001), FEV1/forced vital capacity ratio (rho = −0.27, p < 0.001), immunoglobulin E (rho = 0.20, p < 0.001), eosinophil count (rho = 0.28, p < 0.001), and exhaled nitric oxide (rho = 0.40, p < 0.001). Conclusion Segmentation of the paranasal sinuses on CT can be automated using a CNN, providing truly objective, volumetric quantitation of sinonasal inflammation.

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The BODE index is good at predicting the worsening of HRQOL in patients with severe COPD.

BACKGROUND: Pulmonary endothelial damage has been shown to precede the development of emphysema in animals, and vascular changes in humans have been observed in COPD and emphysema.RESEARCH QUESTION: Is intraparenchymal vascular pruning associated with longitudinal progression of emphysema on CT imaging or decline in lung function over 5 years?STUDY DESIGN AND METHODS: The Genetic Epidemiology of COPD Study enrolled ever smokers with and without COPD from 2008 through 2011. The percentage of emphysemalike lung, or "percent emphysema," was assessed at baseline and after 5 years on noncontrast CT imaging as the percentage of lung voxels < -950 Hounsfield units. An automated CT imaging-based tool assessed and classified intrapulmonary arteries and veins. Spirometry measures are postbronchodilator. Pulmonary arterial pruning was defined as a lower ratio of small artery volume (< 5 mm 2 cross-sectional area) to total lung artery volume. Mixed linear models included demographics, anthropomorphics, smoking, and COPD, with emphysema models also adjusting for CT imaging scanner and lung function models adjusting for clinical center and baseline percent emphysema.RESULTS: At baseline, the 4,227 participants were 60 AE 9 years of age, 50% were women, 28% were Black, 47% were current smokers, and 41% had COPD. Median percent emphysema was 2.1 (interquartile range, 0.6-6.3) and progressed 0.24 percentage points/y (95% CI, ABBREVIATIONS: BV5 = volume of pulmonary vessels less than 5 mm 2 in cross-sectional area; BV5a = volume of pulmonary arteries less than 5 mm 2 in cross-sectional area; COPDGene = Genetic Epidemiology of COPD; HU = Hounsfield units; PD15 = lung density at the 15th percentile; percent emphysema -950 = percentage of lung volume with attenuation < -950 Hounsfield units; TBVa = total arterial volume of interparenchymal vessels