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      Disease Staging and Prognosis in Smokers Using Deep Learning in Chest Computed Tomography

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Deep learning is a powerful tool that may allow for improved outcome prediction.

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          Speech Recognition with Deep Recurrent Neural Networks

          Alex Graves, Abdel-rahman Mohamed, Geoffrey E Hinton (2013)
          Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cursive handwriting recognition. However RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks. This paper investigates \emph{deep recurrent neural networks}, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs. When trained end-to-end with suitable regularisation, we find that deep Long Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to our knowledge is the best recorded score.
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            Determinants of underdiagnosis of COPD in national and international surveys.

            Bernd Lamprecht, Joan B Soriano, Michael Studnicka … (2015)
            COPD ranks within the top three causes of mortality in the global burden of disease, yet it remains largely underdiagnosed. We assessed the underdiagnosis of COPD and its determinants in national and international surveys of general populations.
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              Computed tomographic measurements of airway dimensions and emphysema in smokers. Correlation with lung function.

              Y Nakano, S Muro, H Sakai … (2000)
              Chronic obstructive pulmonary disease (COPD) is characterized by the presence of airflow obstruction caused by emphysema or airway narrowing, or both. Low attenuation areas (LAA) on computed tomography (CT) have been shown to represent macroscopic or microscopic emphysema, or both. However CT has not been used to quantify the airway abnormalities in smokers with or without airflow obstruction. In this study, we used CT to evaluate both emphysema and airway wall thickening in 114 smokers. The CT measurements revealed that a decreased FEV(1) (%predicted) is associated with an increase of airway wall area and an increase of emphysema. Although both airway wall thickening and emphysema (LAA) correlated with measurements of lung function, stepwise multiple regression analysis showed that the combination of airway and emphysema measurements improved the estimate of pulmonary function test abnormalities. We conclude that both CT measurements of airway dimensions and emphysema are useful and complementary in the evaluation of the lung of smokers.
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                Author and article information

                Journal
                Title: American Journal of Respiratory and Critical Care Medicine
                Abbreviated Title: Am J Respir Crit Care Med
                Publisher: American Thoracic Society
                ISSN (Print): 1073-449X
                ISSN (Electronic): 1535-4970
                Publication date Created: January 15 2018
                Publication date (Print): January 15 2018
                Volume: 197
                Issue: 2
                Pages: 193-203
                Affiliations
                [1 ]Sierra Research, Alicante, Spain
                [2 ]Applied Chest Imaging Laboratory, Department of Radiology, and
                [3 ]Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston Massachusetts
                Article
                DOI: 10.1164/rccm.201705-0860OC
                PMC ID: 5768902
                PubMed ID: 28892454
                SO-VID: fd144597-d93f-43f2-bf6d-83bfa58192ab
                Copyright © © 2018
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