Emphysema
What's New
Last Posted: Nov 01, 2022
- The Potential Role of Artificial Intelligence in Lung Cancer Screening Using Low-Dose Computed Tomography.
Grenier Philippe A et al. Diagnostics (Basel, Switzerland) 2022 12(10) - Use of machine learning models to predict prognosis of combined pulmonary fibrosis and emphysema in a Chinese population.
Liu Qing et al. BMC pulmonary medicine 2022 22(1) 327 - Invited editorial: Q and A on hereditary lung cancer.
Benusiglio Patrick R et al. Respiratory medicine and research 2022 81100881 - AI-Driven Model for Automatic Emphysema Detection in Low-Dose Computed Tomography Using Disease-Specific Augmentation.
Nagaraj Yeshaswini et al. Journal of digital imaging 2022 - An Italian expert consensus on the management of alpha1-antitrypsin deficiency: a comprehensive set of algorithms.
Balbi Bruno et al. Panminerva medica 2022 - Using contrast-enhanced CT and non-contrast-enhanced CT to predict EGFR mutation status in NSCLC patients-a radiomics nomogram analysis.
Yang Xiaoyan et al. European radiology 2021 - Prevalence of Alpha-1 Antitrypsin Deficiency, Self-reported Behavior Change, and Healthcare Engagement Among Direct-to-Consumer Recipients of a Personalized Genetic Risk Report.
Ashenhurst James R et al. Chest 2021 - Differential Genomic Profile in TERT, DSP, and FAM13A Between COPD Patients With Emphysema, IPF, and CPFE Syndrome.
Guzmán-Vargas Javier et al. Frontiers in medicine 2021 8725144 - The cardiovascular phenotype of Chronic Obstructive Pulmonary Disease (COPD): Applying machine learning to the prediction of cardiovascular comorbidities.
Nikolaou Vasilis et al. Respiratory medicine 2021 186106528 - Automated CT Staging of Chronic Obstructive Pulmonary Disease Severity for Predicting Disease Progression and Mortality with a Deep Learning Convolutional Neural Network.
Hasenstab Kyle A et al. Radiology. Cardiothoracic imaging 2021 3(2) e200477 - The Emerging Role of Quantification of Imaging for Assessing the Severity and Disease Activity of Emphysema, Airway Disease, and Interstitial Lung Disease.
Goldin Jonathan Gerald et al. Respiration; international review of thoracic diseases 2021 1-14 - Improving Clinical Disease Sub-typing and Future Events Prediction through a Chest CT based Deep Learning Approach.
Singla Sumedha et al. Medical physics 2020 Dec - Relative contributions of family history and a polygenic risk score on COPD and related outcomes: COPDGene and ECLIPSE studies.
Moll Matthew et al. BMJ open respiratory research 2020 Nov 7(1) - Clinical and laboratory data, radiological structured report findings and quantitative evaluation of lung involvement on baseline chest CT in COVID-19 patients to predict prognosis.
Salvatore Cappabianca et al. La Radiologia medica 2020 Oct - Chronic obstructive pulmonary disease and related phenotypes: polygenic risk scores in population-based and case-control cohorts.
Moll Matthew et al. The Lancet. Respiratory medicine 2020 8(7) 696-708 - Machine Learning/Deep Neuronal Network: Routine Application in Chest Computed Tomography and Workflow Considerations.
Fischer Andreas M et al. Journal of thoracic imaging 2020 May 35 Suppl 1S21-S27 - New Patient-Centric Approaches to the Management of Alpha-1 Antitrypsin Deficiency.
Chorostowska-Wynimko Joanna et al. International journal of chronic obstructive pulmonary disease 2020 15345-355 - Deep learning for screening of interstitial lung disease patterns in high-resolution CT images.
Agarwala S et al. Clinical radiology 2020 Feb - Deep Learning Enables Automatic Classification of Emphysema Pattern at CT.
Humphries Stephen M et al. Radiology 2019 Dec 191022 - Improving the geographical precision of rural chronic disease surveillance by using emergency claims data: a cross-sectional comparison of survey versus claims data in Sullivan County, New York.
Lee David C et al. BMJ open 2019 Nov 9(11) e033373
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About HLBS-PopOmics
HLBS-PopOmics is an online, continuously updated, searchable database of published scientific literature, CDC and NIH resources, and other materials that address the translation of genomic and other precision health discoveries into improved health care and prevention related to Heart and Vascular Diseases(H), Lung Diseases(L), Blood Diseases(B), and Sleep Disorders(S)...more
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Mensah GA, Yu W, Barfield WL, Clyne M, Engelgau MM, Khoury MJ. HLBS-PopOmics: an online knowledge base to accelerate dissemination and implementation of research advances in population genomics to reduce the burden of heart, lung, blood, and sleep disorders. Genet Med. 2018 Sep 10. doi: 10.1038/s41436-018-0118-1
Disclaimer: Articles listed in the Public Health Knowledge Base are selected by Public Health Genomics Branch to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
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- Page last updated:Apr 25, 2024
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