Pulmonary Function Tests
What's New
Last Posted: Apr 25, 2023
- Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation.
Nilakash Das et al. Eur Respir J - Detection and staging of chronic obstructive pulmonary disease using a computed tomography-based weakly supervised deep learning approach.
Sun Jiaxing et al. European radiology 2022 - A Machine Learning Application to Predict Early Lung Involvement in Scleroderma: A Feasibility Evaluation.
Murdaca Giuseppe et al. Diagnostics (Basel, Switzerland) 2021 11(10) - Machine learning for lung texture analysis on thin-section CT: Capability for assessments of disease severity and therapeutic effect for connective tissue disease patients in comparison with expert panel evaluations.
Ohno Yoshiharu et al. Acta radiologica (Stockholm, Sweden : 1987) 2021 2841851211044973 - Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?
Giri Paresh C et al. Frontiers in physiology 2021 12678540 - Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis.
Kaplan Alan et al. The journal of allergy and clinical immunology. In practice 2021 - Novel machine learning can predict acute asthma exacerbation.
Zein Joe G et al. Chest 2021 Jan - Expert artificial intelligence-based natural language processing characterises childhood asthma.
Seol Hee Yun et al. BMJ open respiratory research 2020 Feb 7(1) - Elastic Registration-driven Deep Learning for Longitudinal Assessment of Systemic Sclerosis Interstitial Lung Disease at CT.
Chassagnon Guillaume et al. Radiology 2020 Oct 200319 - Does an mHealth system reduce health service use for asthma?
To Teresa et al. ERJ open research 2020 Jul 6(3) - The value of preoperative spirometry testing for predicting postoperative risk in upper abdominal and thoracic surgery assessed using big-data analysis.
Park Hyung Jun et al. Journal of thoracic disease 2020 Aug 12(8) 4157-4167 - Phenotype of children with inconclusive cystic fibrosis diagnosis after newborn screening.
Munck Anne et al. Pediatric pulmonology 2020 Jan - Deep Learning Enables Automatic Classification of Emphysema Pattern at CT.
Humphries Stephen M et al. Radiology 2019 Dec 191022 - B 2 adrenergic receptor gene polymorphism effect on childhood asthma severity and response to treatment.
Alghobashy Ashgan Abdallah et al. Pediatric research 2018 83(3) 597-605 - Identifying Alpha-1 Antitrypsin Deficiency Based on Computed Tomography Evidence of Emphysema.
Miskoff Jeffrey A et al. Cureus 2019 Jan 11(1) e3971 - Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests.
Topalovic Marko et al. The European respiratory journal 2019 Feb - CLINGEN Actionability Report for Alpha-1 Antitrypsin Deficiency - SERPINA1
ClinGen Actionability Working Group - CLINGEN Actionability Report for Emery-Dreifuss Muscular Dystrophy (AD, XL) - LMNA, EMD, FHL1
ClinGen Actionability Working Group - CLINGEN Actionability Report for Myofibrillar Myopathy -DES, BAG3, FLNC
ClinGen Actionability Working Group - Lung Transplant
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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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