Journal article
A survey on artificial intelligence in pulmonary imaging
Wiley interdisciplinary reviews. Data mining and knowledge discovery, Vol.13(6), e1510
11/2023
DOI: 10.1002/widm.1510
PMCID: PMC10796150
PMID: 38249785
Appears in UI Libraries Support Open Access
Abstract
Over the last decade, deep learning (DL) has contributed to a paradigm shift in computer vision and image recognition creating widespread opportunities of using artificial intelligence in research as well as industrial applications. DL has been extensively studied in medical imaging applications, including those related to pulmonary diseases. Chronic obstructive pulmonary disease, asthma, lung cancer, pneumonia, and, more recently, COVID-19 are common lung diseases affecting nearly 7.4% of world population. Pulmonary imaging has been widely investigated toward improving our understanding of disease etiologies and early diagnosis and assessment of disease progression and clinical outcomes. DL has been broadly applied to solve various pulmonary image processing challenges including classification, recognition, registration, and segmentation. This article presents a survey of pulmonary diseases, roles of imaging in translational and clinical pulmonary research, and applications of different DL architectures and methods in pulmonary imaging with emphasis on DL-based segmentation of major pulmonary anatomies such as lung volumes, lung lobes, pulmonary vessels, and airways as well as thoracic musculoskeletal anatomies related to pulmonary diseases.
Details
- Title: Subtitle
- A survey on artificial intelligence in pulmonary imaging
- Creators
- Punam K. Saha - University of IowaSyed Ahmed Nadeem - University of IowaAlejandro P. Comellas - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Wiley interdisciplinary reviews. Data mining and knowledge discovery, Vol.13(6), e1510
- DOI
- 10.1002/widm.1510
- PMID
- 38249785
- PMCID
- PMC10796150
- NLM abbreviation
- Wiley Interdiscip Rev Data Min Knowl Discov
- ISSN
- 1942-4787
- eISSN
- 1942-4795
- Publisher
- Wiley
- Grant note
- DOI: 10.13039/100000002, name: National Institutes of Health, award: R01HL142042
- Language
- English
- Electronic publication date
- 07/07/2023
- Date published
- 11/2023
- Academic Unit
- Radiology; Electrical and Computer Engineering; Pulmonary, Critical Care, and Occupational Medicine; ICTS; Internal Medicine
- Record Identifier
- 9984443863102771
Metrics
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