A survey on artificial intelligence in pulmonary imaging

Author:

Saha Punam K.12ORCID,Nadeem Syed Ahmed2ORCID,Comellas Alejandro P.3ORCID

Affiliation:

1. Department of Electrical and Computer Engineering University of Iowa Iowa City Iowa USA

2. Department of Radiology University of Iowa Iowa City Iowa USA

3. Department of Internal Medicine University of Iowa Iowa City Iowa USA

Abstract

AbstractOver 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.This article is categorized under: Application Areas > Health Care Technologies > Artificial Intelligence Technologies > Computational Intelligence Application Areas > Science and Technology

Funder

National Institutes of Health

Publisher

Wiley

Subject

General Computer Science

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