Universidad Peruana Cayetano Heredia

Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition

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dc.contributor.author Correa, Malena
dc.contributor.author Zimic-Peralta, Mirko Juan
dc.contributor.author Barrientos, Franklin
dc.contributor.author Barrientos, Ronald
dc.contributor.author Román-Gonzalez, Avid
dc.contributor.author Pajuelo Travezaño, Monica Jhenny
dc.contributor.author Anticona, Cynthia
dc.contributor.author Mayta, Holger
dc.contributor.author Alva, Alicia
dc.contributor.author Solis-Vasquez, Leonardo
dc.contributor.author Figueroa, Dante Anibal
dc.contributor.author Chavez, Miguel A.
dc.contributor.author Lavarello, Roberto
dc.contributor.author Castañeda, Benjamin
dc.contributor.author Paz-Soldan Parlette, Valerie Andrea
dc.contributor.author Checkley, William
dc.contributor.author Gilman, Robert Hugh
dc.contributor.author Oberhelman, Richard
dc.date.accessioned 2019-03-05T15:23:30Z
dc.date.available 2019-03-05T15:23:30Z
dc.date.issued 2018
dc.identifier.uri https://hdl.handle.net/20.500.12866/5897
dc.description.abstract Pneumonia is one of the major causes of child mortality, yet with a timely diagnosis, it is usually curable with antibiotic therapy. In many developing regions, diagnosing pneumonia remains a challenge, due to shortages of medical resources. Lung ultrasound has proved to be a useful tool to detect lung consolidation as evidence of pneumonia. However, diagnosis of pneumonia by ultrasound has limitations: it is operator-dependent, and it needs to be carried out and interpreted by trained personnel. Pattern recognition and image analysis is a potential tool to enable automatic diagnosis of pneumonia consolidation without requiring an expert analyst. This paper presents a method for automatic classification of pneumonia using ultrasound imaging of the lungs and pattern recognition. The approach presented here is based on the analysis of brightness distribution patterns present in rectangular segments (here called "characteristic vectors") from the ultrasound digital images. In a first step we identified and eliminated the skin and subcutaneous tissue (fat and muscle) in lung ultrasound frames, and the "characteristic vectors"were analyzed using standard neural networks using artificial intelligence methods. We analyzed 60 lung ultrasound frames corresponding to 21 children under age 5 years (15 children with confirmed pneumonia by clinical examination and X-rays, and 6 children with no pulmonary disease) from a hospital based population in Lima, Peru. Lung ultrasound images were obtained using an Ultrasonix ultrasound device. A total of 1450 positive (pneumonia) and 1605 negative (normal lung) vectors were analyzed with standard neural networks, and used to create an algorithm to differentiate lung infiltrates from healthy lung. A neural network was trained using the algorithm and it was able to correctly identify pneumonia infiltrates, with 90.9% sensitivity and 100% specificity. This approach may be used to develop operator-independent computer algorithms for pneumonia diagnosis using ultrasound in young children. en_US
dc.language.iso eng
dc.publisher Public Library of Science
dc.relation.ispartofseries PLoS ONE
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject Article en_US
dc.subject artificial neural network en_US
dc.subject automation en_US
dc.subject child en_US
dc.subject clinical article en_US
dc.subject controlled study en_US
dc.subject digital imaging en_US
dc.subject disease classification en_US
dc.subject echography en_US
dc.subject female en_US
dc.subject human en_US
dc.subject image analysis en_US
dc.subject image processing en_US
dc.subject infant en_US
dc.subject lung infiltrate en_US
dc.subject male en_US
dc.subject Peru en_US
dc.subject pneumonia en_US
dc.subject sensitivity and specificity en_US
dc.subject thorax radiography en_US
dc.title Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition en_US
dc.type info:eu-repo/semantics/article
dc.identifier.doi https://doi.org/10.1371/journal.pone.0206410
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#3.02.03
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#3.02.07
dc.relation.issn 1932-6203


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