Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition

• Autores: Correa M., Zimic M., Barrientos F., Barrientos R., Román-Gonzalez A., Pajuelo M.J., Anticona C., Mayta H., Alva A., Solis-Vasquez L., Figueroa D.A., Chavez M.A., Lavarello R., Castañeda B., Paz-Soldán V.A., Checkley W., Gilman R.H., Oberhelman R.
• Formato: artículo
• Fecha de Publicación: 2018
• Institución: Consejo Nacional de Ciencia Tecnología e Innovación
• Repositorio: CONCYTEC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.concytec.gob.pe:20.500.12390/534
• Enlace del recurso: https://hdl.handle.net/20.500.12390/534; https://doi.org/10.1371/journal.pone.0206410
• Nivel de acceso: acceso abierto
• Materia: male; Article; artificial neural network; automation; child; clinical article; controlled study; digital imaging; disease classification; echography; female; human; image analysis; image processing; infant; lung infiltrate; https://purl.org/pe-repo/ocde/ford#3.02.00

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.