Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition
| Autor: | Cynthia Anticona, Valerie A. Paz-Soldan, Franklin Barrientos, Benjamin Castaneda, Richard A. Oberhelman, Alicia Alva, Holger Mayta, Ronald Barrientos, Malena Correa, Roberto Lavarello, Miguel A. Chavez, Dante Figueroa, Robert H. Gilman, William Checkley, Avid Roman-Gonzalez, Monica J. Pajuelo, Leonardo Solis-Vasquez, Mirko Zimic |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Male
Pulmonology preschool child Diagnostic Radiology 030218 nuclear medicine & medical imaging 0302 clinical medicine Ultrasound Imaging Peru Medicine and Health Sciences Image Processing Computer-Assisted 030212 general & internal medicine Child Musculoskeletal System Lung Ultrasonography thorax radiography education.field_of_study child clinical article Multidisciplinary medicine.diagnostic_test Radiology and Imaging Applied Mathematics Simulation and Modeling Ultrasound Thorax Pulmonary Imaging 3. Good health medicine.anatomical_structure female classification Child Preschool Physical Sciences Pattern recognition (psychology) Pleurae Medicine Anatomy Algorithms Research Article Computer and Information Sciences Soft Tissues Imaging Techniques diagnostic imaging Science Population digital imaging disease classification Ribs Physical examination Research and Analysis Methods Article lung infiltrate lung 03 medical and health sciences male Diagnostic Medicine Artificial Intelligence image analysis medicine Humans pneumonia controlled study procedures human education Artificial Neural Networks Skeleton automation purl.org/pe-repo/ocde/ford#3.02.03 [https] Computational Neuroscience business.industry Biology and Life Sciences Computational Biology Infant echography Pattern recognition Pneumonia Neural Networks (Computer) medicine.disease infant Lung ultrasound image processing respiratory tract diseases Biological Tissue sensitivity and specificity purl.org/pe-repo/ocde/ford#3.02.07 [https] Neural Networks Computer Artificial intelligence business Mathematics Classification of pneumonia artificial neural network Neuroscience |
| Zdroj: | PLoS ONE, Vol 13, Iss 12, p e0206410 (2018) CONCYTEC-Institucional Consejo Nacional de Ciencia Tecnología e Innovación Tecnológica instacron:CONCYTEC PLoS ONE |
| ISSN: | 1932-6203 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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