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, 13 (12), e0206410
eCollection

Automatic Classification of Pediatric Pneumonia Based on Lung Ultrasound Pattern Recognition

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Automatic Classification of Pediatric Pneumonia Based on Lung Ultrasound Pattern Recognition

Malena Correa et al. PLoS One.

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.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Ultrasound of the lung with the echogenic pleural line and the horizontal artifacts “A lines“.
Fig 2
Fig 2
(A) Ultrasound of the lung, (B) Segmentation of (A) in vectors.
Fig 3
Fig 3
(A) Selection of the analysis zone. (B) Application of the skin filter (segmentation of the area above the pleural line).
Fig 4
Fig 4. Examples of vectors in specific regions used to compute brightness profiles: healthy (green), rib-bone (blue), and pneumonia (red).
Fig 5
Fig 5. Automatic identification of the pleural line by the algorithm and removal of the skin.
(A) ultrasound image of a healthy lung where the pleura is identified between two rib-bones (blue arrow), (B) ultrasound image of the lung where the soft tissues were removed and with evidence of infiltrate (red arrow).
Fig 6
Fig 6. Brightness profile of an example vector with: Healthy lung (green), pneumonia (red) and rib-bone (black).
Fig 7
Fig 7. Sensitivity and Specificity in function of number of neurons in the hidden layer of the artificial neural network.

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