Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition

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.

Use of artificial intelligence in infectious diseases

“…This diagnosis depends on two factors: the expertise of the operator and the potential bias during interpretation by the medical personal. Using pattern recognition and image analysis was used for automatic classification of pneumonia (Correa et al, 2018). The neural network trained correctly identified pneumonia infiltrates (>90% sensitivity and 100% specificity).…”

Machine learning health-related applications in low-income and middle-income countries: a scoping review protocol

“…Examples of studies of interest include: (1) development of a 'deep learning-based visual evaluation algorithm' to early identify cervical cancer signs based on data from women in Costa Rica, 13 (2) classification of free-text (random forest) in emergency department records from nine hospitals in Nicaragua 14 and (3) automatic classification (neural networks) of paediatric pneumonia based on ultrasound records from children in Peru. 15 Distinguishing between ML applications and more conventional statistical methods could be challenging because in some cases the definitions are unclear, for instance, regression analysis. Nonetheless, from the context of the scientific paper, from the aims or overall methodological approach, it is possible to reckon whether a study uses ML techniques versus more conventional statistical methods.…”

Survey on deep learning for pulmonary medical imaging

“…Abdullah et al [120] proposed a detection method for the pneumonia symptoms by using the CNNs based on the difference of gray-scale color and the segmentation between normal and suspicious lung regions. Correa [121,130] introduced a method of automatic diagnosis of pneumonia by pulmonary ultrasound imaging. Different from Refs.…”