Multi-Level Classification of Lung Pathologies in Neonates using Recurrence Features.

The use of Lung Ultrasound (LUS) as a tool to diagnose and monitor lung diseases in neonates has increased in urban hospitals. LUS's main advantages compared to chest CT or X-rays is that it is less expensive, more accessible, and does not expose the patient to radiation. Performing LUS on neonates and diagnosing the LUS images require highly trained medical professional and clinicians. While availability of such specialists in general is not an issue in urban areas, there is lack of such personnel in rural and remote communities. Hence, an automated computer-aided screening approach as a first level diagnosis assistance in such scenarios might be of significant value. Many of the image morphologies used by clinicians in diagnosing the LUS have strong recurrence characteristics. Building upon this knowledge, in this paper, we propose a feature extraction method designed to quantify such recurrent features for classification of LUS images into 6 common neonatal lung conditions. These conditions were normal lung, chronic lung disease (CLD), transient tachypnea of the newborn (TTN), pneumothorax (PTX), respiratory distress syndrome (RDS), and consolidation (CON) that could be pneumonia or atelectasis. The proposed method extracts virtual scanlines from the LUS images and converts them into signals. Then using recurrence quantification analysis (RQA), features were extracted and fed to pattern classifiers. Using a simple linear classifier the proposed features can achieve a classification accuracy of 69.3% without clinical features and 77.6% with clinical features. Clinical Relevance- Development of an automated computer-aided screening tool for first level diagnosis assistance in neonatal LUS pathologies. Such a tool will be of significant value in rural and remote medical communities.