Tuberculosis (TB) is still considered a leading cause of death and a substantial threat to global child health. Both TB infection and disease are curable using antibiotics. However, most children who die of TB are never diagnosed or treated. In clinical practice, experienced physicians assess TB by examining chest X-rays (CXR). Pediatric CXR has specific challenges compared to adult CXR, which makes TB diagnosis in children more difficult. Computer-aided diagnosis systems supported by Artificial Intelligence have shown performance comparable to experienced radiologist TB readings, which could ease mass TB screening and reduce clinical burden. We propose a multi-view deep learning-based solution which, by following a proposed template, aims to automatically regionalize and extract lung and mediastinal regions of interest from pediatric CXR images where key TB findings may be present. Experimental results have shown accurate region extraction, which can be used for further analysis to confirm TB finding presence and severity assessment.
A relevant percentage of COVID-19 patients present bilateral pneumonia. Disease progression and healing is characterized by the presence of different parenchymal lesion patterns. Artificial intelligence algorithms have been developed to identify and assess the related lesions and properly segment affected lungs, however very little attention has been paid to automatic lesion subtyping. In this work we present artificial intelligence algorithms based on CNN to automatically identify and quantify COVID-19 pneumonia patterns. A Dense-efficient CNN architecture is presented to automatically segment the different lesion subtypes. The proposed technique has been independently tested in a multicentric cohort of 100 patients, showing Dice coefficients of 0.988±0.01 for ground glass opacities, 0.948±0.05 for consolidations, and 0.999±0.0003 for healthy tissue with respect to radiologist’s reference segmentations, and high correlations with respect to radiologist severity visual scores.
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