Is Higher Viral Load in the Upper Respiratory Tract Associated With Severe Pneumonia? Findings From the PERCH Study.

BACKGROUND.: The etiologic inference of identifying a pathogen in the upper respiratory tract (URT) of children with pneumonia is unclear. To determine if viral load could provide evidence of causality of pneumonia, we compared viral load in the URT of children with World Health Organization-defined severe and very severe pneumonia and age-matched community controls. METHODS.: In the 9 developing country sites, nasopharyngeal/oropharyngeal swabs from children with and without pneumonia were tested using quantitative real-time polymerase chain reaction for 17 viruses. The association of viral load with case status was evaluated using logistic regression. Receiver operating characteristic (ROC) curves were constructed to determine optimal discriminatory viral load cutoffs. Viral load density distributions were plotted. RESULTS.: The mean viral load was higher in cases than controls for 7 viruses. However, there was substantial overlap in viral load distribution of cases and controls for all viruses. ROC curves to determine the optimal viral load cutoff produced an area under the curve of <0.80 for all viruses, suggesting poor to fair discrimination between cases and controls. Fatal and very severe pneumonia cases did not have higher viral load than less severe cases for most viruses. CONCLUSIONS.: Although we found higher viral loads among pneumonia cases than controls for some viruses, the utility in using viral load of URT specimens to define viral pneumonia was equivocal. Our analysis was limited by lack of a gold standard for viral pneumonia

Deep Learning for Automatic Pneumonia Detection

Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. The pneumonia detection is usually performed through examine of chest X-ray radiograph by highly-trained specialists. This process is tedious and often leads to a disagreement between radiologists. Computer-aided diagnosis systems showed the potential for improving diagnostic accuracy. In this work, we develop the computational approach for pneumonia regions detection based on single-shot detectors, squeeze-and-excitation deep convolution neural networks, augmentations and multi-task learning. The proposed approach was evaluated in the context of the Radiological Society of North America Pneumonia Detection Challenge, achieving one of the best results in the challenge.Comment: to appear in CVPR 2020 Workshops proceeding

Ensembles of Convolutional Neural Networks models for pediatric pneumonia diagnosis

Pneumonia is a lung infection that causes 15% of childhood mortality, over 800,000 children under five every year, all over the world. This pathology is mainly caused by viruses or bacteria. X-rays imaging analysis is one of the most used methods for pneumonia diagnosis. These clinical images can be analyzed using machine learning methods such as convolutional neural networks (CNN), which learn to extract critical features for the classification. However, the usability of these systems is limited in medicine due to the lack of interpretability, because of these models cannot be used to generate an understandable explanation (from a human-based perspective), about how they have reached those results. Another problem that difficults the impact of this technology is the limited amount of labeled data in many medicine domains. The main contributions of this work are two fold: the first one is the design of a new explainable artificial intelligence (XAI) technique based on combining the individual heatmaps obtained from each model in the ensemble. This allows to overcome the explainability and interpretability problems of the CNN "black boxes", highlighting those areas of the image which are more relevant to generate the classification. The second one is the development of new ensemble deep learning models to classify chest X-rays that allow highly competitive results using small datasets for training. We tested our ensemble model using a small dataset of pediatric X-rays (950 samples) with low quality and anatomical variability (which represents one of the biggest challenges). We also tested other strategies such as single CNNs trained from scratch and transfer learning using CheXNet. Our results show that our ensemble model outperforms these strategies obtaining highly competitive results. Finally, we confirmed the robustness of our approach using another pneumonia diagnosis dataset [1]

Low specificity of the bacterial index for the diagnosis of bacterial pneumonia by bronchoalveolar lavage

The bacterial index (BI) as defined by the sum of log10 colony-forming units (cfu) of microorganisms per milliliter of bronchoalveolar lavage (BAL) fluid, i.e., a multiplication of the single cfu/ml, has been used to distinguish between polymicrobial pneumonia (BI≥5) and colonization (BI<5). Since many false-positive results are to be expected using this parameter, the diagnostic value of the BI was studied prospectively by obtaining bacteriologic cultures of BAL fluid in 165 consecutive unselected patients. In 27 cases the diagnosis of bacterial pneumonia was established on clinical criteria. In 133 patients pneumonia could be excluded, and in five patients the diagnosis remained unclear. Using a cut-off of ≥105 cfu/ml BAL fluid, sensitivity and specificity for the diagnosis of pneumonia were 33% (9/27) and 99% (132/133), respectively. Sensitivity was mainly influenced by prior treatment with antibiotics, being 70% (7/10) in untreated and 12% (2/17) in treated patients. Applying the BI methodology at a cut-off of ≥ 5, however, resulted in an unacceptably high rate of 16 additional false-positive results, thus lowering the specificity to 87% (116/133;P<0.0001) while increasing the sensitivity to only 41% (11/27;P=0.77). In conclusion, given the high rate of false-positive results, the methodology of the BI is of doubtful value for the diagnosis of bacterial pneumonia by BAL in an unselected patient group. By applying the absolute number of cfu/ml BAL fluid, however, positive bacteriologic cultures of BAL fluid are highly specific for the diagnosis of pneumonia. Their sensitivity is limited by previous antibiotic therap

Is Higher Viral Load in the Upper Respiratory Tract Associated With Severe Pneumonia? Findings From the PERCH Study.

Background. The etiologic inference of identifying a pathogen in the upper respiratory tract (URT) of children with pneumonia is unclear. To determine if viral load could provide evidence of causality of pneumonia, we compared viral load in the URT of children with World Health Organization–defined severe and very severe pneumonia and age-matched community controls. Methods. In the 9 developing country sites, nasopharyngeal/oropharyngeal swabs from children with and without pneumonia were tested using quantitative real-time polymerase chain reaction for 17 viruses. The association of viral load with case status was evaluated using logistic regression. Receiver operating characteristic (ROC) curves were constructed to determine optimal discriminatory viral load cutoffs. Viral load density distributions were plotted. Results. The mean viral load was higher in cases than controls for 7 viruses. However, there was substantial overlap in viral load distribution of cases and controls for all viruses. ROC curves to determine the optimal viral load cutoff produced an area under the curve of \u3c0.80 for all viruses, suggesting poor to fair discrimination between cases and controls. Fatal and very severe pneumonia cases did not have higher viral load than less severe cases for most viruses. Conclusions. Although we found higher viral loads among pneumonia cases than controls for some viruses, the utility in using viral load of URT specimens to define viral pneumonia was equivocal. Our analysis was limited by lack of a gold standard for viral pneumonia