Computer-aided diagnosis of pneumonia in patients with chronic obstructive pulmonary disease

Abstract

Background: Early diagnosis of pneumonia and discrimination between this disease and chronic obstructive pulmonary disease (COPD) exacerbations in patients with COPD are crucial for optimal clinical management and treatment. Objectives: To examine the use of computerized analysis of respiratory sounds, a hybrid system based on principal component analysis (PCA) and probabilistic neural networks (PNNs), to aid the detection of coexisting pneumonia in patients with COPD. Methods and materials: A convenience sample of 58 patients with COPD (25 patients hospitalized for community-acquired pneumonia and 33 owing to acute exacerbation of COPD) was studied. Auscultations were performed by the patients themselves on their suprasternal notch. Short-time Fourier transform analysis was used to extract features from the recorded respiratory sounds, PCA was selected for dimensionality reduction and a PNN was trained as classifier. 10-Fold cross-validation and receiver operating characteristic curve analysis were used to estimate the system performance. Results: Based on the cross-validation results, a sensitivity and a specificity of 72% and 81.8%, respectively, were achieved in validation data. The operating point was selected to maximize the specificity and sensitivity pair in the training set. Discussion: The results strongly suggest that electronic self-auscultation at a single location (suprasternal notch) can support diagnosis of pneumonia in patients with COPD. Conclusions: A simple, cost-effective method has been proposed to aid decision-making in areas with no radiological facilities available and in resource-constrained settings, and could have a great diagnostic impact on telemedicine applications.