Can computed tomography classifications of chronic obstructive pulmonary disease be identified using Bayesian networks and clinical data?

Abstract

Diagnosis and classification of chronic obstructive pulmonary disease (COPD) may be seen as difficult. Causal reasoning can be used to relate clinical measurements with radiological representation of COPD phenotypes airways disease and emphysema. In this paper a causal probabilistic network was constructed that uses clinically available measurements to classify patients suffering from COPD into the main phenotypes airways disease and emphysema. The network grades the severity of disease and for emphysematous COPD, the type of bullae and its location central or peripheral. In four patient cases the network was shown to reach the same conclusion as was gained from the patients’ High Resolution Computed Tomography (HRCT) scans. These were: airways disease, emphysema with central small bullae, emphysema with central large bullae, and emphysema with peripheral bullae. The approach may be promising in targeting HRCT in COPD patients, assessing phenotypes of the disease and monitoring its progression using clinical data.