A BAYESIAN NETWORK MODEL FOR RADIOLOGICAL-DIAGNOSIS AND PROCEDURE SELECTION - WORK-UP OF SUSPECTED GALLBLADDER-DISEASE

Bayesian networks, a technique for reasoning under uncertainty, curren tly are being developed for application to medical decision making. To explore their usefulness for radiologic decision support, a Bayesian belief network was constructed in the domain of hepatobiliary disease. The network model's nodes represent diagnoses, physical findings, lab oratory test results, and imaging study findings. The connections betw een nodes incorporate conditional probabilities, such as sensitivity a nd specificity, to represent probabilistic influences. Statistical dat a were abstracted from peer-reviewed journal articles on hepatobiliary disease, and a network was created to reflect the data. The network s uccessfully determined the a priori probabilities of various diseases, and incorporated laboratory and imaging results to calculate the a po steriori probabilities. The most informative examination was identifie d, that is, the laboratory study or imaging procedure that led to the greatest diagnostic certainty. Bayesian networks represent a very prom ising technique for decision support in radiology: they can assist phy sicians in formulating diagnoses and in selecting imaging procedures.