ObjectiveTo present a cognitive map to support the radiological diagnosis of solitary
bone tumors, as well as to facilitate the determination of the nature of the
tumor (benign or malignant), in pediatric patients.Materials and MethodsWe selected 28 primary lesions in pediatric patients, and we identified the
findings typically associated with each of the diagnoses. The method used
for the construction of the final cognitive map was the Bayesian belief
network model with backward chaining.ResultsWe developed a logical, sequential structure, in the form of a cognitive map,
based on the Bayesian belief network model, with the intention of simulating
the sequence of human thinking, in order to minimize the number of
unnecessary interventions and iatrogenic complications arising from the
incorrect evaluation of bone lesions.ConclusionWith this map, it will be possible to develop an application that will
provide support to physicians and residents, as well as contributing to
training in this area and consequently to a reduction in diagnostic errors
in patients with bone lesions.