Expert system for automated bone age determination

Abstract

A novel automated bone age determination algorithm using left hand X-ray images, which provides consistent overall bone age as well as five part bone ages, is presented in this paper. Based on the descriptive narrative from the Greulich and Pyle atlas as well as those from other more recent studies, 17 region of interests are selected and based on anatomical similarity, five clinically relevant groupings (or “parts”) are defined on left hand X-ray images. When disharmonious maturations for different regions of interest are large, providing part bone ages with overall bone age is helpful to pediatricians. Based on interviews with two experts to get input on their bone age determination strategy, overall bone age determination can be viewed as the weighted sum of “part bone ages” of the five parts. Using the method of least squares and inputs from five (human) readers, we extract weights for bone age determination using all five parts (as well as reduced algorithms using only four, three or two parts). The weights indicate that part 1 (distal joints) has the highest priority. Overall bone age is then estimated based on the weights and bone ages of available parts. In our work, a computer vision algorithm provides bone ages of individual regions of interest. To combine the region of interest computer classifiers and generate each of the five part bone ages, we develop and analyze fusion rules of multiple classifiers with more than three classes each. The fusion rules take into account performance of each region of interest classifier. Once the part bone ages are obtained based on the fusion rule and region of interest classifiers, the overall bone age is determined in a fully automated way. Finally, we give a use case for the whole automated bone age determination system and validation of the algorithm based on given performance of each region of interest computer classifier. Results indicate that the algorithm works well with reasonably good classifiers.