Optimal method for combining two correlated diagnostic assessments with application to computer-aided diagnosis

We are developing computer-aided diagnosis (CAD) methods that produce a quantitative diagnostic assessment, such as the likelihood of malignancy of a breast lesion. Radiologists who use this computer aid must combine the computer's quantitative assessment with their own. No theoretical or empirical methods are currently available to help radiologists perform this task. Results of recent observer studies show that while CAD helps radiologists improve performance, radiologists' ad hoc performance tends to be inferior to that of the computer alone, indicating that they are unable to use computer aids optimally. We have developed a general method to combine two correlated diagnostic assessments. We calculate a likelihood ratio based on a bivariate binormal model that describes the joint probability density of the latent decision variables from two diagnostic assessments. To the extent that the bivariate binormal model is valid and that the model's parameters can be estimated reliably, results that we obtain in this way will be optimal because that likelihood ratio is used by the ideal observer in combining the diagnostic assessments. Preliminary results indicate that this method can produce better performance than that achieved by radiologists when they use computer aids in an ad hoc way. This method can potentially help radiologists use quantitative computed diagnostic assessments optimally, thereby surpassing the computer in accuracy.