A quadratic model for combining quantitative diagnostic assessments from radiologist and computer in computer-aided...

Some computer-aided diagnosis (CAD) methods produce a quantitative diagnostic assessment (eg, likelihood of malignancy) based on computer image analysis that a radiologist who uses the computer aid must combine with his or her own assessment. Observer studies show that although CAD helps radiologists improve diagnostic performance, ad hoc use of computer aid can produce performance inferior to that of computer alone, indicating that radiologists are unable to incorporate computer assessment optimally into their final assessment. We describe a mathematical model for combining two correlated diagnostic assessments that may provide a basis for merging radiologists' ratings with computer assessments in a way that yields greater diagnostic accuracy than ad hoc merging by radiologists.

We calculate a likelihood ratio from the bivariate binormal model that describes joint probability density functions of latent decision variables of two correlated diagnostic assessments. To the extent that the bivariate binormal model is valid and that the model's parameters can be estimated reliably, results obtained in this way will be optimal because the likelihood ratio is the decision variable used by the ideal observer in any two-group classification task. We evaluated this method on two observer study datasets and in Monte Carlo simulations.

This method produced better performance than achieved by radiologists when they incorporated computer assessment in an ad hoc way. Simulations show that with a large number of cases, this method can produce results indistinguishable from the ideal observer performance.

This method can potentially help radiologists use quantitative computed diagnostic assessments optimally, thereby surpassing the computer in accuracy.