User modeling for improved computer-aided training in radiology: Initial experience

Although mammography is an efficient screening modality for breast cancer, interpretation of mammographic images is a difficult task and notable variability between radiologists performance has been documented. A significant factor impacting radiologists diagnostic performance is adequate training. In this study we propose a new paradigm for computer-assisted training in radiology based on constructing user models for radiologists-in-training that capture individual error making patterns. Such user models are developed and trained to use image features for prediction of the extent of error made by a particular radiologist for variety of cases and therefore estimate difficulty of different types of cases for that radiologist. The constructed user model can be used to develop a personalized training protocol for the radiologist-in-training that focuses on cases that may pose a particular difficulty to the trainee. We initially demonstrate the concept of building individual user models for the task of breast mass diagnosis. Data collected from three resident observers at Duke University was used for the experiments. The result indicate that the proposed models are capable of learning to distinguish difficult and easy cases for each observer with moderate accuracy which shows promise for the proposed concept.