An Intelligent Melanoma Diagnostic Training System

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DESCRIPTION (provided by applicant): This project will determine whether the well-described paradigm of the model-tracing Intelligent Tutoring System can be adapted to create a multimedia, knowledge-based, medical training system. We propose to develop the Melanoma Diagnostic Training System, a virtual-slide based tutor for pathology residents. The system is designed to provide instruction in detection, classification, and reporting of Malignant Melanoma and other melanocytic skin lesions. Melanocytic lesions are a difficult area of histologic cancer diagnosis. False negative and false positive diagnoses of Melanoma can result in significant morbidity and mortality, and are among the most commonly litigated pathology case types. New advances in treatment of Melanoma have placed an increasing responsibility on the pathologist to identify and report on a range of histologic prognostic indicators. We propose to develop a diagnostic training system in this domain using the paradigm of the Intelligent Tutoring Systems (ITS). ITS are computer-based systems that provide individualized instruction by incorporating models of expert performance and dynamically building a unique student model for each user. ITS can be highly effective in systems that simulate real-world tasks, enabling students to work through case-based scenarios as the ITS offers guidance, points out errors and organizes the curriculum to address the needs of that individual learner. As part of the project, we will develop a library of whole-slide digital images of melanocytic lesions and melanoma, each with a gold-standard diagnosis. System development will be accompanied by a controlled, randomized laboratory evaluation in which we will examine the effect of the system on accuracy of detection, classification, and reporting using pre-test and post-test methods. In the final year of the project, we will deploy the system across multiple sites in the Pennsylvania Cancer Alliance Bioinformaties Consortium, and evaluate acceptance and use of the system using surveys, interviews, and log-file analysis.