Bayesian network interface for assisting radiology interpretation and education

Jeffrey Duda; Emmanuel Botzolakis; Po-Hao Chen; Suyash Mohan; Ilya Nasrallah; Andreas Rauschecker; Jeffrey Rudie; R. Nick Bryan; James Gee; Tessa Cook

doi:10.1117/12.2293691

6 March 2018 Bayesian network interface for assisting radiology interpretation and education

Jeffrey Duda, Emmanuel Botzolakis, Po-Hao Chen, Suyash Mohan, Ilya Nasrallah, Andreas Rauschecker, Jeffrey Rudie, R. Nick Bryan, James Gee, Tessa Cook

Author Affiliations +

Proceedings Volume 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications; 105790S (2018) https://doi.org/10.1117/12.2293691
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

In this work, we present the use of Bayesian networks for radiologist decision support during clinical interpretation. This computational approach has the advantage of avoiding incorrect diagnoses that result from known human cognitive biases such as anchoring bias, framing effect, availability bias, and premature closure. To integrate Bayesian networks into clinical practice, we developed an open-source web application that provides diagnostic support for a variety of radiology disease entities (e.g., basal ganglia diseases, bone lesions). The Clinical tool presents the user with a set of buttons representing clinical and imaging features of interest. These buttons are used to set the value for each observed feature. As features are identified, the conditional probabilities for each possible diagnosis are updated in real time. Additionally, using sensitivity analysis, the interface may be set to inform the user which remaining imaging features provide maximum discriminatory information to choose the most likely diagnosis. The Case Submission tools allow the user to submit a validated case and the associated imaging features to a database, which can then be used for future tuning/testing of the Bayesian networks. These submitted cases are then reviewed by an assigned expert using the provided QC tool. The Research tool presents users with cases with previously labeled features and a chosen diagnosis, for the purpose of performance evaluation. Similarly, the Education page presents cases with known features, but provides real time feedback on feature selection.

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Jeffrey Duda, Emmanuel Botzolakis, Po-Hao Chen, Suyash Mohan, Ilya Nasrallah, Andreas Rauschecker, Jeffrey Rudie, R. Nick Bryan, James Gee, and Tessa Cook "Bayesian network interface for assisting radiology interpretation and education", Proc. SPIE 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, 105790S (6 March 2018); https://doi.org/10.1117/12.2293691

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