6 March 2018 Bayesian network interface for assisting radiology interpretation and education
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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.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jeffrey Duda, Jeffrey Duda, Emmanuel Botzolakis, Emmanuel Botzolakis, Po-Hao Chen, Po-Hao Chen, Suyash Mohan, Suyash Mohan, Ilya Nasrallah, Ilya Nasrallah, Andreas Rauschecker, Andreas Rauschecker, Jeffrey Rudie, Jeffrey Rudie, R. Nick Bryan, R. Nick Bryan, James Gee, James Gee, Tessa Cook, 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); doi: 10.1117/12.2293691; https://doi.org/10.1117/12.2293691
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