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.
In radiology, diagnostic errors occur either through the failure of detection or incorrect interpretation. Errors are
estimated to occur in 30-35% of all exams and contribute to 40-54% of medical malpractice litigations. In this
work, we focus on reducing incorrect interpretation of known imaging features.
Existing literature categorizes cognitive bias leading a radiologist to an incorrect diagnosis despite having correctly
recognized the abnormal imaging features: anchoring bias, framing effect, availability bias, and premature closure.
Computational methods make a unique contribution, as they do not exhibit the same cognitive biases as a human.
Bayesian networks formalize the diagnostic process. They modify pre-test diagnostic probabilities using clinical
and imaging features, arriving at a post-test probability for each possible diagnosis.
To translate Bayesian networks to clinical practice, we implemented an entirely web-based open-source software
tool. In this tool, the radiologist first selects a network of choice (e.g. basal ganglia). Then, large, clearly labeled
buttons displaying salient imaging features are displayed on the screen serving both as a checklist and for input. As
the radiologist inputs the value of an extracted imaging feature, the conditional probabilities of each possible
diagnosis are updated. The software presents its level of diagnostic discrimination using a Pareto distribution chart,
updated with each additional imaging feature.
Active collaboration with the clinical radiologist is a feasible approach to software design and leads to design
decisions closely coupling the complex mathematics of conditional probability in Bayesian networks with practice.
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