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Bayesian Network Interface for Assisting Radiology Interpretation and Education

机译：贝叶斯网络接口，协助放射学解释和教育

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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 arc 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.
机译：在这项工作中，我们介绍了在临床解释过程中使用贝叶斯网络进行放射科医生决策支持。这种计算方法的优点是避免了由于已知的人类认知偏差（例如锚定偏差，成帧效应，可用性偏差和过早关闭）而导致的错误诊断。为了将贝叶斯网络整合到临床实践中，我们开发了一个开放源代码的Web应用程序，该应用程序为各种放射线疾病实体（例如，基底神经节疾病，骨病变）提供了诊断支持。临床工具为用户提供了一组代表感兴趣的临床和影像特征的按钮。这些按钮用于设置每个观察到的特征的值。识别特征后，将实时更新每个可能诊断的条件概率。另外，使用灵敏度分析，可以将界面设置为通知用户哪些剩余的成像功能可提供最大的区分性信息以选择最可能的诊断。案例提交工具允许用户将经过验证的案例和相关的影像特征提交到数据库，然后可将其用于将来的贝叶斯网络调整/测试。然后由指定的专家使用提供的质量控制工具对这些提交的案例进行审查。研究工具为用户提供具有先前标记特征和选定诊断的病例，以进行绩效评估。同样，“教育”页面显示具有已知特征的案例，但提供有关特征选择的实时反馈。

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