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Incorporating intelligence into structured radiology reports

2014; SPIE; Volume: 9039; Linguagem: Inglês

10.1117/12.2043912

1996-756X

Charles E. Kahn,

Radiomics and Machine Learning in Medical Imaging

The new standard for radiology reporting templates being developed through the Integrating the Healthcare Enterprise (IHE) and DICOM organizations defines the storage and exchange of reporting templates as Hypertext Markup Language version 5 (HTML5) documents. The use of HTML5 enables the incorporation of "dynamic HTML," in which documents can be altered in response to their content. HTML5 documents can employ JavaScript, the HTML Document Object Model (DOM), and external web services to create intelligent reporting templates. Several reporting templates were created to demonstrate the use of scripts to perform in-template calculations and decision support. For example, a template for adrenal CT was created to compute contrast washout percentage from input values of precontrast, dynamic postcontrast, and delayed adrenal nodule attenuation values; the washout value can used to classify an adrenal nodule as a benign cortical adenoma. Dynamic templates were developed to compute volumes and apply diagnostic criteria, such as those for determination of internal carotid artery stenosis. Although reporting systems need not use a web browser to render the templates or their contents, the use of JavaScript creates innumerable opportunities to construct highly sophisticated HTML5 reporting templates. This report demonstrates the ability to incorporate dynamic content to enhance the use of radiology reporting templates.