Physiology and coronary artery disease: emerging insights from computed tomography imaging based computational modeling

Improvements in spatial and temporal resolution now permit robust high quality characterization of presence, morphology and composition of coronary atherosclerosis in computed tomography (CT). These characteristics include high risk features such as large plaque volume, low CT attenuation, napkin-ring sign, spotty calcification and positive remodeling. Because of the high image quality, principles of patient-specific computational fluid dynamics modeling of blood flow through the coronary arteries can now be applied to CT and allow the calculation of local lesion-specific hemodynamics such as endothelial shear stress, fractional flow reserve and axial plaque stress. This review examines recent advances in coronary CT image-based computational modeling and discusses the opportunity to identify lesions at risk for rupture much earlier than today through the combination of anatomic and hemodynamic information

A Natural Language Processing Tool for Large-Scale Data Extraction from Echocardiography Reports

<div><p>Large volumes of data are continuously generated from clinical notes and diagnostic studies catalogued in electronic health records (EHRs). Echocardiography is one of the most commonly ordered diagnostic tests in cardiology. This study sought to explore the feasibility and reliability of using natural language processing (NLP) for large-scale and targeted extraction of multiple data elements from echocardiography reports. An NLP tool, EchoInfer, was developed to automatically extract data pertaining to cardiovascular structure and function from heterogeneously formatted echocardiographic data sources. EchoInfer was applied to echocardiography reports (2004 to 2013) available from 3 different on-going clinical research projects. EchoInfer analyzed 15,116 echocardiography reports from 1684 patients, and extracted 59 quantitative and 21 qualitative data elements per report. EchoInfer achieved a precision of 94.06%, a recall of 92.21%, and an F1-score of 93.12% across all 80 data elements in 50 reports. Physician review of 400 reports demonstrated that EchoInfer achieved a recall of 92–99.9% and a precision of >97% in four data elements, including three quantitative and one qualitative data element. Failure of EchoInfer to correctly identify or reject reported parameters was primarily related to non-standardized reporting of echocardiography data. EchoInfer provides a powerful and reliable NLP-based approach for the large-scale, targeted extraction of information from heterogeneous data sources. The use of EchoInfer may have implications for the clinical management and research analysis of patients undergoing echocardiographic evaluation.</p></div

Extraction of data elements and values into structured format from structured, semi-structured, and unstructured data from echocardiography reports.

<p>Extraction of data elements and values into structured format from structured, semi-structured, and unstructured data from echocardiography reports.</p

Examples of EchoInfer’s identification of data element and corresponding value structured output.

<p>Examples of EchoInfer’s identification of data element and corresponding value structured output.</p

Examples of non-standardized echocardiographic reporting that are not identified or extracted by EchoInfer.

<p>Examples of non-standardized echocardiographic reporting that are not identified or extracted by EchoInfer.</p

Precision and Recall for ten most frequent data elements identified in 15,116 echocardiograms.

<p>Precision and Recall for ten most frequent data elements identified in 15,116 echocardiograms.</p

Summary on precision and recall for 21 different random data elements validated on multiple data sets of echocardiographic reports.

<p>Summary on precision and recall for 21 different random data elements validated on multiple data sets of echocardiographic reports.</p

List of EchoInfer data elements targeted for extraction.

<p>List of EchoInfer data elements targeted for extraction.</p