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Related Experiment Videos

PathBot: A Radiology-Pathology Correlation Dashboard.

Linda C Kelahan1, Amit D Kalaria2, Ross W Filice2,3

  • 1Department of Radiology, MedStar Georgetown University Hospital, 3800 Reservoir Road NW, Washington, DC, 20007, USA. Lkelahan2@gmail.com.

Journal of Digital Imaging
|April 5, 2017
PubMed
Summary
This summary is machine-generated.

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Radiologists can now automatically correlate pathology reports with their dictations using a new dashboard. This tool enhances self-education and peer review by linking diagnostic imaging with pathology findings.

Area of Science:

  • Medical Imaging and Diagnostics
  • Computational Pathology

Background:

  • Pathology is the gold standard in diagnostic medicine.
  • Radiology-pathology correlation is crucial but often hindered by practical constraints like time and electronic medical record complexity.

Purpose of the Study:

  • To develop an automated system for correlating radiology and pathology reports.
  • To create a user-friendly dashboard for presenting these correlations to radiologists.

Main Methods:

  • Utilized the RadLex ontology and NCBO Annotator to identify and map anatomic concepts.
  • Developed an algorithm to match pathology reports to corresponding radiology dictations for diagnostic imaging and image-guided procedures.
  • Presented matched reports via a web-based dashboard.
Keywords:
DashboardMedical records systemsRadLexRadiology teaching filesRadiology workflowRadiology-pathology correlation

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Main Results:

  • The developed algorithm demonstrated high specificity in detecting radiology-pathology matches.
  • Sensitivity was lower than anticipated, potentially due to limitations in the RadLex ontology and mapping algorithms.

Conclusions:

  • Automated radiology-pathology correlation via a dashboard can encourage pathology follow-up for self-education and peer review.
  • This tool can facilitate the creation of educational materials like teaching files, lectures, and publications.
  • Integrating pathology findings enhances the educational value of diagnostic images.