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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Related Experiment Video

Updated: Dec 16, 2025

An Investigation of the Effects of Sports-related Concussion in Youth Using Functional Magnetic Resonance Imaging and the Head Impact Telemetry System

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tbiExtractor: A framework for extracting traumatic brain injury common data elements from radiology reports.

Margaret Mahan¹, Daniel Rafter¹, Hannah Casey¹

¹Department of Biomedical Informatics and Computational Biology, University of Minnesota, Minneapolis, Minnesota, United States of America.

|July 2, 2020

Summary

Automating traumatic brain injury (TBI) data extraction from CT reports using tbiExtractor significantly improves data quality and consistency. This validated algorithm enhances research and clinical applications by providing faster, more accurate TBI information.

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Area of Science:

Medical Informatics
Natural Language Processing
Radiology

Background:

Manual data extraction from electronic medical records is inefficient and prone to errors.
Automating data extraction with standardized terminology can improve data quality for research and clinical use.
Developing a framework to extract traumatic brain injury (TBI) clinical conditions from computed tomography (CT) reports is crucial.

Purpose of the Study:

To develop and validate a framework for extracting TBI-related clinical findings from CT reports.
To improve the quality, consistency, and efficiency of data extraction for TBI research.
To create a tool that can assist in stratifying patients based on TBI severity.

Main Methods:

Developed tbiExtractor, an algorithm extending pyConTextNLP for TBI data extraction.
Utilized negation detection and contextual features within the algorithm.
Validated the algorithm against a gold standard of two physician annotators.

Main Results:

tbiExtractor achieved high sensitivity (0.92-0.94) and specificity (0.99).
The algorithm demonstrated 94.6% equivalence with human annotators and high accuracy (F1 Score ≥ 0.80 for 85% of findings).
Extraction time was significantly reduced (0.3 sec vs 1.7 min per report).

Conclusions:

tbiExtractor is a validated tool for extracting TBI data from radiology reports.
Automation by tbiExtractor enhances data consistency and reduces extraction time.
The tool can aid in stratifying subjects based on TBI-related findings in reports.