# Automated Decision Support System for Traumatic Brain Injury through Image Processing and Machine Learning Approaches

> **NIH NIH F31** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $32,086

## Abstract

Summary:
There is an urgent need for an automated decision support system for diagnosis and prognosis of traumatic
brain injuries (TBI). TBI is one of the leading causes of death in the modern world, and substantially contributes
to disability and impairment. The early detection of TBI and its proper management presents an unfilled need.
We therefore aim to supplement clinicians' decisions by developing a decision support system for monitoring
and integrating available information of a TBI patient for accurate and quantitative diagnosis and prognosis.
This project is the main component of a long-term goal of building a system that creates personalized
treatment plans. Specifically, we intend to automatically detect and accurately quantify two critical
abnormalities including shift in the brain's middle structure (Aim 1) and intracranial hemorrhage (Aim 2) from
computed tomography (CT) head scans. In Aim 1, we develop a model for delineating the spatial shift in brain
structure and its predictive power. We employ anatomical landmarks to detect a 3D deformed surface of the
brain midline after TBI. Such an approach allows us to quantify the shifted volume, a measurement that is not
currently achievable. Additionally, it provides accurate and timely access to conventional midline shift in a 2D
CT slice. In Aim 2, we build a model for delineating intracranial hemorrhage and its predictive power. We
implement a 3D convolutional neural network model to detect hemorrhagic regions and quantify and localize
their volume. Currently, these measurements are inaccurate and not readily available due to the cumbersome
manual process; instead a lesion's thickness in a 2D CT slice is used to assess its severity. In both Aim 1 and
2, we automatically calculate conventional and proposed volumetric and locational measurements and
compare them to suggest the best diagnostic metric for each abnormality. Finally, in Aim 3, we build an
automated pipeline for TBI severity assessment and outcome prediction. To this end, manual CT scan reads
will be integrated with patient-level information available from electronic health records to achieve accurate
data-driven diagnosis and prognosis. We implement machine learning approaches to build models capable of
predicting short and long-term clinical outcomes. Our prediction models will be developed independently of our
image processing algorithms. Upon achievement of Aims 1 and 2, automatically calculated information from
CT scans will be incorporated into machine learning models. The proposed research is significant, because it
is expected to advance TBI care, specifically within the “golden hour" post-injury. Ultimately, such a system
has the potential to reduce delayed and missed diagnosis, thereby reducing TBI morbidity and mortality.
Additionally, by preventing permanent and/or secondary injuries, and minimizing the time of hospitalization and
rehabilitation, our system will contribute to reducing the annual $76 billion burde...

## Key facts

- **NIH application ID:** 9989634
- **Project number:** 5F31LM012946-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Negar Farzaneh
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $32,086
- **Award type:** 5
- **Project period:** 2019-07-10 → 2021-04-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9989634

## Citation

> US National Institutes of Health, RePORTER application 9989634, Automated Decision Support System for Traumatic Brain Injury through Image Processing and Machine Learning Approaches (5F31LM012946-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9989634. Licensed CC0.

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