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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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Published on: April 13, 2013

Outcome Prediction in Patients with Severe Traumatic Brain Injury Using Deep Learning from Head CT Scans.

Matthew Pease¹, Dooman Arefan¹, Jason Barber¹

¹From the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.).

|April 26, 2022

Summary

A new deep learning model combining head CT scans and clinical data accurately predicts long-term outcomes in severe traumatic brain injury (sTBI) patients. This AI-driven approach offers improved prognostication for comatose individuals, aiding clinical care decisions.

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Area of Science:

Neurology
Radiology
Artificial Intelligence in Medicine

Background:

Accurate long-term prognostication is crucial for acute clinical care following severe traumatic brain injury (sTBI).
Predicting outcomes in comatose sTBI patients remains a significant clinical challenge.

Purpose of the Study:

To develop and validate a prognostic model integrating deep learning analysis of head CT scans with clinical information.
The model aims to predict long-term patient outcomes (mortality and unfavorable outcomes) at 6 months post-sTBI.

Main Methods:

Retrospective analysis of 537 patients from a model-building set and 220 patients from an external cohort (TRACK-TBI study).
A convolutional neural network utilizing transfer and curriculum learning was applied to admission head CT scans.
A holistic fusion model combined deep learning imaging features with clinical data for outcome prediction.

Main Results:

The fusion model demonstrated superior performance over the IMPACT model for predicting mortality (AUC, 0.92 vs 0.80) and unfavorable outcomes (AUC, 0.88 vs 0.82) in internal testing.
On external validation (TRACK-TBI), the fusion model did not significantly differ from the IMPACT model for mortality prediction (AUC, 0.83).
Both imaging and fusion models underperformed the IMPACT model for predicting unfavorable outcomes in the external cohort.

Conclusions:

A deep learning model integrating head CT scans and clinical information shows promise for predicting 6-month outcomes after severe traumatic brain injury.
The developed fusion model outperformed neurosurgeon predictions, suggesting potential for AI in sTBI prognostication.