Artificial Intelligence Analysis of Initial Scan Evolution of Traumatic Brain Injured Patient to Predict Neurological Outcome

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Clinical Phase

NA

Total Enrollment

30 participants

Study Classification

INTERVENTIONAL

Study Start Date

2020-01-01

Study Completion Date

2021-10-12

Brief Summary

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We assume that an early iterative automatic CT scan analysis (D0, D1 and D3) by different AI approaches will allow an early differentiation of the tissues evolution after TBI. Our objective is to couple theses scan profiles to a neurological evolution, measured by therapeutic intensity.

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Detailed Description

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Traumatic brain injury is a common and serious pathology, responsible of an important morbi-mortality. The TBI can be consider as a complex set of nosological entities of different evolution with difficult early identification whereas the main issue of this pathology depends on prevention and management of the lesions caused by the initial cerebral aggression.

Different evolutionary profiles seems to exist and sometimes coexists: edema evolution, hemorrhagic transformation and/or cerebrospinal fluid (CSF) resorption issues with hydrocephalus apparition.

Currently, there is no Imaging methods that can be used in every day clinical management that allows a visualization, quantification and prediction of these different lesional evolutions

CT scan is the reference imaging method for TBI patient monitoring. It allows a lesion description, a therapeutic adaptation and an evaluation of the prognostic.

Even if it is used as a routine examination, the analysis of cerebral scanners remains manual and a non-quantitative one, which make a little informative analysis as far as lesions evolution is concerned.

Recently it has been established the automatic MRI analysis with AI approach allows:

1. \- To show aspects of images that can't be seen to the naked eye
2. \- To automatically segment and quantify the different tissues (edema, hemorrhage...). First tests on this kind of analysis on CT scans shows that this technology can be transferred from MRI to CT scans and more importantly it brings out new quantitative informations on cerebral lesions evolution.

Conditions

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Trauma, Brain

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

PREVENTION

Blinding Strategy

NONE

Study Groups

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CT scan

During this study each patient will have 3 CT scans : D0, D1 and D3. A daily follow up during first seven days in ICU, then a follow up at D28 if still in hospital, and a phone call at M6 for neurological outcome

Group Type EXPERIMENTAL

CT scan

Intervention Type RADIATION

3 ct scans : D0, D1 and D3

Interventions

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CT scan

3 ct scans : D0, D1 and D3

Intervention Type RADIATION

Eligibility Criteria

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Inclusion Criteria

* Age \> or = 18 years old
* Closed TBI
* Primary admission in Grenoble University Hospital
* Initial CT scan with visible cerebral lesion rated at least 3 on abbreviated injury score (AIS)
* In ICU for an expected length of 48 hours
* Social security system affiliation

Exclusion Criteria

* Life expectation \<48 hours
* In ICU for more than 24h
* Transferred from another hospital
* Patients corresponding to articles L1121-5, L1121-6, L1121-7, L1121-8 (under legal protection) of French Public Health Code
* Patient in exclusion time of another study

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No