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Automatic PredICtion of Edema After Stroke

2019-08-20 20:17:11 | BioPortfolio

Summary

To use machine learning for early detection of malignant brain edema in patients with MCA ischemia

Description

Malignant cerebral edema following large ischemic strokes account for up to 10% of all ischemic strokes. Mortality rates are high and most of the survivors are left severely disabled. Although decompressive craniectomy has been shown to significantly decrease mortality, high morbidity rates among survivors are reported. The optimal timepoint when neurosurgical decompression should be performed in the individual patient varies and is a subject of debate.

Early prediction of malignant brain edema to identify those patients who benefit from surgical treatment is a clinical challenge. The aim of this study is to use machine learning for comprehensive analysis of CT images as well as clinical data from 1500 patients with large ischemic MCA strokes in oder to develop a model for early prediction of malignant brain edema. In a first step algorithms automatically identify characteristic imaging features and clinical data of 1400 retrospective data sets to create a multistage model (learning phase). This is followed by a validation phase where the model is tested with 100 other retrospective data sets.

Study Design

Conditions

Stroke, Acute

Location

University Hospital Tuebingen
Tuebingen
Germany
72076

Status

Recruiting

Source

University Hospital Tuebingen

Results (where available)

View Results

Links

Published on BioPortfolio: 2019-08-20T20:17:11-0400

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Medical and Biotech [MESH] Definitions

Restoration of functions to the maximum degree possible in a person or persons suffering from a stroke.

Sudden death from overwork, most often as a result of acute CARDIOVASCULAR STROKE.

Stroke caused by lacunar infarction or other small vessel diseases of the brain. It features hemiparesis (see PARESIS), hemisensory, or hemisensory motor loss.

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