a multicenter study on post-stroke functional outcome prediction

View the PDF file from the paper entitled Federal Learning for astonishment based on MRI: A multi -center study on predicting functional results after a stroke, by Vincent Roca and 10 other authors

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a summary:$ \ Textbf {Goal:} $ Different Age that has been activated in the brain (anxiety) is a biological sign of nervous imaging that reflects the health of the brain. However, the training of strong anxiety models requires large data sets, and often restricted with privacy concerns. This study evaluates the performance of federal learning (FL) to estimate the brain in the patients with isceive brain who was treated by removing mechanical thrombosis, and examines its association with clinical apparent patterns and functional results.

$ \ Textbf {Methods:} $ We used Flair brain images from 1674 stroke patients across 16 hospital center. We have implemented standard automatic learning models and deep learning for imaginations of imaginations under three data management strategies: central learning (collected data), Florida (local training in each site), and learning at one site. We have reported prediction and studying errors between brain risk factors and risk factors (for example, diabetes, high blood pressure, smoking), as well as functional results in three months after stroke. The logistical slope assessed the predictive value of anxiety of these results, adaptation to age, sex, vascular risk factors, stroke intensity, time between MRI and arterial hole, previous intravenous clotting, and re -reduction results.

$ \ Textbf {Results:} $ while central learning resulted in the most accurate predictions, FL has constantly outperformed one models. Wealth was much higher in patients with diabetes in all models. Comparisons between patients with good and weak functional results showed and multiple variables of these results are the importance of the relationship between recovery after recovery.

$ \ Textbf {Conclude:} $ FL provides accurate ages without decentralization of data. The strong link between imaginations, vascular risk factors and recovery after stroke highlights its sorrowful models in caring for stroke.