AI Article Synopsis
- The prediction of tissue survival in acute ischemic stroke treatment is crucial for balancing risks and benefits of interventions like clot retrieval.
- A new deep learning model analyzes local patches from MRI hypoperfusion features to predict tissue fate right after stroke symptoms begin.
- Testing on 19 patients demonstrates that this model outperforms traditional single-voxel regression methods, leading to more accurate predictions.
Article Abstract
In acute ischemic stroke treatment, prediction of tissue survival outcome plays a fundamental role in the clinical decision-making process, as it can be used to assess the balance of risk vs. possible benefit when considering endovascular clot-retrieval intervention. For the first time, we construct a deep learning model of tissue fate based on randomly sampled local patches from the hypoperfusion (Tmax) feature observed in MRI immediately after symptom onset. We evaluate the model with respect to the ground truth established by an expert neurologist four days after intervention. Experiments on 19 acute stroke patients evaluated the accuracy of the model in predicting tissue fate. Results show the superiority of the proposed regional learning framework versus a single-voxel-based regression model.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5597003 | PMC |
| http://dx.doi.org/10.1109/BIBM.2015.7359869 | DOI Listing |
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