Abstract：
RATIONALE AND OBJECTIVES:Intravenous thrombolysis decision-making and obtaining of consent would be assisted by an individualized risk-benefit ratio. Deep learning (DL) models may be able to assist with this patient selection. MATERIALS AND METHODS:Clinical data regarding consecutive patients who received intravenous thrombolysis across two tertiary hospitals over a 7-year period were extracted from existing databases. The noncontrast computed tomography brain scans for these patients were then retrieved with hospital picture archiving and communication systems. Using a combination of convolutional neural networks (CNN) and artificial neural networks (ANN) several models were developed to predict either improvement in the National Institutes of Health Stroke Scale of ≥4 points at 24 hours ("NIHSS24"), or modified Rankin Scale 0-1 at 90 days ("mRS90"). The developed CNN and ANN were then applied to a test set. The THRIVE, HIAT, and SPAN-100 scores were also calculated for the patients in the test set and used to predict NIHSS24 and mRS90. RESULTS:Data from 204 individuals were included in the project. The best performing DL model for prediction of mRS90 was a combination CNN + ANN based on clinical data and computed tomography brain (accuracy = 0.74, F1 score = 0.69). The best performing model for NIHSS24 prediction was also the combination CNN + ANN (accuracy = 0.71, F1 score = 0.74). CONCLUSION:DL models may aid in the prediction of functional thrombolysis outcomes. Further investigation with larger datasets and additional imaging sequences is indicated.
journal_name
Acad Radioljournal_title
Academic radiologyauthors
Bacchi S,Zerner T,Oakden-Rayner L,Kleinig T,Patel S,Jannes Jdoi
10.1016/j.acra.2019.03.015subject
Has Abstractpub_date
2020-02-01 00:00:00pages
e19-e23issue
2eissn
1076-6332issn
1878-4046pii
S1076-6332(19)30174-6journal_volume
27pub_type
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