Deep Learning in the Prediction of Ischaemic Stroke Thrombolysis Functional Outcomes

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. 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. 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. 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). DL models may aid in the prediction of functional thrombolysis outcomes. Further investigation with larger datasets and additional imaging sequences is indicated.