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Updated: Jul 5, 2026
A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia
Published on: September 16, 2017
Prediction of Major Stroke (NIHSS > 5) Based on MR Perfusion Imaging Using a 3D ResNet
Christoph C Kurmann1,2, Adnan Mujanovic3, Morin Beyeler4
1Department of Diagnostic and Interventional Neuroradiology, University Hospital of Bern, Bern, Switzerland. christoph.kurmann@insel.ch.
Purpose:
Prediction of stroke symptoms from perfusion patterns in ischemic stroke imaging would be desirable for decision making in relevant clinical scenarios as for example after incomplete mechanical thrombectomy or in perioperative strokes. Aim was to train a model to detect a major stroke defined as NIHSS > 5.
Methods:
Retrospective study of patients from the prospectively collected local Stroke Registry with large-/medium-vessel anterior circulation or without occlusion from 01/2015-12/2021 (N = 982, median age 74.5 (IQR 64.3-82), 47.5% female). Splits into training-set (80%), validation-set (10%) and temporally separated internal test-set (10%). Admission MR-perfusion Tmax-maps from 6 different MR-scanners were used as input for an 18-layer 3D-ResNet to predict major stroke (NIHSS > 5). Association of reported and predicted NIHSS > 5 with 90-days modified Rankin Scale (mRS) > 2 was tested using multivariate logistic regression.
Results:
Vessel occlusion was observed in 80% and NIHSS > 5 in 50% of all patients. On the internal test-set, Receiver Operating Characteristic area under the curve (ROC-AUC) was 0.87 (95%-CI ± 0.04) and Precision-Recall-AUC (PR-AUC) 0.85 (95%-CI ± 0.05). Accuracy was 78%, weighted F1-score 0.78. Including only vessel occlusions in the internal test-set, ROC-AUC was 0.81 (95%-CI ± 0.05) and PR-AUC 0.87 (95%-CI ± 0.05), accuracy was 73%, weighted F1-score 0.73. Reported NIHSS > 5 (aOR 2.03, 95%-CI 0.94-4.38, p = 0.07) and predicted NIHSS > 5 (aOR 3.61, 95%-CI 0.88-14.88, p = 0.08) both predicted 90-days mRS > 2, with similar model performance (BIC: 310.6 vs. 310.7; AIC: 252.4 vs. 252.5).
Conclusion:
Prediction of a major stroke (NIHSS > 5) from MR-perfusion Tmax-maps was feasible and performed well on our internal test set.
Insights
A deep learning model accurately predicted major stroke (NIHSS > 5) using MR-perfusion Tmax-maps. This tool aids clinical decision-making in acute ischemic stroke scenarios.
Area of Science:
- Neurology
- Radiology
- Artificial Intelligence in Medicine
Background:
- Clinical decision-making in ischemic stroke management, particularly after interventions like mechanical thrombectomy or in perioperative cases, benefits from accurate symptom prediction.
- Assessing stroke severity, defined by a National Institutes of Health Stroke Scale (NIHSS) score greater than 5, is crucial for patient outcomes.
Purpose of the Study:
- To develop and validate a deep learning model capable of predicting major stroke (NIHSS > 5) based on MR-perfusion imaging.
- To assess the model's performance in identifying patients with significant stroke deficits.
Main Methods:
- A retrospective analysis of 982 patients from a prospectively collected stroke registry (2015-2021) was conducted.
- An 18-layer 3D-ResNet model was trained using MR-perfusion Tmax-maps from multiple scanners to predict NIHSS > 5.
- The model's predictions were evaluated against reported NIHSS scores and their association with 90-day modified Rankin Scale (mRS) > 2 using logistic regression.
Main Results:
- The model achieved a Receiver Operating Characteristic area under the curve (ROC-AUC) of 0.87 and a Precision-Recall AUC (PR-AUC) of 0.85 on the internal test set.
- Accuracy was 78% with a weighted F1-score of 0.78 for predicting major stroke.
- Both reported and predicted NIHSS > 5 were associated with predicting a 90-day mRS > 2, indicating clinical relevance.
Conclusions:
- The developed deep learning model demonstrates feasibility and strong performance in predicting major stroke (NIHSS > 5) from MR-perfusion Tmax-maps.
- This AI-driven approach shows promise for enhancing clinical decision support in acute stroke care.
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