Academic Journal
Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion stroke
| العنوان: | Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion stroke |
|---|---|
| المؤلفون: | Jakob Sommer, Fiona Dierksen, Tal Zeevi, Anh Tuan Tran, Emily W. Avery, Adrian Mak, Ajay Malhotra, Charles C. Matouk, Guido J. Falcone, Victor Torres-Lopez, Sanjey Aneja, James Duncan, Lauren H. Sansing, Kevin N. Sheth, Seyedmehdi Payabvash |
| المصدر: | Frontiers in Artificial Intelligence, Vol 7 (2024) |
| بيانات النشر: | Frontiers Media S.A., 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | deep learning, stroke, thrombectomy, CT angiography, outcome, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | PurposeComputed Tomography Angiography (CTA) is the first line of imaging in the diagnosis of Large Vessel Occlusion (LVO) strokes. We trained and independently validated end-to-end automated deep learning pipelines to predict 3-month outcomes after anterior circulation LVO thrombectomy based on admission CTAs.MethodsWe split a dataset of 591 patients into training/cross-validation (n = 496) and independent test set (n = 95). We trained separate models for outcome prediction based on admission “CTA” images alone, “CTA + Treatment” (including time to thrombectomy and reperfusion success information), and “CTA + Treatment + Clinical” (including admission age, sex, and NIH stroke scale). A binary (favorable) outcome was defined based on a 3-month modified Rankin Scale ≤ 2. The model was trained on our dataset based on the pre-trained ResNet-50 3D Convolutional Neural Network (“MedicalNet”) and included CTA preprocessing steps.ResultsWe generated an ensemble model from the 5-fold cross-validation, and tested it in the independent test cohort, with receiver operating characteristic area under the curve (AUC, 95% confidence interval) of 70 (0.59–0.81) for “CTA,” 0.79 (0.70–0.89) for “CTA + Treatment,” and 0.86 (0.79–0.94) for “CTA + Treatment + Clinical” input models. A “Treatment + Clinical” logistic regression model achieved an AUC of 0.86 (0.79–0.93).ConclusionOur results show the feasibility of an end-to-end automated model to predict outcomes from admission and post-thrombectomy reperfusion success. Such a model can facilitate prognostication in telehealth transfer and when a thorough neurological exam is not feasible due to language barrier or pre-existing morbidities. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2624-8212 |
| Relation: | https://www.frontiersin.org/articles/10.3389/frai.2024.1369702/full; https://doaj.org/toc/2624-8212 |
| DOI: | 10.3389/frai.2024.1369702 |
| URL الوصول: | https://doaj.org/article/c14bcc3560d9493088e70665dce648f8 |
| رقم الانضمام: | edsdoj.14bcc3560d9493088e70665dce648f8 |
| قاعدة البيانات: | Directory of Open Access Journals |
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