Deep learning on pre-procedural computed tomography and clinical data predicts outcome following stroke thrombectomy.
J Neurointerv Surg
; 2024 Mar 25.
Article
em En
| MEDLINE
| ID: mdl-38527795
ABSTRACT
BACKGROUND:
Deep learning using clinical and imaging data may improve pre-treatment prognostication in ischemic stroke patients undergoing endovascular thrombectomy (EVT).METHODS:
Deep learning models were trained and tested on baseline clinical and imaging (CT head and CT angiography) data to predict 3-month functional outcomes in stroke patients who underwent EVT. Classical machine learning models (logistic regression and random forest classifiers) were constructed to compare their performance with the deep learning models. An external validation dataset was used to validate the models. The MR PREDICTS prognostic tool was tested on the external validation set, and its performance was compared with the deep learning and classical machine learning models.RESULTS:
A total of 975 patients (550 men; mean±SD age 67.5±15.1 years) were studied with 778 patients in the model development cohort and 197 in the external validation cohort. The deep learning model trained on baseline CT and clinical data, and the logistic regression model (clinical data alone) demonstrated the strongest discriminative abilities for 3-month functional outcome and were comparable (AUC 0.811 vs 0.817, Q=0.82). Both models exhibited superior prognostic performance than the other deep learning (CT head alone, CT head, and CT angiography) and MR PREDICTS models (all Q<0.05).CONCLUSIONS:
The discriminative performance of deep learning for predicting functional independence was comparable to logistic regression. Future studies should focus on whether incorporating procedural and post-procedural data significantly improves model performance.
Texto completo:
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Base de dados:
MEDLINE
Idioma:
En
Revista:
J Neurointerv Surg
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Nova Zelândia