Article Text
PDF
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.
- CT
- CT Angiography
- Stroke
- Thrombectomy
Data availability statement
Data are available upon reasonable request. The code for the final models are published open source on https://github.com/jdddog/deep-skull and https://github.com/jdddog/deep-mt. The pre-trained model weights and data that support the findings of this study are available from the corresponding author upon reasonable request.
Statistics from Altmetric.com
Data availability statement
Data are available upon reasonable request. The code for the final models are published open source on https://github.com/jdddog/deep-skull and https://github.com/jdddog/deep-mt. The pre-trained model weights and data that support the findings of this study are available from the corresponding author upon reasonable request.
Footnotes
Twitter @stoobsg, @BeharryJames
JPD and WKD contributed equally.
Contributors WD conceived the concept of the research. WD, AM, GPT, KG, JB, and JH collected the data. JPD and T-YC developed the models. MTMW performed statistical analysis. TW, DC, and PAB provided oversight of the study. WD drafted the manuscript. All authors contributed to the writing and intellectual content of the article. JPD and WD contributed equally to the work. PAB is the guarantor of the study.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.