Development and external validation of a stability machine learning model to identify wake-up stroke onset time from MRI.
Objectives: To develop and externally validate a machine learning (ML) model based on diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) to identify the onset time of wake-up stroke from MRI.Methods: DWI and FLAIR images of stroke patients within 24 h of clear symptom o...
| Published in: | European Radiology Vol. 32; no. 6; pp. 3661 - 3670 |
|---|---|
| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Jun2022
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157006562&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157006562 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jun2022 vid: 32 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 157006562 157006562 NLM35037969 10.1007/s00330-021-08493-6 NLM35037969 157006562 ppf: 3661 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Development and external validation of a stability machine learning model to identify wake-up stroke onset time from MRI. aug: au: Jiang, Liang Wang, Siyu Ai, Zhongping Shen, Tingwen Zhang, Hong Duan, Shaofeng Chen, Yu-Chen Yin, Xindao Sun, Jun affil: Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No. 68, Changle Road, 210006, Nanjing, China sug: ab: Objectives: To develop and externally validate a machine learning (ML) model based on diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) to identify the onset time of wake-up stroke from MRI.Methods: DWI and FLAIR images of stroke patients within 24 h of clear symptom onset in our hospital (dataset 1, n = 410) and another hospital (dataset 2, n = 177) were included. Seven ML models based on dataset 1 were developed to estimate the stroke onset time for binary classification (≤ 4.5 h or > 4.5 h): Random Forest (RF), support vector machine with kernel (svmLinear) or radial basis function kernel (svmRadial), Bayesian (Bayes), K-nearest neighbor (KNN), adaptive boosting (AdaBoost), and neural network (NNET). ROC analysis and RSD were performed to evaluate the performance and stability of the ML models, respectively, and dataset 2 was externally validated to evaluate the model generalization ability using ROC analysis.Results: svmRadial achieved the best performance with the highest AUC and accuracy (AUC: 0.896, accuracy: 0.878), and was the most stable (RSD% of AUC: 0.08, RSD% of accuracy: 0.06). The svmRadial model was then selected as the final model, and the AUC of the svmRadial model for predicting the onset time external validation was 0.895, with 0.825 accuracy.Conclusions: The svmRadial model using DWI + FLAIR is the most stable and generalizable for identifying the onset time of wake-up stroke patients within 4.5 h of symptom onset.Key Points: • Machining learning model helps clinicians to identify wake-up stroke patients within 4.5 h of symptom onset. • A prospective study showed that svmRadial model based on DWI + FLAIR was the most stable in predicting the stroke onset time. • External validation showed that svmRadial model has good generalization ability in predicting the stroke onset time. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|