Prediction of Thrombectomy Functional Outcomes Using Multimodal Data

Samak, Zeynel A.; Clatworthy, Philip; Mirmehdi, Majid

doi:10.1007/978-3-030-52791-4_21

Cited by 14 publications

(28 citation statements)

References 33 publications

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“…The mean or median ages of the study participants ranged from 64.0 to 86.0 years, and the proportion of male participants ranged from 35.0 to 65.9%. Only one US study ( 24 ) specifically described the self-reported ethnicity of the patients (63.0–69.0% European ancestry); the other studies reported the place of patient recruitment [USA: 1 ( 32 ); Europe: 10 ( 22 , 23 , 26 – 29 , 31 , 33 – 35 ); Asia: 4 ( 25 , 30 , 36 , 37 )]. The training sample sizes ranged widely, from 109 to 1,401.…”

Section: Resultsmentioning

confidence: 99%

“…The training sample sizes ranged widely, from 109 to 1,401. Regarding the testing sample, two studies used hold-out test sets, respectively containing 208 patients ( 30 ) and 100 patients ( 35 ). The remaining studies performed cross-validation ( 23 – 26 , 28 , 29 , 31 – 34 , 36 , 37 ) or bootstrap approach ( 22 , 27 ).…”

Section: Resultsmentioning

confidence: 99%

“…The remaining studies performed cross-validation ( 23 – 26 , 28 , 29 , 31 – 34 , 36 , 37 ) or bootstrap approach ( 22 , 27 ). The five studies ( 23 , 29 , 31 , 34 , 35 ) used data obtained from MR CLEAN Registry ( 38 ). Fifteen studies reported the occlusion sites, of which 14 studies ( 22 , 23 , 25 – 36 ) included patients with anterior circulation occlusion and one ( 24 ) further included patients with occlusion in the posterior circulation.…”

Section: Resultsmentioning

confidence: 99%

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Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis

et al. 2022

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IntroductionMachine learning (ML) methods are being increasingly applied to prognostic prediction for stroke patients with large vessel occlusion (LVO) treated with endovascular thrombectomy. This systematic review aims to summarize ML-based pre-thrombectomy prognostic models for LVO stroke and identify key research gaps.MethodsLiterature searches were performed in Embase, PubMed, Web of Science, and Scopus. Meta-analyses of the area under the receiver operating characteristic curves (AUCs) of ML models were conducted to synthesize model performance.ResultsSixteen studies describing 19 models were eligible. The predicted outcomes include functional outcome at 90 days, successful reperfusion, and hemorrhagic transformation. Functional outcome was analyzed by 10 conventional ML models (pooled AUC=0.81, 95% confidence interval [CI]: 0.77–0.85, AUC range: 0.68–0.93) and four deep learning (DL) models (pooled AUC=0.75, 95% CI: 0.70–0.81, AUC range: 0.71–0.81). Successful reperfusion was analyzed by three conventional ML models (pooled AUC=0.72, 95% CI: 0.56–0.88, AUC range: 0.55–0.88) and one DL model (AUC=0.65, 95% CI: 0.62–0.68).ConclusionsConventional ML and DL models have shown variable performance in predicting post-treatment outcomes of LVO without generally demonstrating superiority compared to existing prognostic scores. Most models were developed using small datasets, lacked solid external validation, and at high risk of potential bias. There is considerable scope to improve study design and model performance. The application of ML and DL methods to improve the prediction of prognosis in LVO stroke, while promising, remains nascent.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021266524, identifier CRD42021266524

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis

et al. 2022

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“…The results were reported on both dichotomized and non-dichotomized mRS with accuracy used for full scale mRS. Reporting accuracy as an evaluation metric for non-dichotomized output in the presence of class imbalance may mislead in measuring model performance [37].…”

Section: Non-dichotomized Outputmentioning

confidence: 99%

Predicting Clinical Outcome in Acute Ischemic Stroke Using Parallel Multi-Parametric Feature Embedded Siamese Network

2020

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Stroke is the second leading cause of death and disability worldwide, with ischemic stroke as the most common type. The preferred diagnostic procedure at the acute stage is the acquisition of multi-parametric magnetic resonance imaging (MRI). This type of imaging not only detects and locates the stroke lesion, but also provides the blood flow dynamics that helps clinicians in assessing the risks and benefits of reperfusion therapies. However, evaluating the outcome of these risky therapies beforehand is a complicated task due to the variability of lesion location, size, shape, and cerebral hemodynamics involved. Though the fully automated model for predicting treatment outcomes using multi-parametric imaging would be highly valuable in clinical settings, MRI datasets acquired at the acute stage are mostly scarce and suffer high class imbalance. In this paper, parallel multi-parametric feature embedded siamese network (PMFE-SN) is proposed that can learn with few samples and can handle skewness in multi-parametric MRI data. Moreover, five suitable evaluation metrics that are insensitive to imbalance are defined for this problem. The results show that PMFE-SN not only outperforms other state-of-the-art techniques in all these metrics but also can predict the class with a small number of samples, as well as the class with high number of samples. An accuracy of 0.67 on leave one cross out testing has been achieved with only two samples (minority class) for training and accuracy of 0.61 with the highest number of samples (majority class). In comparison, state-of-the-art using hand crafted features has 0 accuracy for minority class and 0.33 accuracy for majority class.

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“…Previous approaches, which introduced multimodal network architectures, have combined medical images with basic demographics to predict the outcome of endovascular treatment from clinical metadata and imaging [20], or to classify skin lesions from dermoscopic images and patient age and sex [7]. Limited amount of work has been done in relation to cardiomegaly classification from combining imaging and non-imaging data.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Classification of Cardiomegaly Using Combined Imaging and Non-imaging ICU Data

Grant

Papież

Parsons

et al. 2021

Lecture Notes in Computer Science

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In this paper, we investigate the classification of cardiomegaly using multimodal data, combining imaging data from chest radiography with routinely collected Intensive Care Unit (ICU) data comprising vital sign values, laboratory measurements, and admission metadata. In practice a clinician would assess for the presence of cardiomegaly using a synthesis of multiple sources of data, however, prior machine learning approaches to this task have focused on chest radiographs only. We show that non-imaging ICU data can be used for cardiomegaly classification and propose a novel multimodal network trained simultaneously on both chest radiographs and ICU data. We compare the predictive power of both single-mode approaches with the joint network. We use a subset of data from the publicly available MIMIC-CXR and MIMIC-IV datasets, which contain both chest radiographs and non-imaging ICU data for the same patients. The approach from non-imaging ICU data alone achieves an AUC of 0.684 and the standard chest radiography approach an AUC of 0.840. Our joint model achieves an AUC of 0.880. We conclude that non-imaging ICU data have predictive value for cardiomegaly, and that combining chest radiographs with non-imaging ICU data has the potential to improve model performance for the same subset of patients, with further work required to demonstrate a significant improvement.

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Prediction of Thrombectomy Functional Outcomes Using Multimodal Data

Cited by 14 publications

References 33 publications

Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis

Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis

Predicting Clinical Outcome in Acute Ischemic Stroke Using Parallel Multi-Parametric Feature Embedded Siamese Network

Deep Learning Classification of Cardiomegaly Using Combined Imaging and Non-imaging ICU Data

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