Deep Learning in the Prediction of Ischaemic Stroke Thrombolysis Functional Outcomes

Bacchi, Stephen; Zerner, Toby; Oakden-Rayner, Luke; Kleinig, Timothy; Patel, Sandy; Jannes, Jim

doi:10.1016/j.acra.2019.03.015

Cited by 86 publications

(73 citation statements)

References 11 publications

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“…Besides, the more extensive follow-up NIHSS could be used to define poor functional outcome in future studies. Furthermore, for future research, ML models could be created using the raw imaging data (CT or CT angiography or both) and combined with the models created in this study (10,12,36,37). However, the large number of data points has to be taken into account when developing such approaches because imaging data is often of high dimensionality, and medical datasets have often a very limited number of samples.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Predicting Poor Outcome Before Endovascular Treatment in Patients With Acute Ischemic Stroke

Ramos

Kappelhof

et al. 2020

Front. Neurol.

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Background: Although endovascular treatment (EVT) has greatly improved outcomes in acute ischemic stroke, still one third of patients die or remain severely disabled after stroke. If we could select patients with poor clinical outcome despite EVT, we could prevent futile treatment, avoid treatment complications, and further improve stroke care. We aimed to determine the accuracy of poor functional outcome prediction, defined as 90-day modified Rankin Scale (mRS) score ≥5, despite EVT treatment. Methods: We included 1,526 patients from the MR CLEAN Registry, a prospective, observational, multicenter registry of ischemic stroke patients treated with EVT. We developed machine learning prediction models using all variables available at baseline before treatment. We optimized the models for both maximizing the area under the curve (AUC), reducing the number of false positives. Results: From 1,526 patients included, 480 (31%) of patients showed poor outcome. The highest AUC was 0.81 for random forest. The highest area under the precision recall curve was 0.69 for the support vector machine. The highest achieved specificity was 95% with a sensitivity of 34% for neural networks, indicating that all models contained false positives in their predictions. From 921 mRS 0-4 patients, 27-61 (3-6%) were incorrectly classified as poor outcome. From 480 poor outcome patients in the registry, 99-163 (21-34%) were correctly identified by the models. Conclusions: All prediction models showed a high AUC. The best-performing models correctly identified 34% of the poor outcome patients at a cost of misclassifying 4% of non-poor outcome patients. Further studies are necessary to determine whether these accuracies are reproducible before implementation in clinical practice.

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Section: Strengths and Limitationsmentioning

confidence: 99%

Predicting Poor Outcome Before Endovascular Treatment in Patients With Acute Ischemic Stroke

Ramos

Kappelhof

et al. 2020

Front. Neurol.

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“…60 A combination CNN and ANN approach incorporating clinical and NCCT data predicted functional thrombolysis outcomes with accuracy 0.71 for 24-hour NIHSS improvement of $4 and accuracy 0.74 for 90-day mRS of 0-1. 61 Finally, traditional ML techniques and neural networks were used to predict hemorrhagic transformation of acute ischemic stroke before treatment from MRP source images and DWI, with the highest AUC of 0.837 6 2.6% using a kernel spectral regression ML technique. 62 One limitation of this study was the variable recanalization of the participants, which may have confounded results.…”

Section: Prognosticationmentioning

confidence: 99%

Artificial Intelligence and Acute Stroke Imaging

Soun¹,

Chow

Nagamine

et al. 2020

AJNR Am J Neuroradiol

165

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Artificial intelligence technology is a rapidly expanding field with many applications in acute stroke imaging, including ischemic and hemorrhage subtypes. Early identification of acute stroke is critical for initiating prompt intervention to reduce morbidity and mortality. Artificial intelligence can help with various aspects of the stroke treatment paradigm, including infarct or hemorrhage detection, segmentation, classification, large vessel occlusion detection, Alberta Stroke Program Early CT Score grading, and prognostication. In particular, emerging artificial intelligence techniques such as convolutional neural networks show promise in performing these imaging-based tasks efficiently and accurately. The purpose of this review is twofold: first, to describe AI methods and available public and commercial platforms in stroke imaging, and second, to summarize the literature of current artificial intelligence-driven applications for acute stroke triage, surveillance, and prediction. ABBREVIATIONS: AI ¼ artificial intelligence; ANN ¼ artificial neural network; AUC ¼ area under the curve; CNN ¼ convolutional neural network; DL ¼ deep learning; ICC ¼ intraclass correlation coefficient; ICH ¼ intracranial hemorrhage; LVO ¼ large vessel occlusion; ML ¼ machine learning; MRP ¼ MR perfusion; RF ¼ random forest; SVM ¼ support vector machine S troke is the second leading cause of death worldwide with an annual mortality of about 5.5 million. 1,2 In the United States, nearly 800,000 people have a stroke annually, and the economic burden of stroke is estimated at $34 billion per year. 3 Morbidity is high, with more than half of patients with stroke left chronically disabled. 2 Neuroimaging is an important tool for the detection, characterization, and prognostication of acute strokes, including ischemic and hemorrhagic subtypes. Artificial intelligence (AI) technology is a rapidly burgeoning field, providing a promising avenue for fast and efficient imaging analysis. 4 AI applications for imaging of acute cerebrovascular disease have been implemented, including tools for triage, quantification, surveillance, and prediction. This review aims to summarize the current landscape of AIdriven applications for acute cerebrovascular disease assessment focusing primarily on deep learning (DL) methods. OVERVIEW OF AI Although AI, machine learning (ML), and DL are used interchangeably, these in fact represent subdisciplines. Specifically, DL is a subset of ML, and ML is a subset of AI (Fig 1). Broadly, AI uses computers to perform tasks that typically require human knowledge. ML, a subset of AI, uses statistical approaches to enable machines to optimize outcome prediction as they are exposed to data and train computers for pattern recognition, a task generally requiring human intelligence. 5 ML offers several potential advantages over visual inspection by human experts, including objective and quantitative evaluation, the ability to detect subtle voxel-level patterns, speed, and large-scale implementation. Feature selection, classifi...

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“…Despite the large dataset, they suggested the need for more data to optimize the model's performance. Bacchi et al [22] showed the trained convolutional neural network (CNN) in combination with ANN on 204 samples for the prediction of dichotomized three-month mRS ≤ 1 and mRS ≤ 2. CT scans and clinical data served as inputs to CNN and ANN, respectively.…”

Section: 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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Deep Learning in the Prediction of Ischaemic Stroke Thrombolysis Functional Outcomes

Cited by 86 publications

References 11 publications

Predicting Poor Outcome Before Endovascular Treatment in Patients With Acute Ischemic Stroke

Predicting Poor Outcome Before Endovascular Treatment in Patients With Acute Ischemic Stroke

Artificial Intelligence and Acute Stroke Imaging

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

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