Radiology - Research Publications
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ItemMachine Learning for Outcome Prediction of Acute Ischemic Stroke Post Intra-Arterial Therapy(PUBLIC LIBRARY SCIENCE, 2014-02-10)INTRODUCTION: Stroke is a major cause of death and disability. Accurately predicting stroke outcome from a set of predictive variables may identify high-risk patients and guide treatment approaches, leading to decreased morbidity. Logistic regression models allow for the identification and validation of predictive variables. However, advanced machine learning algorithms offer an alternative, in particular, for large-scale multi-institutional data, with the advantage of easily incorporating newly available data to improve prediction performance. Our aim was to design and compare different machine learning methods, capable of predicting the outcome of endovascular intervention in acute anterior circulation ischaemic stroke. METHOD: We conducted a retrospective study of a prospectively collected database of acute ischaemic stroke treated by endovascular intervention. Using SPSS®, MATLAB®, and Rapidminer®, classical statistics as well as artificial neural network and support vector algorithms were applied to design a supervised machine capable of classifying these predictors into potential good and poor outcomes. These algorithms were trained, validated and tested using randomly divided data. RESULTS: We included 107 consecutive acute anterior circulation ischaemic stroke patients treated by endovascular technique. Sixty-six were male and the mean age of 65.3. All the available demographic, procedural and clinical factors were included into the models. The final confusion matrix of the neural network, demonstrated an overall congruency of ∼ 80% between the target and output classes, with favourable receiving operative characteristics. However, after optimisation, the support vector machine had a relatively better performance, with a root mean squared error of 2.064 (SD: ± 0.408). DISCUSSION: We showed promising accuracy of outcome prediction, using supervised machine learning algorithms, with potential for incorporation of larger multicenter datasets, likely further improving prediction. Finally, we propose that a robust machine learning system can potentially optimise the selection process for endovascular versus medical treatment in the management of acute stroke.
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ItemAdvances in medical revascularisation treatments in acute ischemic stroke.(Hindawi Limited, 2014)Urgent reperfusion of the ischaemic brain is the aim of stroke treatment and there has been ongoing research to find a drug that can promote vessel recanalisation more completely and with less side effects. In this review article, the major studies which have validated the use and safety of tPA are discussed. The safety and efficacy of other thrombolytic and anticoagulative agents such as tenecteplase, desmoteplase, ancrod, tirofiban, abciximab, eptifibatide, and argatroban are also reviewed. Tenecteplase and desmoteplase are both plasminogen activators with higher fibrin affinity and longer half-life compared to alteplase. They have shown greater reperfusion rates and improved functional outcomes in preliminary studies. Argatroban is a direct thrombin inhibitor used as an adjunct to intravenous tPA and showed higher rates of complete recanalisation in the ARTTS study with further studies which are now ongoing. Adjuvant thrombolysis techniques using transcranial ultrasound are also being investigated and have shown higher rates of complete recanalisation, for example, in the CLOTBUST study. Overall, development in medical therapies for stroke is important due to the ease of administration compared to endovascular treatments, and the new treatments such as tenecteplase, desmoteplase, and adjuvant sonothrombolysis are showing promising results and await further large-scale clinical trials.