Qiu W, Kuang H, Teleg E, Ospel JM, Sohn S Il, Almekhlafi M, et al. Stroke | National Heart, Lung, and Blood Institute (NHLBI). Stroke location is an independent predictor of cognitive outcome. Munsch F, Sagnier S, Asselineau J, Bigourdan A, Guttmann CR, Debruxelles S, et al. Multivariate prediction of functional outcome using lesion topography characterized by acute diffusion tensor imaging. Moulton E, Valabregue R, Lehéricy S, Samson Y, Rosso C. The impact of early specialist management on outcomes of patients with in-hospital stroke. Automated ASPECTS on noncontrast CT scans in patients with acute ischemic stroke using machine learning. Kuang H, Najm M, Chakraborty D, Maraj N, Sohn SI, Goyal M, et al. Predicting long-term outcome after acute ischemic stroke. König IR, Ziegler A, Bluhmki E, Hacke W, Bath PMW, Sacco RL, et al. Predicting clinical outcome of stroke patients with tractographic feature. Machine learning in acute ischemic stroke neuroimaging. Combined clinical and imaging information as an early stroke outcome measure. Johnston KC, Wagner DP, Haley EC, Connors AF. Global, regional, and national burden of stroke, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Johnson CO, Nguyen M, Roth GA, Nichols E, Alam T, Abate D, et al. James G, Witten D, Hastie T, Tibshirani R. Machine learning-based model for prediction of outcomes in acute stroke. Heo JN, Yoon JG, Park H, Kim YD, Nam HS, Heo JH. Detection of early infarction signs with machine learning-based diagnosis by means of the Alberta Stroke Program Early CT score (ASPECTS) in the clinical routine. Guberina N, Dietrich U, Radbruch A, Goebel J, Deuschl C, Ringelstein A, et al. The relationship between Precision-Recall and ROC curves. A coefficient of agreement for nominal scales. Influence of stroke infarct location on functional outcome measured by the Modified Rankin Scale. Available from: Ĭheng B, Forkert ND, Zavaglia M, Hilgetag CC, Golsari A, Siemonsen S, et al. Multimodal predictive modeling of endovascular treatment outcome for acute ischemic stroke using machine-learning. (00)02237-6īrugnara G, Neuberger U, Mahmutoglu MA, Foltyn M, Herweh C, Nagel S, et al. Validity and reliability of a quantitative computed tomography score in predicting outcome of hyperacute stroke before thrombolytic therapy. Available from: īarber PA, Demchuk AM, Zhang J, Buchan AM. Interobserver agreement for the assessment of handicap in stroke patients. Conclusionĭespite being more complex, critical eloquent brain areas and damage severity can play a crucial role in improving clinical prediction in ischemic stroke prognosis, which deserves careful attention in the context of the current data science techniques.īamford JM, Sandercock PAG, Warlow CP, Slattery J. We identified six promising areas and observed that the weighted approach had the best performance among the binary option and ASPECTS scenarios. We applied a wrapper feature selection based on sequential forward strategy combined with a linear discriminant analysis classifier considering: (1) the binary lesion approach for investigating the role of critical eloquent brain areas (2) the weighted lesion approach for investigating the role of lesion severity (3) the ASPECTS predictor performance for benchmark comparison. ![]() The stroke outcome prediction performance was investigated and compared with results obtained under the classical ASPECTS. This study aims to identify the eloquent brain areas most related to the patients’ prognosis through machine learning techniques, considering a binary (affected or non-affected region - as ranked under ASPECTS paradigm) and a weighted (3-level damage severity degree) approach. Despite its widespread use, the ASPECTS has some limitations since it does not consider different eloquent brain regions impact on stroke patients’ functional outcome or even the lesion severity of the respective area. ![]() Among the predictors used, the Alberta Stroke Program Early CT Score (ASPECTS) deserves special attention, given its standardized assessment by evaluating computed tomography (CT) scans. A careful analysis of potential clinical predictors after stroke under the current data science paradigms can significantly improve prognosis reliability and lead to the anticipation of the most suitable rehabilitation program to be adopted, with crucial benefits to avoid severe disabilities. Acute ischemic stroke is one of the leading causes of disability globally, requiring the best-integrated approach between prevention, intervention, and therapies aiming to avoid the worse outcome scenario.
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