ArticleThe neuroradiology journal2022
Value of machine learning to predict functional outcome of endovascular treatment for acute ischaemic stroke of the posterior circulation.
Article in The neuroradiology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
- Diagnostic Accuracy of Machine Learning Models in Predicting Functional Outcome of Thrombectomy for Acute Posterior Circulation Artery Occlusion: a Systematic Review and Meta-Analysis.Clinical neuroradiology · 2026Pooled it
- Outcome prediction prior to thrombectomy of the posterior circulation with machine learning.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2025Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeClinical outcomes vary considerably among individuals with vessel occlusion of the posterior circulation. In the present study we evaluated machine learning algorithms in their ability to discriminate between favourable and unfavourable outcomes in patients with endovascular treatment of acute ischaemic stroke of the posterior circulation.
methodsThis retrospective study evaluated three algorithms (generalised linear model, K-nearest neighbour and random forest) to predict functional outcomes at dismissal of 30 patients with acute occlusion of the basilar artery who were treated with thrombectomy. Input variables encompassed baseline as well as peri and postprocedural data. Favourable outcome was defined as a modified Rankin scale score of 0-2 and unfavourable outcome was defined as a modified Rankin scale score of 3-6. The performance of the algorithms was assessed with the area under the receiver operating curve and with confusion matrixes.
resultsSuccessful reperfusion was achieved in 83%, with 30% of the patients having a favourable outcome. The area under the curve was 0.93 for the random forest model, 0.86 for the K-nearest neighbour model and 0.78 for the generalised linear model. The accuracy was 0.69 for the generalised linear model and 0.84 for the random forest and the K nearest neighbour models.
conclusionFavourable and unfavourable outcomes at dismissal of patients with acute ischaemic stroke of the posterior circulation can be predicted immediately after the follow-up non-enhanced computed tomography using machine learning.
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