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We aimed to predict functional outcomes in patients with acute brainstem infarction using deep neuroimaging features extracted by convolutional neural networks (CNNs).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This nationwide multicenter stroke registry study included 1482 patients with acute brainstem infarction. We applied CNNs to automatically extract deep neuroimaging features from diffusion\u2010weighted imaging. Deep learning models based on clinical features, laboratory features, conventional imaging features (infarct volume, number of infarctions), and deep neuroimaging features were trained to predict functional outcomes at 3\u00a0months poststroke. Unfavorable outcome was defined as modified Rankin Scale score of 3 or higher at 3\u00a0months. The models were evaluated by comparing the area under the receiver operating characteristic curve (AUC).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A model based solely on 14 deep neuroimaging features from CNNs achieved an extremely high AUC of 0.975 (95% confidence interval [CI] = 0.934\u20130.997) and significantly outperformed the model combining clinical, laboratory, and conventional imaging features (0.772, 95% CI = 0.691\u20130.847, <jats:italic>p\u00a0<\/jats:italic>&lt;\u00a00.001) in prediction of functional outcomes. The deep neuroimaging model also demonstrated significant improvement over traditional prognostic scores. In an interpretability analysis, the deep neuroimaging features displayed a significant correlation with age, National Institutes of Health Stroke Scale score, infarct volume, and inflammation factors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Deep learning models can successfully extract objective neuroimaging features from the routine radiological data in an automatic manner and aid in predicting the functional outcomes in patients with brainstem infarction at 3\u00a0months with very high accuracy.<\/jats:p><\/jats:sec>","DOI":"10.1111\/ene.15181","type":"journal-article","created":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T05:44:35Z","timestamp":1636782275000},"page":"744-752","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Predicting functional outcome in patients with acute brainstem infarction using deep neuroimaging features"],"prefix":"10.1111","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0114-8157","authenticated-orcid":false,"given":"Lingling","family":"Ding","sequence":"first","affiliation":[{"name":"Department of Neurology Beijing Tiantan Hospital Capital Medical University Beijing China"},{"name":"China National Clinical Research Center for Neurological Diseases Beijing China"},{"name":"Research Unit of Artificial Intelligence in Cerebrovascular Disease Chinese Academy of Medical Sciences Beijing China"},{"name":"China National Clinical Research Center\u2010Hanalytics Artificial Intelligence Research Centre for Neurological Disorders Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Biological Science and Medical Engineering Beihang University Beijing 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