An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study
Hao Zhang,
Yun-Feng Yang,
Xue-Lin Song
et al.
Abstract:Objectives
To develop and validate a novel interpretable artificial intelligence (AI) model that integrates radiomic features, deep learning features, and imaging features at multiple semantic levels to predict the prognosis of intracerebral hemorrhage (ICH) patients at 6 months post-onset.
Materials and methods
Retrospectively enrolled 222 patients with ICH for Non-contrast Computed Tomography (NCCT) images and clinical data, who were divided into… Show more
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