Multi-modal deep learning for predicting functional outcomes in intracerebral hemorrhage using 3D CT and clinical data

Intracerebral hemorrhage (ICH) is a critical neurological condition with a 30-day mortality rate as high as 35–52 % [1]. Among survivors, only a small proportion regain functional independence, placing a substantial economic and caregiving burden on families and society [2]. Therefore, accurately predicting long-term functional outcomes in the early stages of the disease is crucial for guiding individualized treatment and optimizing the allocation of medical resources [3].

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