Deep learning survival model predicts outcome after intracerebral hemorrhage from initial CT scan.

ABSTRACT:

Background

Predicting functional impairment after intracerebral hemorrhage (ICH) provides valuable information for planning of patient care and rehabilitation strategies. Current prognostic tools are limited in making long term predictions and require multiple expert-defined inputs and interpretation that make their clinical implementation challenging. This study aimed to predict long term functional impairment of ICH patients from admission non-contrast CT scans, leveraging deep learning models in a survival analysis framework.

Methods

We used the admission non-contrast CT scans from 882 patients from the Massachusetts General Hospital ICH Study for training, hyperparameter optimization, and model selection, and 146 patients from the Yale New Haven ICH Study for external va

SUBMITTER: Chen Y 

PROVIDER: S-EPMC11569453 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature