Artificial Intelligence Can Effectively Predict Early Hematoma Expansion of Intracerebral Hemorrhage Analyzing Noncontrast Computed Tomography Image.

Frontiers in Aging Neuroscience
Linyang TengTianhua Ren

Abstract

This study aims to develop and validate an artificial intelligence model based on deep learning to predict early hematoma enlargement (HE) in patients with intracerebral hemorrhage. A total of 1,899 noncontrast computed tomography (NCCT) images of cerebral hemorrhage patients were retrospectively analyzed to establish a predicting model and 1,117 to validate the model. And a total of 118 patients with intracerebral hemorrhage were selected based on inclusion and exclusion criteria so as to validate the value of the model for clinical prediction. The baseline noncontrast computed tomography images within 6 h of intracerebral hemorrhage onset and the second noncontrast computed tomography performed at 24 ± 3 h from the onset were used to evaluate the prediction of intracerebral hemorrhage growth. In validation dataset 1, the AUC was 0.778 (95% CI, 0.768-0.786), the sensitivity was 0.818 (95% CI, 0.790-0.843), and the specificity was 0.601 (95% CI, 0.565-0.632). In validation dataset 2, the AUC was 0.780 (95% CI, 0.761-0.798), the sensitivity was 0.732 (95% CI, 0.682-0.788), and the specificity was 0.709 (95% CI, 0.658-0.759). The sensitivity of intracerebral hemorrhage hematoma expansion as predicted by an artificial intelligence...Continue Reading

References

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