A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans.

ABSTRACT: Acute Intracranial hemorrhage (ICH) is a life-threatening disease that requires emergency medical attention, which is routinely diagnosed using non-contrast head CT imaging. The diagnostic accuracy of acute ICH on CT varies greatly among radiologists due to the difficulty of interpreting subtle findings and the time pressure associated with the ever-increasing workload. The use of artificial intelligence technology may help automate the process and assist radiologists for more prompt and better decision-making. In this work, we design a deep learning approach that mimics the interpretation process of radiologists, and combines a 2D CNN model and two sequence models to achieve accurate acute ICH detection and subtype classification. Being developed using the extensive 2019-RSNA Brain CT Hem

SUBMITTER: Wang X 

PROVIDER: S-EPMC8377493 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature