Deep learning-assisted detection of intracranial hemorrhage: validation and impact on reader performance.

ABSTRACT:

Purpose

Intracranial hemorrhage (ICH) requires urgent treatment, and accurate and timely diagnosis is essential for improving outcomes. This pivotal clinical trial aimed to validate a deep learning algorithm for ICH detection and assess its clinical utility through a reader performance test.

Methods

Retrospective CT scans from patients with and without ICH were collected from a tertiary hospital. Two experts evaluated all scans, with a third expert reviewing disagreements for the final diagnosis. We analyzed the performance of the deep learning algorithm, JLK-ICH, for all cases and ICH subtypes. Additional external validation was performed using a multi-ethnic U.S.

Dataset

A reader performance study included six non-expert readers who evaluated 800 CT scans, with and

SUBMITTER: Kang DW 

PROVIDER: S-EPMC12357807 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature