Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies.

ABSTRACT:

Background

Highly accurate detection of intracranial hemorrhages (ICH) on head computed tomography (HCT) scans can prove challenging at high-volume centers. This study aimed to determine the number of additional ICHs detected by an artificial intelligence (AI) algorithm and to evaluate reasons for erroneous results at a level I trauma center with teleradiology services.

Methods

In a retrospective multi-center cohort study, consecutive emergency non-contrast HCT scans were analyzed by a commercially available ICH detection software (AIDOC, Tel Aviv, Israel). Discrepancies between AI analysis and initial radiology report (RR) were reviewed by a blinded neuroradiologist to determine the number of additional ICHs detected and evaluate reasons leading to errors.

Results

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SUBMITTER: Kundisch A 

PROVIDER: S-EPMC8629230 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature