A real-world demonstration of machine learning generalizability in the detection of intracranial hemorrhage on head computerized tomography.

ABSTRACT: Machine learning (ML) holds great promise in transforming healthcare. While published studies have shown the utility of ML models in interpreting medical imaging examinations, these are often evaluated under laboratory settings. The importance of real world evaluation is best illustrated by case studies that have documented successes and failures in the translation of these models into clinical environments. A key prerequisite for the clinical adoption of these technologies is demonstrating generalizable ML model performance under real world circumstances. The purpose of this study was to demonstrate that ML model generalizability is achievable in medical imaging with the detection of intracranial hemorrhage (ICH) on non-contrast computed tomography (CT) scans serving as the use case. An M

SUBMITTER: Salehinejad H 

PROVIDER: S-EPMC8382750 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature