  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 ML model was trained using 21,784 scans from the
RSNA Intracranial Hemorrhage CT dataset while generalizability was evaluated
using an external validation dataset obtained from our busy trauma and
neurosurgical center. This real world external validation dataset consisted of
every unenhanced head CT scan (n = 5,965) performed in our emergency department
in 2019 without exclusion. The model demonstrated an AUC of 98.4%, sensitivity
of 98.8%, and specificity of 98.0%, on the test dataset. On external
validation, the model demonstrated an AUC of 95.4%, sensitivity of 91.3%, and
specificity of 94.1%. Evaluating the ML model using a real world external
validation dataset that is temporally and geographically distinct from the
training dataset indicates that ML generalizability is achievable in medical
imaging applications.
