Clinical evaluation of a deep-learning model for automatic scoring of the Alberta stroke program early CT score on non-contrast CT.

ABSTRACT:

Background

Automated measurement of the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) can support clinical decision making. Based on a deep learning algorithm, we developed an automated ASPECTS scoring system (Heuron ASPECTS) and validated its performance in a prespecified clinical trial.

Methods

For model training, we used non-contrast computed tomography images of 487 patients with acute ischemic stroke (AIS). For the clinical trial, 326 patients (87 with AIS, 56 with other acute brain diseases, and 183 with no brain disease) were enrolled. The results of Heuron ASPECTS were compared with the consensus generated by two stroke experts using the Bland-Altman agreement. A mean difference of less than 0.35 and a maximum allowed difference of less than 3.8 we

SUBMITTER: Lee SJ 

PROVIDER: S-EPMC10804033 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature