Assessment of commercially available artificial intelligence software for differentiating hemorrhage from contrast on head CT following thrombolysis for ischemic stroke

Background: In patients who have undergone ischemic stroke therapy, retained iodine-based contrast can resemble acute intracranial hemorrhage (ICH) on standard computed tomography (CT). The purpose of this study is to determine the accuracy of commercially available artificial intelligence software for differentiating hemorrhage from contrast in such cases.

Methods: A total of 45 CT scans analyzed by Aidoc software that also included dual-energy iodine subtraction maps from dual energy CT from 23 unique patients (12 male, 11 female, age range 30–99 years, mean age 67.6 years, standard deviation 18.5 years) following recent ischemic stroke therapy were retrospectively reviewed for the presence of hemorrhage versus retained contrast material.

Results: The sensitivity and specificity of the model in detecting acute intracranial hemorrhage as opposed to contrast were 51.7 and 50.0%, respectively. The positive and negative predictive values were 65.2 and 36.4%, respectively.

Conclusion: The current Aidoc software is not optimized for differentiating between acute hemorrhage and retained contrast on CT. This justifies the development of a more robust artificial intelligence model trained to differentiate between ICH and iodine contrast based on both DECT and standard CT images.