Enhanced physician performance when using an artificial intelligence model to detect ischemic stroke on computed tomography
Hillis, J. M.; Bizzo, B. C.; Gauriau, R.; Bridge, C. P.; Chin, J. K.; Hakamy, B.; Mercaldo, S.; Conklin, J.; Dutta, S.; Mehan, W. A.; Regenhardt, R. W.; Singh, A.; Singhal, A. B.; Sonis, J. D.; Succi, M. D.; Zhang, T.; Xing, B.; Kalafut, J. F.; Dreyer, K. J.; Lev, M. H.; Gonzalez, R. G.
Show abstract
Acute ischemic stroke can be subtle to detect on non-contrast computed tomography imaging. We show that a novel artificial intelligence model significantly improves the performance of physicians, including ED physicians, neurologists and radiologists, in identifying and quantifying the volume of acute ischemic stroke lesions. This model may lead to improved clinical decision-making for stroke patients.
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