Deep Learning System Boosts Radiologist Detection of Intracranial Hemorrhage.

Autor:	Warman R; Neuroscience, Caire Health, Inc., Tampa, USA., Warman A; Neuroscience, Caire Health, Inc., Tampa, USA., Warman P; Neuroscience, Caire Health, Inc., Tampa, USA., Degnan A; Radiology, University of Pittsburgh Medical Center (UPMC) Children's Hospital of Pittsburgh, Pittsburgh, USA., Blickman J; Radiology, Caire Health, Inc., Tampa, USA., Chowdhary V; Radiology, Caire Health, Inc., Tampa, USA., Dash D; Emergency Medicine, Stanford University, Stanford, USA., Sangal R; Emergency Medicine, Yale School of Medicine, New Haven, USA., Vadhan J; Emergency Medicine, The University of Texas Southwestern (UTSW), Dallas, USA., Bueso T; Neurology, The Texas Tech University Health Sciences Center (TTUHSC), Lubbock, USA., Windisch T; Radiology, Covenant Health, Lubbock, USA., Neves G; Neurology, The Texas Tech University Health Sciences Center (TTUHSC), Lubbock, USA.
Jazyk:	angličtina
Zdroj:	Cureus [Cureus] 2022 Oct 13; Vol. 14 (10), pp. e30264. Date of Electronic Publication: 2022 Oct 13 (Print Publication: 2022).
DOI:	10.7759/cureus.30264
Abstrakt:	Background: Intracranial hemorrhage (ICH) requires emergent medical treatment for positive outcomes. While previous artificial intelligence (AI) solutions achieved rapid diagnostics, none were shown to improve the performance of radiologists in detecting ICHs. Here, we show that the Caire ICH artificial intelligence system enhances a radiologist's ICH diagnosis performance. Methods: A dataset of non-contrast-enhanced axial cranial computed tomography (CT) scans (n=532) were labeled for the presence or absence of an ICH. If an ICH was detected, its ICH subtype was identified. After a washout period, the three radiologists reviewed the same dataset with the assistance of the Caire ICH system. Performance was measured with respect to reader agreement, accuracy, sensitivity, and specificity when compared to the ground truth, defined as reader consensus. Results: Caire ICH improved the inter-reader agreement on average by 5.76% in a dataset with an ICH prevalence of 74.3%. Further, radiologists using Caire ICH detected an average of 18 more ICHs and significantly increased their accuracy by 6.15%, their sensitivity by 4.6%, and their specificity by 10.62%. The Caire ICH system also improved the radiologist's ability to accurately identify the ICH subtypes present. Conclusion: The Caire ICH device significantly improves the performance of a cohort of radiologists. Such a device has the potential to be a tool that can improve patient outcomes and reduce misdiagnosis of ICH. Competing Interests: The authors have declared financial relationships, which are detailed in the next section. (Copyright © 2022, Warman et al.)
Databáze:	MEDLINE
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