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The AI-based model for dynamic MRI protocoling demonstrated strong diagnostic performance across multiple brain pathologies. In the initial stage, infarct detection reached 90.1% sensitivity […]
Source: UNIVERSITY OF COPENHAGEN
Source: DIAGNOSTICS
Source: DIAGNOSTICShttps://www.mdpi.com/2075-4418/13/12/2111
Discover the concept of a “safe reporting backlog” centering on optimizing safety, prioritization, and quality of care for patients – enabled by the use of AI to streamline radiology processes and boost patient safety.
Source: NATIONAL LIBRARY OF MEDICINE
This opinion paper highlights the ongoing transformation of MRI systems from static to dynamic, algorithm-driven operations. AI-powered MRI systems can automatically tailor scanning protocols […]
On February 15, 2024, an enlightening webinar organized by CEREBRIU brought together leading experts from esteemed institutions such as Guy’s and St Thomas’ NHS […]
Source: DIAGNOSTICS
Can AI and related technology positively impact the imaging process
In a study involving 995 patients (mean age 69 years; 53% female), an AI-driven MRI model was evaluated for its ability to detect acute […]
In a pilot study of 43 consecutive patients, the AI tool flagged 13 of 16 acute non-lacunar infarcts, yielding a diagnostic accuracy of 81%. […]