1446
clinical study

AI-augmented review of CT cases to facilitate peer review of intracranial hemorrhage by practicing neuroradiologists

Intracranial Hemorrhage

Materials & Methods

A retrospective study compared discrepancies between CT reports and AI-based detection of ICH. All non-contrast CT brain cases were collected over two months; cases reported as negative for ICH were included (n=1,812), while cases reported as suspicious for ICH (n=504) were excluded. Fellowship-trained NRs finalized all reports. An AI-tool trained to detect ICH was then run on the case set.

Results

Upon final consensus, 22 cases of ICH detected by AI were missed on finalized reports, yielding an error rate of 4.2% (22/ (504+22)).

Conclusions

AI-augmentation can flag potential discrepancies between scans and their reports, possibly improving the quantification of error rates among practicing radiologists.

Read Study

Explore the Latest AI Insights, Trends and Research

Follow a manual added link

Webinars

Aidoc experts, customers and industry leaders share the latest in AI benefits and adoption.

Follow a manual added link

eBooks

Explore how clinical AI can transform your health system with insights rooted in real-world experiences.

Follow a manual added link

AI Strategy

Learn how to go beyond the algorithm to develop a scalable AI strategy and implementation plan.

Follow a manual added link

Solutions

Explore how Aidoc can help increase hospital efficiency, improve outcomes and demonstrate ROI.

From Our Blog

View all

© Copyright 2024 Aidoc