Plesner, Louis Lind | RegionH Denmark

Scientific poster presentation (W2-SPIN-3) at RSNA 2023, 26 – 30. November 2023 in Chicago, US

Purpose: Can an AI tool effectively triage CXR cases into remarkable and unremarkable categories in clinical practice?

Method: Retrospective validation of an AI model (Annalise Enterprise CXR), post-processed to provide unremarkable and remarkable distinction on 1.990 consecutive CXR studies with dual thoracic radiologist’s reference. The AI model was compared to binary classification extracted from RIS regarding various performance measures.

Results: The AI model demonstrated an AUC of 0.926 and was statistically superior to routine classification across all evaluated measures. 

Key Takeaway: The AI model achieved excellent discrimination between unremarkable and remarkable CXRs and was superior to clinically assigned priority levels. 

Interested in learning more? Get in touch at info@annalise.ai