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Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation

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Enhancing Radiologist Efficiency with AI: A Multi-Reader Multi-Case Study on Aortic Dissection Detection and

Martina Cotena¹, Angela Ayobi¹, Colin Zuchowski²

¹Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France.

Diagnostics (Basel, Switzerland)

|December 17, 2024

Summary

This summary is machine-generated.

Deep learning AI significantly reduces scan-to-assessment time and interpretation time for acute aortic dissection detection on CTAs. This AI-assisted approach improves workflow efficiency over standard methods.

Keywords:

aortic dissection automated detection deep learning emergency radiology multi-reader multi-case study prioritized worklist

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Area of Science:

Radiology
Artificial Intelligence
Cardiovascular Imaging

Background:

Acute aortic dissection (AD) is a critical condition requiring rapid diagnosis.
Early detection of AD on chest CT angiographies (CTAs) is vital for patient outcomes.
Evaluating AI's role in improving AD detection efficiency is crucial.

Purpose of the Study:

To assess the clinical benefits of a deep learning (DL) application for automated AD detection on CTAs.
To quantify the reduction in scan-to-assessment time (STAT) and interpretation time (IT) with AI assistance.
To compare AI-assisted AD detection workflow against the standard First-In, First-Out (FIFO) method.

Main Methods:

Retrospective Multi-Reader Multi-Case (MRMC) study design.
Comparison of CTA assessments with and without AI assistance by expert radiologists.
Measurement of STAT and IT in both AI-assisted and unaided arms.

Main Results:

AI significantly reduced STAT for AD detection by 68% (from 15.84 min to 5.07 min).
AI also significantly reduced interpretation time (IT) (from 21.22 s to 14.17 s).
The study included 285 CTAs with 37% AD prevalence.

Conclusions:

Deep learning algorithms effectively reduce STAT and IT for AD detection on CTAs.
AI-assisted workflows enhance diagnostic efficiency and prioritize urgent cases.
AI integration offers a superior approach compared to traditional FIFO workflows.