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Potential Impact of an Artificial Intelligence-based Mammography Triage Algorithm on Performance and Workload in a

Alyssa T Watanabe^1,2, Hoanh Vu², Chi Y Chim²

¹Department of Radiology, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA, USA.

Journal of Breast Imaging

|September 8, 2024

Summary

This summary is machine-generated.

Artificial intelligence (AI) triage significantly improved mammography performance, reducing recall rates and increasing positive predictive values. This AI tool shows potential for substantial workload reduction in screening mammography without missing any cancers.

Keywords:

artificial intelligence decision support deep learning screening mammogram workload triage

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence in Healthcare

Background:

Screening mammography is crucial for early breast cancer detection.
Current mammography workflows face challenges with performance metrics and workload demands.
Artificial intelligence (AI) offers potential solutions for optimizing screening processes.

Purpose of the Study:

To evaluate the performance and workload impact of a commercial AI-based triage device in a population-based screening mammography sample.
To compare AI triage performance against radiologist reports and actual outcomes.
To assess the theoretical workload reduction achievable with AI triage.

Main Methods:

A retrospective study analyzed 2129 screening mammograms.
A commercial AI triage device categorized cases as 'suspicious' or 'low suspicion'.
AI performance was compared with radiologist reports, actual outcomes, and national benchmarks using key mammography metrics, with up to 5 years of follow-up data.

Main Results:

At 93% sensitivity, AI triage significantly improved recall rate (45.5%), PPV1 (119%), PPV2 (28.5%), sensitivity (20%), and specificity (7.2%).
A theoretical workload reduction of 62.5% was observed at 93% sensitivity and 27% at 99% sensitivity.
No cancers were missed by the AI algorithm at either tested sensitivity setting.

Conclusions:

AI-based triage shows significant potential to enhance mammography performance metrics.
The AI tool can lead to substantial theoretical workload reductions in screening mammography.
The implementation of AI triage in this simulation did not compromise cancer detection rates.