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Performance of a Screening Mammography AI Algorithm Repurposed for Symptomatic Mammography in a Tertiary Outpatient
Helen Ngo1, Eric Niller1, Eric Schmitz1
1Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine-University of Freiburg, 79106 Freiburg, Germany.
Diagnostics (Basel, Switzerland)
|April 14, 2026
Summary
A screening artificial intelligence (AI) algorithm demonstrated high diagnostic accuracy for symptomatic mammograms in women. This AI tool shows promise for improving breast cancer detection in clinical settings.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Evaluating diagnostic accuracy of AI algorithms in clinical practice is crucial.
- Commercial AI algorithms developed for screening may have potential for diagnostic settings.
Purpose of the Study:
- To assess the diagnostic accuracy of a commercial AI algorithm for symptomatic mammography.
- To determine AI performance in symptomatic women compared to screening mammography.
Main Methods:
- Retrospective diagnostic accuracy study of 78 symptomatic women undergoing digital mammography.
- Application of a U.S. Food and Drug Administration (FDA)-cleared AI algorithm to mammograms.
- Assessment of AI performance using receiver operating characteristic (ROC) analysis and area under the curve (AUC), stratified by breast density.
Main Results:
- The AI algorithm achieved a high overall AUC of 0.96.
- High diagnostic accuracy was maintained in both non-dense (AUC=0.96) and dense breasts (AUC=0.99).
- Decision curve analysis indicated consistent positive net benefit across various threshold probabilities.
Conclusions:
- A screening-trained AI algorithm shows promising diagnostic accuracy for symptomatic mammograms.
- Further validation in larger, multicenter studies is necessary before widespread clinical implementation.
- AI may enhance diagnostic capabilities in evaluating symptomatic breast lesions.
