Including AI in diffusion-weighted breast MRI has potential to increase reader confidence and reduce workload

Bounias D, Simons L, Baumgartner M, Ehring C, Neher P, Kapsner L, Kovacs B, Floca R, Jaeger PF, Eberle J, Hadler D, Laun FB, Ohlmeyer S, Maier-Hein L, Uder M, Wenkel E, Maier-Hein KH, Bickelhaupt S (2025)

Publication Type: Journal article

Publication year: 2025

Journal

Journal of the American Medical Informatics Association BMJ Publishing Group / Elsevier

Book Volume: 32

Pages Range: 1908-1915

Journal Issue: 12

DOI: 10.1093/jamia/ocaf156

Abstract

Objectives: Breast diffusion-weighted imaging (DWI) has shown potential as a standalone imaging technique for certain indications, eg, supplemental screening of women with dense breasts. This study evaluates an artificial intelligence (AI)-powered computer-aided diagnosis (CAD) system for clinical interpretation and workload reduction in breast DWI. Materials and Methods: This retrospective IRB-approved study included: n = 824 examinations for model development (2017-2020) and n = 235 for evaluation (01/2021-06/2021). Readings were performed by three readers using either the AI-CAD or manual readings. BI-RADS-like (Breast Imaging Reporting and Data System) classification was based on DWI. Histopathology served as ground truth. The model was nnDetection-based, trained using 5-fold cross-validation and ensembling. Statistical significance was determined using McNemar’s test. Inter-rater agreement was calculated using Cohen’s kappa. Model performance was calculated using the area under the receiver operating curve (AUC). Results: The AI-augmented approach significantly reduced BI-RADS-like 3 calls in breast DWI by 29% (P =.019) and increased interrater agreement (0.57 ± 0.10 vs 0.49 ± 0.11), while preserving diagnostic accuracy. Two of the three readers detected more malignant lesions (63/69 vs 59/69 and 64/69 vs 62/69) with the AI-CAD. The AI model achieved an AUC of 0.78 (95% CI: [0.72, 0.85]; P <.001), which increased for women at screening age to 0.82 (95% CI: [0.73, 0.90]; P <.001), indicating a potential for workload reduction of 20.9% at 96% sensitivity. Discussion and Conclusion: Breast DWI might benefit from AI support. In our study, AI showed potential for reduction of BI-RADS-like 3 calls and increase of inter-rater agreement. However, given the limited study size, further research is needed.

Authors with CRIS profile

Lorenz Kapsner Institute of Radiology Jessica Eberle Institute of Radiology Dominique Hadler Institute of Radiology Frederik Bernd Laun Professur für quantitative MR-Bildgebung Sabine Ohlmeyer Institute of Radiology Michael Uder Lehrstuhl für Diagnostische Radiologie Sebastian Bickelhaupt Institute of Radiology

Involved external institutions

Deutsches Krebsforschungszentrum (DKFZ) DE Germany (DE) Ruprecht-Karls-Universität Heidelberg DE Germany (DE) Radiologie München GbR DE Germany (DE)

How to cite

APA:

Bounias, D., Simons, L., Baumgartner, M., Ehring, C., Neher, P., Kapsner, L.,... Bickelhaupt, S. (2025). Including AI in diffusion-weighted breast MRI has potential to increase reader confidence and reduce workload. Journal of the American Medical Informatics Association, 32(12), 1908-1915. https://doi.org/10.1093/jamia/ocaf156

MLA:

Bounias, Dimitrios, et al. "Including AI in diffusion-weighted breast MRI has potential to increase reader confidence and reduce workload." Journal of the American Medical Informatics Association 32.12 (2025): 1908-1915.

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