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

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 interpretatio...

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Main Authors: Bounias, Dimitrios (Author) , Simons, Lina (Author) , Baumgartner, Michael (Author) , Ehring, Chris (Author) , Neher, Peter (Author) , Kapsner, Lorenz A (Author) , Kovács, Bálint (Author) , Floca, Ralf (Author) , Jaeger, Paul F. (Author) , Eberle, Jessica (Author) , Hadler, Dominique (Author) , Laun, Frederik B (Author) , Ohlmeyer, Sabine (Author) , Maier-Hein, Lena (Author) , Uder, Michael (Author) , Wenkel, Evelyn (Author) , Maier-Hein, Klaus H. (Author) , Bickelhaupt, Sebastian (Author)
Format: Article (Journal)
Language:English
Published: 23 September 2025
In: Journal of the American Medical Informatics Association
Year: 2025, Volume: 32, Issue: 12, Pages: 1908-1915
ISSN:1527-974X
DOI:10.1093/jamia/ocaf156
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1093/jamia/ocaf156
Verlag, lizenzpflichtig, Volltext: https://academic.oup.com/jamia/article/32/12/1908/8262149?login=true
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Author Notes:Dimitrios Bounias, MSc, Lina Simons, Michael Baumgartner, PhD, Chris Ehring, PhD, Peter Neher, PhD, Lorenz A Kapsner, MD, Balint Kovacs, MSc, Ralf Floca, PhD, Paul F Jaeger, PhD, Jessica Eberle, MD, Dominique Hadler, MD, Frederik B Laun, PhD, Sabine Ohlmeyer, MD, Lena Maier-Hein, PhD, Michael Uder, MD, Evelyn Wenkel, MD, Klaus H Maier-Hein, PhD, Sebastian Bickelhaupt, MD
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Summary: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.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).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.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.
Item Description:Gesehen am 03.02.2026
Physical Description:Online Resource
ISSN:1527-974X
DOI:10.1093/jamia/ocaf156

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