guest ::
| Home > Publications database > Including AI in diffusion-weighted breast MRI has potential to increase reader confidence and reduce workload. |
| Home > Publications database > Including AI in diffusion-weighted breast MRI has potential to increase reader confidence and reduce workload. |
| Journal Article | DKFZ-2025-01947 |
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;
2025
Oxford Univ. Press
Oxford
This record in other databases: 
Please use a persistent id in citations: doi:10.1093/jamia/ocaf156
Abstract: 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.
Keyword(s): artificial intelligence ; breast cancer ; computer-aided diagnosis ; diffusion-weighted imaging ; machine learning ; magnetic resonance imaging
; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; Current Contents - Social and Behavioral Sciences ; Essential Science Indicators ; IF >= 5 ; JCR ; Nationallizenz
; SCOPUS ; Science Citation Index Expanded ; Social Sciences Citation Index ; Web of Science Core Collection
|
The record appears in these collections: |