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

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Artificial intelligence (AI) support in breast diffusion-weighted imaging (DWI) can reduce ambiguous BI-RADS-like 3 calls and improve reader agreement. This AI-powered system shows promise for enhancing diagnostic efficiency and accuracy in breast cancer screening.

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Breast diffusion-weighted imaging (DWI) is a valuable tool for breast cancer detection and supplemental screening in women with dense breasts.
  • Current interpretation of breast DWI can be subjective, leading to variability in diagnostic accuracy and potential for unnecessary follow-ups.

Purpose of the Study:

  • To evaluate an artificial intelligence (AI)-powered computer-aided diagnosis (CAD) system for its impact on clinical interpretation and workload reduction in breast DWI.
  • To assess the AI system's performance in classifying breast lesions based on DWI and its effect on inter-reader agreement.

Main Methods:

  • A retrospective study involving 824 examinations for model development and 235 for evaluation.
  • Readings were performed by three readers with and without AI-CAD assistance, using BI-RADS-like classification based on DWI.
  • The AI model, based on nnDetection, was trained using 5-fold cross-validation and ensembling; performance was assessed using AUC and inter-rater agreement (Cohen's kappa).

Main Results:

  • The AI-augmented approach significantly reduced BI-RADS-like 3 calls by 29% (P=.019) and improved inter-rater agreement (0.57 vs 0.49).
  • Two readers detected more malignant lesions with AI-CAD assistance.
  • The AI model achieved an AUC of 0.78, increasing to 0.82 for women at screening age, indicating potential for 20.9% workload reduction at 96% sensitivity.

Conclusions:

  • AI support shows potential to enhance breast DWI interpretation by reducing ambiguous classifications and improving reader consistency.
  • The AI-CAD system demonstrated improved diagnostic performance and efficiency, suggesting its utility in clinical practice.
  • Further research with larger study cohorts is recommended to validate these findings.