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Abstract #1551

Potential time and recall benefits for adaptive AI breast MRI screening

Luuk Balkenende1,2, Joren Brunekreef3,4, Jonas Teuwen2,3,4, and Ritse M. Mann1,2
1Radiology, Netherlands Cancer Institute (NKI), Amsterdam, Netherlands, 2Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands, 3Radiation Oncology, Netherlands Cancer Institute (NKI), Amsterdam, Netherlands, 4Informatics, University of Amsterdam, Amsterdam, Netherlands

Synopsis

Keywords: Diagnosis/Prediction, Screening

Motivation: Full MRI breast cancer screening protocols are time-intensive and abbreviated protocols have relatively high recall. An AI-driven hybrid protocol could combine the strengths of both protocols.

Goal(s): To determine the theoretical bounds of time and recall for an adaptive protocol.

Approach: We calculated the maximum and minimum overlap between cases identified by radiologists and AI for the abbreviated protocol.

Results: An AI driven protocol could improve MRI screening by reducing acquisition time and recall rates, compared to the full and abbreviated protocol respectively.

Impact: The AI-based hybrid protocol could improve MRI screening by reducing acquisition time and recall rates, enhancing patient-centric care, improving MRI availability and achieving better breast cancer screening.

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