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Possible strategies for use of artificial intelligence in screen-reading of mammograms, based on retrospective data from 122,969 screening examinations

Conclusion: Several theoretical scenarios with Transpara and radiologists have the potential to reduce the volume in screen-reading without affecting cancer

An AI-based Mammography Screening Protocol: Outcome and Radiologists Workload

Conclusion: Transpara could detect normal, moderate-risk, and suspicious mammograms in a breast cancer screening program, which may reduce the radiologist

Artificial Intelligence Evaluation of 122 969 Mammography Examinations from a Population-based Screening Program

Conclusion: The proportion of screen-detected cancers not selected by Transpara at three evaluated thresholds was less than 20%. According to

Interval Cancer Detection Using a Neural Network and Breast Density in Women with Negative Screening Mammograms

Conclusion: The combined assessment of the Transpara score and breast density measurements enabled identification of a larger proportion of women

Stand-Alone Use of Artificial Intelligence for Digital Mammography and Digital Breast Tomosynthesis Screening: A Retrospective Evaluation

Conclusion: Transpara could replace radiologists’ readings in breast screening, achieving a noninferior sensitivity, with a lower recall rate for digital

Using deep learning to assist readers during the arbitration process: a lesion-based retrospective evaluation of breast cancer screening performance